Analytics

Does your business have a working strategy

Does Your Business Have a Working Strategy?

Veravizion 4 comments

The previous Veracle discussed whether strategy is really indispensable for businesses. It ends with a few reflective questions for business people.

One of the questions relates to the types of strategies adopted by businesses. No, it does not refer to the cost-based, differentiation-based stuff. It refers to types of strategies at a more fundamental, and practical, level.

The question asks whether your business has a working strategy.

What is a working strategy?

We know that a strategy is a plan of action designed to achieve long-term goals.

A working strategy is a plan of action that incorporates the components essential to achieve the goals.

Before we discuss components of a working strategy, let us first understand the strategies businesses typically employ.

A close observation of businesses reveals interesting insights about the strategies they use to operate and grow.

Such strategies classify into three types.

  1. Nope Strategy
  2. Hope Strategy
  3. Deliberate Strategy

The names given to these strategies might sound ludicrous, but the underlying phenomena are visible all around us.

The first two approaches in the list above are examples of what not to do. Yet, this is what many businesses still do.

The third approach focuses on developing a working strategy. This is the strategy successful businesses implement.

The three types of strategies are different based on the attitudes of executives running the businesses. The difference comes from two factors. First, “the need for predictability of positive outcome”, and second, “the risk propensity to commit resources to grow”.

The need for predictability of positive outcomes

The need for predictability of positive outcomes means whether the executives are keen to consciously make the growth happen, rather than leaving it to uncertainty in the face of a constantly changing business environment.

In simple words, executives’ need for predictability of positive outcomes is high when they are growth-oriented and cannot tolerate uncertainty for long. And executives’ need for predictability of positive outcomes is low when they are cost-saving oriented and are afraid to lose what they currently have.

For instance, Kodak is an example where the top management was cost-saving oriented. They were afraid to lose their film business and so, were reluctant to look beyond film for future growth areas.

The risk propensity to commit resources

The risk propensity to commit resources for growth means the willingness of business executives to expend resources – energy, money, and efforts – to consciously make the growth happen.

To illustrate, Xerox and Sony help us explain this phenomenon.

Xerox was actually the first company to invent the PC. Surprised? But, it is true.

Yet, they did not commit resources to its advancement thereby losing the market share to Apple. Smith and Alexander even wrote a book about Xerox called: “Fumbling the Future: How Xerox Invented, then Ignored, the First Personal Computer.”

On similar lines, Sony actually had the technology to launch a product even better than the iPod. But the executives were too afraid to commit resources to test out something new, eventually losing to – guess who? Apple again.

So, how do these two factors influence Nope, Hope, and Deliberate Strategies?

Nope strategy” is one where business executives have an operational business but have no real working strategy to grow the business. The business executives are oriented towards protecting what they already have, rather than creating new areas of strategic growth.

Nokia and Kodak are two prominent examples of companies failing to Nope Strategy.

Nokia is discussed at length in the next Veracle.

In the “Hope strategy” approach, business executives are keen on the positive growth outcome but are not inclined to commit the resources required for it. The executives operate the business by doing a lot of the same things. The business has some inexplicit approach that is rooted in the belief that if a business follows the industry best practices and adopts the prevalent marketing trends, it should grow.

On probing them, one hears an implicit hope that a working strategy will somehow emerge from the many best practices followed.

Hope strategy is a bit tricky because it does not sound wrong. Here, the business outcome is unpredictable because it varies based on many environmental factors.

What about Deliberate Strategy?

Deliberate strategy, on the other hand, is interesting. Here, an organization devises a plan of action that includes the components of a working strategy. This is to make it work in the context of its environment. It includes defining a specific business objective that is both measurable and achievable. Thereafter, the business develops a deliberate plan that serves as a working strategy to achieve that business objective.

Apple’s growth over the last decade is evidence of how deliberate strategy succeeds. Amazon is another example of a firm growing in this manner.

At Veravizion, we believe in employing a deliberate strategy to help our clients define and achieve their business objectives. Businesses have too many resources at stake to not employ a strategy that truly works.

Circling back to working strategy…

A working strategy, then, is one that assists an organization to achieve its business objectives in a predictable manner.

Predictability is the key.

That is why deliberate strategy is important!

In the next three Veracles, we will dig deeper to understand the attributes of each type of the strategies. We will discuss these with examples to find out the strategy that works.

Related Posts:

<– Is business strategy really indispensable?

Can a business still win with a Nope Strategy? –>

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Is business strategy really indispensable?

Is business strategy really indispensable?

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Why do I need a business strategy?

Recently, the chief executive of a retail business asked me this question. Let us first understand what strategy is!

In simple words, strategy is a plan of action designed to achieve long-term goals.

People apply strategy in many different contexts. For instance, military, sports, and business are some areas where strategy is necessary to win.

In the military, strategy is essential to win a war. It allows armed forces to plan military operations – offensive and defensive – in order to gain battlefield advantage over enemies, and achieve goals of national (and global) security.

In sports, strategy is essential to win a game. It allows sportspersons to devise a game-plan in order to gain an on-field advantage over rival teams, and win a match.

In business, strategy is essential to be profitable and grow over the long term. It allows organizations to develop business models and design operational processes in order to gain a competitive advantage, and achieve goals of financial security.

Coming back to the question then, is business strategy really indispensable?

For a moment, let us hypothesize about situations when strategy may not be important.

What if you are the general of an army having limitless troops and tanks? Or, the coach of a sports team having boundless talent and practice hours? Or, the CEO of a business having unlimited resources?

These situations might tempt us into thinking that one can easily trump the opponent without needing a strategy if one has unlimited resources.

In reality, organizations always have limited resources.

Even if organizations have resources in huge numbers, they are always finite in quantity.

Military organizations have a finite number of soldiers, shooting weapons, and shells.

Sports teams have a finite number of players, play paraphernalia, and practice periods.

Business firms have a finite number of competencies, capacities, and capital.

So, when you have something in a limited amount, what do you do? You find ways and mechanisms to use it judiciously such that you achieve your objective before expending the resources entirely.

Strategy is that mechanism!

In short, Strategy is important because resources are always finite!

To clarify, here is an interesting way to look at it.

The right strategy assists you in allocating your finite resources in such a way that you can build a competitive advantage against your rivals of any size, and can still win.

There is a gem of insight in that last sentence in case you missed it.

Strategy is the concept that helps you use your resources wisely and effectively. It allows you to prudently allocate your resources where they can deliver the maximum possible returns.

Some Examples

There are numerous examples in the military, sports, and business where a smaller team has implemented the right strategy to beat a disproportionately larger opponent.

History books are replete with instances of battles where a very small army has defeated a large one by employing strategic maneuvers. The battle of Longewala, the battle at Rezang La, Napolean’s 1812 invasion of Russia, and the 1775 battle of Lexington and Concord in Massachusetts are few such examples.

Sports archives are awash with games won by employing a tangible strategy; such games were called the biggest upsets of the time as a strategy was a late entrant in the world of sports as compared to some of the other fields. Here are three examples:

In the final of 1950 world cup football, Uruguay beat Brazil by keeping the game simple, focused, and warlike. Brazil was the hot favourites to win the game. Uruguay team was under no pressure and their captain asked the team to play a no-holds-barred natural attacking game, which they did.

In the final of the 1983 ICC world cup, the underdogs India beat consecutive three-time finalists (and two-time champions) West Indies by playing to the team’s strength of disciplined bowling.

One of the best examples of strategy winning a sports match is the “Miracle on Ice” game during the men’s ice hockey tournament at the 1980 Winter Olympics in Lake Placid, New York. In this medal-round game, the United States team consisting exclusively of amateur players (but following military-style discipline) beat the four-time defending gold medallists the Soviet Union that consisted primarily of professional players.

Examples from Business World

The business world is full of case studies of businesses devising deliberate strategies, developing sustainable competitive advantage, and capturing significant market share on their road to business growth. Here are two examples of businesses winning on strategy:

Blockbuster was founded in 1985 as a video (VHS) rental company. Within 15 years, it had 6,500 video rental stores around the US and revenues upwards of $5 billion. Netflix began operations in 1999 and led its strategy based on people’s video-watching preferences. Netflix devised a highly customer-centric strategy that included subscription-based charges and no late fees, among other things. As a result, customers could watch a video for as long as they wanted or return it and get a new one. By end of 2010, blockbuster was bankrupt while Netflix, on the back of its deliberate customer-centric strategy, is worth more than $150 billion today.

In the late 1980s, the sales of carbonated soft drinks were at a high. It would be foolish to introduce yet another drink in the fiercely competitive market. Yet, Austrian entrepreneur Dietrich Mateschitz partnered with a Thai businessman Chaleo Yoovidhya to introduce a new drink named Red Bull. Predictably, sales were (s)low during the initial years. That’s when the co-founder defined a strategy sharply focused on a chosen market segment. To that effect, Red Bull was positioned as an energy drink for students and adventure enthusiasts. The strategy would eventually help the business increase annual sales to 6.79 billion cans in 2018. As a result, Mateschitz became the 31st richest person in the world.

These and many such examples signify that strategy is extremely important because organizations are invariably resource-constrained.

So what?

On this note, some meaningful follow-up questions to ask would be: Is any strategy good enough? Does your business have a working strategy? Are you able to explain it clearly?

Related Posts:

<– Top Analytics Trends 2017 – An INFOGRAPHIC

Does Your Business Have a Working Strategy? –>

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Top analytics trends 2017

Top analytics trends 2017 – An INFOGRAPHIC

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Here are the top analytics trends 2017 for businesses based on what industry and our clients are saying.

These trends reveal a pattern similar to the one observed last year. Embedded BI facilitates the analytics of everything on demand. Moreover, application of IoT devices continues to increase rapidly. Gartner estimates that 20.8 billion connected things will be in use worldwide by 2020.

While analytics, IoT and their applications in business continue to permeate deeper, artificial intelligence (AI) and machine learning (ML) is gaining further attention.

Artificial Intelligence and Machine learning is also the number 1 among the 2017 strategic technology trends report published by Gartner.

Until a few years back, mid-size organizations hardly considered AI as a possible solution to any of their problems. However, the pressure on margins due to increasing competitiveness fueled by online players is making it imperative for all businesses, big and small, to be more efficient.

Besides analytics, IOT, and AI, there is one interesting trend that silently continues to grow and intensify because of how human beings are evolving – the urgent need for clear, relevant, and crisp visualization of data.

According to a research by scientists, human attention span is shrinking so much that even a goldfish can hold a thought for longer. The study by Microsoft says that average human attention span has fallen from 12 seconds in 2000, (or around the time the mobile revolution began), to 8.25 seconds in 2015.

While the comparison with the attention span of goldfish is debatable, the underlying insight – that humans are less attentive than ever before – hardly is. Powerful visualization of information remains the key.

Another trend catching the attention of businesses is the use of predictive analytics. In today’s uncertain business environment, companies want the ability to forecast future business performance based on the past. Predictive analytics tries to answer questions such as: What is likely to happen tomorrow? How can we make the business improve? Consequently, predictive and prescriptive analytics are among the most discussed analytics trends among the professionals.

In summary, smart businesses are recognizing the contribution of analytics (and the associated technologies) in their ongoing success. The top analytics trends 2017 continue to reflect this new reality. Unfortunately, Business analytics talent is scarce. Companies are struggling to hire (and afford) the right people that will help them realize the true benefits of analytics. This makes it ever-more critical to engage with partners that will bring on-board the right combination of computing know-how, analytical and visualization skills, and business acumen.

So, here are the top analytics trends 2017 at a glance. Do read-on, review and respond.

Download PDF of Analytics top trends 2017

Top analytics trends 2017

Top analytics trends 2017

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Is business strategy really indispensable? –>

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Analytics in Healthcare - A Case study. Image of a medical professional using analytics in complex medical procedures

Analytics in Healthcare: A Veravizion Case Study

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This article isn’t just about the application of analytics in healthcare. It is about how healthcare industry is harnessing analytics to evaluate the latest innovations in healthcare technology. Such as evaluation can help leaders in healthcare make policy decisions about embracing the new technology.

Techno-medical innovations

Over the last couple of decades, there have been quite a few noteworthy technological advancements in healthcare industry. For example, Electronic health records (EHRs); HAART for HIV combined drug therapy; minimally invasive surgery; needle-free injection technology; MRI, genomics; and non-invasive diagnostics.

These innovations are extraordinary because they are disrupting the way healthcare professionals can diagnose and treat patients in a better, faster, and safer way.

One such technological advancement was endoscopic surgery or minimally invasive surgery. This innovation revolutionized the way healthcare surgeons perform surgeries now. Knowledge@Wharton once ranked it tenth among the “Top 30 Innovations of the Last 30 Years” list.

The conventional surgical procedures were highly invasive, riskier, painful, and time-consuming. They required long post-operative hospital stays and longer recovery times. Thanks to technological innovations, today, patients have an option of choosing either robotic surgery or endoscopic (non-robotic) surgery. These new methods result in much shorter recovery times, less pain, and dramatically reduced scarring.  This augurs well for patients who are looking to return back to work quickly.

Minimally Invasive Cardiac Surgery

Many hospitals and cardiac care centers worldwide are evaluating the efficacy of the newer – minimal invasive approach – for specific cardiac surgical procedures.

The conventional cardiac valve repair/replacement surgery involved opening up a patient with an 18 to 20 cm vertical incision at the sternum. The newer minimally invasive procedure involves approaching the heart through a much smaller horiontal incision. This incision (or a key-hole) under the right breast is only up to 7cm.

The new cardiac procedures are more complex for surgeons to perform as the area of access (to heart) narrows down drastically compared to the wider access that the conventional surgery allows. Nevertheless, the new procedure is believed to more beneficial. The benefits involve less bleeding, lower risk of infection, faster recovery times, and lesser expenses for patients.

MinInv-mod

 

Analytics in Healthcare – A Case Study

Our client# had been studying the effectiveness of the new method of cardiac surgery compared to the conventional way of performing the same procedure. The study allowed them to enroll patients for one of the two types of procedures. This enrolling depended upon a number of physiological and health factors of each patient.

They performed the study over a period of three years and recorded the observations. The observed data included the type of procedure performed along with a number of associated output parameters such as hospital stay duration and pain levels experienced, among many others. We chose a power of 80%; we randomized the data for detailed analysis.

Our analysts collaborated closely with the client to understand the nature and significance of each output parameter. We identified the right statistical tools and techniques to be applied based on the nature and type of the data to analyse. The statistical significance level for the analyses 5%.

The results were examined in detail both for statistical and clinical significance. We cross-checked the statistical test results quantitatively as well as qualitatively with subject matter experts for completeness and correctness in order to arrive at unambiguous conclusions. Each conclusion shared with the client was solidly backed with data. The results would help them make a fact-based policy decision to embrace the newer procedure for their center.

Going beyond the statistical analysis, a predictive model was developed based on the results of the initial study. The predictive model would assist the client in determining the right approach to adopt for a future patient depending upon a number of factors. This is expected to improve the cardiac procedural outcome at the healthcare center.

Application of analytics in healthcare

Predictive modeling using machine learning is a powerful technique that helps in forecasting a probable outcome based on empirical data. Predictive modeling and analytics has tremendous potential in healthcare to improve the overall quality of patient care services. Analytics has shown promise to all the constituents involved in the healthcare sector viz. patients, physicians/surgeons, hospitals, pharmaceutical companies, insurance companies, and public health professionals.

  • Patients – more aware of self-health

Some of the uses of predictive analytics include increased accuracy of diagnosis, early detection of a disease condition in at-risk patients using genomics, and evidence based medicine. In general, with the proliferation of wearables, patients can be more aware and assured of their own physical conditions.

  • Physicians/Surgeons – increase diagnostic accuracy

When a patient is visiting a physician complaining chest pain, it is often difficult for the physician to know whether the person needs hospitalization. If the doctor is using a well-tested predictive diagnostic system, in which he can accurately input the patient’s physical and clinical condition, then the system can assist the physician make an informed judgement.

On the treatment side, a physician can follow a patient’s data (or EHRs) for many years and can prescribe a treatment regime tailored to the patient’s specific condition. As a result, this fact-based treatment reduces the probability of causing any major side effects.

  • Hospitals – improve patient care with low mortality rates

Like the case study narrated above, predictive analytics can help hospitals and research centers in evaluating the efficacy of various procedures and treatments. This can help in improving the mortality and morbidity rates during the post-op period.

  • Pharmaceutical Companies – bring new more effective drugs to market faster

Researching a new drug and conducting a clinical trial for the new drug are two very lengthy, costly and resource intensive processes for pharma companies. The R&D process for pharma companies can become more productive by leveraging the power of machine learning to systematically test the mixtures of existing proven molecular components. This may help in identifying new drugs with higher probability for success. Moreover, predictive modeling can be implemented to test the effectiveness of new drugs in a faster and less expensive manner. This will not only help them bring the drug to the market more quickly, but will also reduce the overall healthcare costs per patient significantly.

  • Insurance Companies – reduce cost of insurance

Healthcare insurance service providers can implement predictive analytics models to better forecast insurance cost for individuals. Presently, the insurance cost is more a function of a person’s age, current medical condition, and the ‘plan’ they are opting for.

Now, advancements in medical technology have made it possible to make genetic information and other healthcare related data easily available. Insurance providers can make use of this information to arrive at future medical expenses for a person. Also, they can make more informed decisions about the insurance costs associated with that individual. This will be a more realistic assessment of insurance needs for a person. Thus, it will be beneficial to both sides in terms of provisions to be made.

  • Public Health (Professionals)

The World Health Organization defines public health as all privately and publicly sponsored measures to prevent disease, promote health, and prolong life among the population as a whole. Its activities aim to provide conditions in which people can be healthy and focus on entire populations, not on individual patients or diseases. Here, analytics can be implemented in predicting early detection of pandemics and flu outbreaks. GoogleFlu was a project which estimated Flu and Dengue fever based on search patterns. While the project is not publishing anymore, empirical data is available for research purposes.

Conclusion

While application of analytics in healthcare is possible in all spheres of patient care, it is more about leveraging the power of analytics in rapidly evaluating the true value of techno-medical innovations for human benefits. Analytics makes it possible to make fact-based decisions about adopting it. Moreover, it also helps internalizing these latest technological advancements that promises to help us lead a quality life for a few years more.

References:

http://knowledge.wharton.upenn.edu: a-world-transformed-what-are-the-top-30-innovations-of-the-last-30-years/

http://pharmajet.com: significant medical innovations of the past 20 years

Cover image credit: ©Pharmajet

# Client names and other details are confidential

Related Posts:

<– Top Analytics Trends 2016 for SMBs

Top Analytics Trends 2017 – An INFOGRAPHIC –>

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Top Analytics Trends 2016 for SMBs

Top Analytics Trends 2016 for SMBs

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Like last year, we bring to you top analytics trends 2016 for SMBs. These trends are based on what industry stalwarts and our clients are saying.

A quick comparison with last year’s trends reveals that some trends continue to evolve. Topics like Deep learning, Self-service-BI, or Cognitive computing are some latest ones being discussed. Nonetheless, others are rapidly gravitating towards some common theme.

One such theme is ‘Big Data Analytics’. More and more small and medium-sized businesses (SMBs in short) are going digital. They are embracing analytics and leveraging their data to turn insights into higher revenues, reduced costs, and overall business growth. According to analysts, the big data analytics market is expected to reach nearly $50B by 2019.

Our Trends focus on the applicability of these technologies to small and medium business (SMB) organizations. As we know, technology plays a vital role in running a business successfully. Yet, some of these emerging technologies are not immediately relevant to SMBs. While it can be helpful to develop an awareness of these technologies, very few SMBs are actually going to use them. For example, the uses of 3D printing or AI in 2016.

This year, we have identified nine top analytics trends that are most relevant to SMBs.

1. More SMBs use analytics for business benefits.

Until a few years back, big data (analytics) was more hype than reality. Google was awash with searches involving keywords centered on ‘Big Data’. However, over the last couple of years, analytics has left the hype curve to provide real value. Today, analytics is everywhere.

Earlier, SMBs were not too savvy about maintaining the data of their customers, product orders, and suppliers. This was largely because of the costs involved in the data storage without the apparent benefit of maintaining the data. However, with the data explosion through various media over the last couple of years and with the availability of custom-analytics providers, they woke to the possibility of utilizing their data for getting answers to some key questions around their businesses. As the benefits started becoming visible – in terms of exponential business growth in a few cases – SMBs started focusing on analytics and become more data-driven to improve their business results.

However, making this data meaningful and easy to understand is still a challenge for many. We think that 2016 will be the year that small-scale analytics will really take off for SMBs, as it allows them to leverage their data from disparate data sources for their business benefits.

2. Internet of Things (IoT) enters our daily lives.

This is what Nikola Tesla said in a 1926 interview with Colliers magazine:

Top Analytics Trends 2016 - Internet of Things globe pic
IoT world

When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole… and the instruments through which we shall be able to do this will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket.

How it has become a reality less than a century later!

The Internet of Things (IoT) in its current form proliferated with the surge in low cost sensors embedded with Bluetooth wireless capability onto a small chip. And it is rapidly evolving from the realm of fascinating gizmos to real-world utility gadgets. Many leading companies such as Google, Amazon, Cisco, Dell, and TI have developed their versions of IoT products. There are already some cool IoT devices like Nest, Fitbit, and Belkin, to name just a few, that are vying for consumers’ attention in the market.

So what’s in it for SMBs?

In terms of Google trends shown in Exhibit-A, IoT today is where big data analytics was around 4-years back. Gartner forecasts that there will be 6.4 billion internet-connected things in 2016. Although the potential of IoT is huge, few SMBs consider it their ‘critical’ priority for investment at the moment. For them, it is still a nice buzzword. There is still time before every visible thing will have a sensor attached to it that will communicate with your servers in real-time. Meanwhile, SMBs are willing to watch and ride the hype-cycle.

Top Analytics Trends 2016 - IoT interest over time (Google graph)

3. Predictive analytics to address cybersecurity concerns.

As SMBs expand their technology footprint to run their business operations, the need to secure and protect data grows. Data security and privacy concerns continue to exist among small and large business organizations. However, many SMBs feel challenged and intimidated to deal with the rising complexity of cybersecurity breaches. Companies are normally content with the conventional approach of putting defensive mechanisms to ward off security risks. However, with technology advancements, the security breaches have also become more sophisticated and more risky wherever consumer data is involved.

While large organizations invest heavily into advanced (read: expensive) security mechanisms, SMBs do not have the luxury to do so. Nevertheless, they are now custom-developing predictive analytic models to proactively monitor log files and other user data sources to detect any threat perception or breach alerts. Clustering algorithms can help them identify anomalies in user login or other events which can be recorded on an ongoing basis. 2016 is likely to see an increase in the application of predictive analytics to deal with cybersecurity concerns.

4. Machine Learning algorithms foster man-machine collaboration.

We are entering the ‘smart’ era – smart people working alongside smart machines in smart cities. IDC¹ predicts that companies will spend more than $60 billion on cognitive solutions by 2025. Theoretically, machine learning algorithms based on neural network and AI have existed for a long time. However, their widespread application in everyday life is getting acceptance only now. This is made possible due to the tremendous increase in processing power that enables real-time split-second decision making.

Machine learning algorithms are currently being employed primarily in retail industry. With more people shopping across multiple channels looking for lowest prices, machine learning algorithms will become very popular in implementing dynamic pricing and devising on-the-spot offers in retail stores to retain the buyer.

For example, during this year’s holiday shopping season, leading retailers such as Amazon and Walmart were relying heavily on algorithmic pricing. Both retailers re-priced 15% of 18,000 product SKUs being tracked by a pricing intelligence solution on November 14th alone. These algorithms will be the backbone of any and every e-commerce business striving to win and retain customers.

[Example credit: Forbes]

5. Rising smartphone and tablet penetration continues to increase consumer mobility.

According to a comScore – Morgan Stanley research, mobile users globally have surpassed desktop users at the beginning of 2014. Rising mobile adoption, among people of all ages, impacts consumer purchasing patterns in a big way. With the increasing mobility, SMBs view mobile apps as a way to reach and engage end-users. SMB Group’s 2014 SMB Mobile Solutions Study indicates 59% of SMBs view mobile solutions and services as ‘critical’ to their business.

6. Hybrid cloud options still complex for SMBs.

2015 saw cloud making deep inroads into data-centers, data warehouses, centralized storages, and servers. SMB group’s market study shows that the cloud is poised to overtake on-premises deployment in the next year in areas such as collaboration, file sharing and marketing automation.

However, SMBs are largely using public cloud and staying away from private (or hybrid) cloud options because of the lack of clarity. Microsoft, Dell, and IBM have their own cloud platforms as hybrid cloud options however they do not yet seem to provide a compelling proposition for SMBs to embrace.

7. Omni-Channel integration or cross-device challenge?

Omni-channel is not a buzzword anymore given the availability of multiple screens every customer has. People have indicated that they love to shop across channels. So, more and more brands are going omni-channel way in a bid to woo consumers and to help them buy in their preferred channels. Brands are applying strategies like location based analytics to make relevant offers when consumers are in the vicinity of their stores. Businesses (like Macy’s or Virgin) that offer a unified omni-channel experience to their customers appear to have a competitive edge over others that cannot.

However, in a March 2015 study by Signal, 51% of marketers worldwide reported that they did not have a single view of customers, and only 6% of marketers worldwide reported they had an adequate single view of customers or prospects across all devices and touchpoints.

From our perspective, this year businesses will make this decision of whether they will play the omni-channel game and how.

8. Real analytics talent is (still) scarce.

According to the 2015 MIT Sloan Management Review survey² of business executives, managers and analytics professionals, 49% of respondents, who believe analytics creates competitive advantage for their organization, say that their company lacks appropriate analytics talent. While there is no dearth of analytics CVs in the market, very few of those appear to have real data science skills. In reality, companies need data scientists who possess the rounded knowledge of computer science, algorithms, math-statistics, business, and analytical skills.

Organizations that hire the less than appropriately skilled analysts end up wasting more than just money without any real benefits accrued. This is the reason more than 50% of analytically challenges organizations have stated that they outsource analytical services to external consultants or organizations, according to the survey.

Smart companies are realizing that analytical talent is critical to their success and in short supply, but more than 40 percent struggle with finding the talent they need. There seems to be a growing belief among SMBs that it is best to focus on customers to grow business leaving the necessary analysis tech work to specialists.

9. Visualization will be vital to SMBs application of analytics.

Data and the insights from that data are no more relegated only to the analysts. Business owners and SMB executives want to visualize their data to understand ‘what’s really going on’ in their business. And they want to do it in minimum possible time to be able to focus on the more important aspect of applying those insights for the improvement of their business. Business analytics in the SMB space is likely to stop being just a set of bar- and pie-based charts, and will be more multi-variate and intuitive. SMBs will demand more from the analysts in terms of visualization techniques that makes it easier and faster to visualize, understand, and explore data and uncover real insights from it.

Conclusion

This is an extremely exciting time for SMBs who can now apply customized analytics as per their specific requirements to take their business to a new level in an economical way. We believe this was not an option they previously had. It will be interesting to see how SMBs embrace business analytics to leverage the opportunity and explore unlimited possibilities.

References:

1 International Data Corporation

2 Ransbotham, D. Kiron and P.K. Prentice, “The Talent Dividend: Analytics talent is driving competitive advantage at data-oriented companies,”MIT Sloan Management Review, April 2015.

3 IoT image credit: wikipedia

<– Most Popular Perspectives from 2015

Analytics in Healthcare: A Veravizion Case Study –>

Top Analytics Trends 2017 – An INFOGRAPHIC –>

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Most Popular Perspectives from 2015

Most Popular Perspectives from 2015

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It’s New Year again – Happy New Year 2016!

Thanks for your overwhelming response to our insights shared with you over the last year.  We are excited to announce the most popular perspectives from 2015 published at Veravizion/Perspectives. These are our biggest stories of 2015 in case you missed them.

One of the wonderful aspects about sharing our insights is appreciating the incredible business acumen, diversity, and depth of thinking of our readers. Our articles, which we call our perspectives, are written after carrying out thorough research on every topic. Our belief is that these articles will push you into thinking about how the (business) world is transforming before our eyes, and how some long-standing business principles may not necessarily hold true today.

As the year is over, take a quick glance at how the world is getting used to being data-driven. Enjoy these stories and let us know about your top content in the comments. In the next one, we will see how the analytics world is likely to unfold in 2016.

Most Popular Perspectives from 2015

Story# 13 Lessons Every Executive Must Learn From Wimbledon Centre Court For Business Success

Most Popular perspectives from 2015 - Lessons from Wimbledon

Sports has always had many lessons to share for business success; and everyone and their grandpa knows this. Nevertheless, its relevance has never been as great as it is in today’s analytics age.

This article illustrates this phenomenon by drawing lessons for business success from 2015 Wimbledon final between Djokovich and Federer.

Story# 2Data Science: The Next Frontier For Business Competitiveness

Most Popular Perspectives from 2015

This article on Data Science by Veravizion was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).“

Story# 3The Digital Transformation Imperative: Why Businesses Must Have Online Presence – An INFOGRAPHIC

Most Popular perspectives from 2015

INFOGRAPHIC: click to enlarge

The business world is fast going online, so what’s the big deal? The big deal is in grasping the fact that it may replace your business if you do not become a part of the change, soon.

The infographic in this article gives a glimpse of how fast the consumer purchasing trends are changing from physical to digital, and what you can do about it.

Story# 4How Do You Achieve Strategic Transformation For Enduring Growth Of Your Company? – Part-I

Most Popular perspectives from 2015

Historically, leaders of cities, communities, and organizations have been embracing strategic initiatives to ensure long term sustenance and growth of their respective ecosystems. Many a times, these initiatives were ‘intentionally’ directed at bringing about long term transformation of their systems. But do such initiatives specifically aimed at strategic transformation always result in the lasting growth of the entity? We discuss it in this article.

Story# 5What Does Digital Maturity Really Mean?

Most Popular perspectives from 2015

This is the last article in the Digital Business series in which we illustrate how small and medium businesses can transform themselves from mere-physical to also-digital, and be more competitive. We do this by taking a visual example of a fictitious light business of our lovable businessman Bobstick.

We hope you enjoy these stories!

<– What does Digital Maturity really mean

Top Analytics Trends 2016 for SMBs –>

Strategic transformation photo credit: businessinsider

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What Does Digital Maturity Really Mean

What Does Digital Maturity Really Mean?

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When I went to medical school, the term 'digital' applied only to rectal exams.” - Dr. Eric Topol

Well, things have certainly changed since! The previous article – an infographic – discussed about the urgent need for businesses to achieve digital maturity in order to survive and thrive. But exactly what does digital maturity really mean for a small and medium business organization that thus far has focused primarily on serving local customers?

You might be thinking whether this question – what does digital maturity really mean – is really relevant in today’s digital age. Our implicit hypothesis is that most businesses, especially those in the developed countries, are already digital by now, and so they must know what digital maturity really means.

Well, evidence shows that the above assumption is far from true.

Consider these stats from the UK Business Digital Index 2015 report which states that almost a quarter of UK businesses still lack basic digital skills:

  • Almost 35% of around 5.2 million organisations in the UK have a very low level of digital understanding and capability – they do not make use of the internet for their business and do not have any web or social media presence
  • Barely 53% of the businesses have their own website
  • Only 46% organizations have a medium level of digital maturity – i.e. they use basic e-commerce tools, perform some banking transactions online, and have basic social media presence.

Therefore our question is extremely relevant even today!

So what does digital maturity really mean?

To understand this, let us quickly recapitulate why businesses want (or need) to go online in the first place.

Businesses go online for a variety of reasons (read: benefits) such as expanding markets to grow business, deepening engagement with target customers, broadening product and service offerings, leveraging multi-channel capabilities, and in general staying competitive amidst the changing global business landscape.

All these reasons can be summed-up in one simple line: Business organizations, like yours, are going digital because your customers are increasingly seeing, hearing, feeling, searching, interacting, sharing, and buying stuff online.

The above sentence encapsulates the entire online activity happening today in the business world. [tweet this]

In short, your market has gone online and it would serve you better if you do, too.

So what takes you there?

Here are the 5-stages on your journey to achieving digital maturity for your business:

  1. Digital Apathy
  2. Digital Literacy
  3. Digital Transactions
  4. Digital Engagement
  5. Digital Maturity

Let us look at each one a bit more closely with an illustrative pictorial example for each:

  1. Digital Apathy:

This is the initial (or default) stage of any organization typically born before internet. This company mostly sells their products mainly to customers in its neighbourhood through its physical stores. There is a passive resistance (or indifference at best) in accepting digital strategy due to inertia mixed with scepticism towards going digital. There is absolutely no online or any beyond-the-shop interaction with the customers. The business owner is unmindful about going out of business in this increasingly digital world, and apparently suffers from ‘it won’t happen to me’ syndrome.

What Does Digital Maturity Really Mean?

  1. Digital Literacy:

There is (almost) a reluctant acceptance to the changing business scenario. The business has a (mostly passive) website that displays the products and services on offer but hardly anything beyond that. On the positive side, customers now have a gateway to your offerings and can find information about your products and services. There is a new one-way channel to update customers about new product and service offerings – a good beginning to say the least.

What Does Digital Maturity Really Mean?

  1. Digital Transactions:

The business finally wakes up to enormous possibilities the e-commerce world offers and introduces online transactions to sell its products online. There is a conscious effort to implement basic customer analytics to understand buying customer profile to grow revenues. The business also tends to apply e-commerce intelligence to provide leads reports to sales teams to grow further. There is an emphasis on generating and distributing user-oriented content in order to draw target customers to purchase online. Businesses may introduce their own inventory management and service fulfilment back offices to excel in their customer service to build customer loyalty. The business starts learning about rule based prioritization as they explore the benefits of implementing analytics for revenue and profitability growth.

What Does Digital Maturity Really Mean?

  1. Digital Engagement:

When a business establishes itself on the various social media platforms, there is a step change in the way it perceives customer interaction, customer engagement, and marketing. Old channels and methods of one-way communication are renounced in favour of digital channels which enable listening to customers’ feedback first-hand and responding in their preferred channel to facilitate effective customer engagement. One important aspect of increasing engagement is to create product touch-points across all channels vis. physical, desktop, mobile, kiosks, catalogue, direct mail, and social media. The order in these cross-channel chaos is set by the use of marketing analytics which helps to mine hidden consumer insights, understand customer purchasing journeys, optimize advertising spend, and engage with prospective customers at early st(age) to nurture them into loyal followers.

What Does Digital Maturity Really Mean?

  1. Digital Maturity

The focus at this stage is on innovating the existing business model and on integrating the overall strategy. Personalization is the key here! Customers have 24x7x365 access to the products and services across different digital channels but still have an Omni-channel experience. For example, a customer becomes aware of your product in one channel, say Pinterest, actively searches for it online on his office desktop, physically touches and considers buying it in-store, ends-up purchasing the stuff on their mobile, and shares his new purchase with Facebook friends. Matured businesses (like P&G and Amazon) have institutionalized integrated use of analytics services to study individual consumer behaviour through comprehensive understanding of customer interests, affinities, and actions. They are drawing intelligence trends to predict customers’ future wants and needs before customers themselves realize it. Considering the enormity of data getting generated every day, matured businesses are implementing advanced algorithms to auto-analyse data at its source for more real-time application.

What Does Digital Maturity Really Mean?

Achieving digital maturity is not the end; rather a beginning of the implementation of a truly personalized digital strategy for each consumer. Businesses embracing digital strategy will eventually lead the way.

We are in the throes of a transition where every publication has to think of their digital strategy” - Bill Gates

Cover photo credit: yourgenome.org

Related Posts:

<– Why your Business should go Digital

Most Popular Perspectives from 2015 –>

If you liked this article then you may also like to read The Digital Transformation Imperative: Why Businesses Must Have Online Presence – AN INFOGRAPHIC.

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The Digital Transformation Imperative

The Digital Transformation Imperative: Why Businesses Must Have Online Presence – An INFOGRAPHIC

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“81% of US consumers turn to search engines to find information on products, services, and businesses before making a purchase,” according to GE Capital Retail Bank’s second annual Major Purchase Shopper Study. The digital transformation imperative study performed by Veravizion indicates that this observation is not much different in other parts of the world.

Less than 20 years back, Google (or an online search engine in its current form) did not exist. Most commercial transactions took place at a brick-and-mortar store. People booked long distance train tickets standing in a long queue at the ticket counter, bought airline tickets from travel agents, rented DVDs in video stores, read about new fashion in a print magazine, and purchased music CDs in record stores.

Today, 70% of all the travel bookings and hotel reservations take place online.

Music CD shops selling LPs, Vinyl records, and CDs have ceased to exist.

DVD stores have virtually disappeared from the streets.

Paper magazines and print advertisements have given way to their online cousins.

‘In the present day, if your customer cannot find your business on Google, you probably don’t exist for them.’ In a relatively short span of time, this has come to be one of glaring truths that business leaders must accept. Today’s consumer seems to have too many things to do, and appears to have become impatient because of limited time at hand. She wants to get everything done at the snap of a finger.

The digital world allows them this convenience of having (almost) everything in just one-click or touch. In fact, customers are adapting to this technology-driven shopping so well that they are touching every screen – even ‘dumb terminals’ – looking for an interactive touch-screen experience. Recent research on e-commerce points to a growing trend of digitalization of businesses and even non-profit social organizations.

Many industries like flowers and footwear, where customers’ need to touch and feel the product was considered important, now have above average online penetration. The grocery and general merchandise retailer Tesco is a case in point. It was one of the chains that saw an increasing role of technology in day-to-day household shopping and launched their online operations; it is now world’s second-largest retailer by revenues. A few industries like online grocery and pet foods (remember Webvan.com and Pets.com?) had a false start because of issues with their online business models, but are now being resurrected by the likes of Amazon and FreshDirect. Slowly but surely, every industry is joining the digital bandwagon.

Consumers on their part are enjoying the omni-channel shopping experience. Omni-channel purchase means a customer buying across multiple channels – online through mobile or desktop, call centre, catalogue, direct mail, kiosks, physical stores, and social media – and having a seamless shopping experience. So a customer may discover a great product offer while browsing Facebook during breakfast, search more information about it online via desktop after reaching office, ring a few call-centres to compare prices during lunch, check the product out at a nearby physical shop on the way back home, and finally purchase it online from their home using a smartphone. Once the product arrives, they may update their friends on social media posting pictures of their new purchase. The entire shopping experience becomes conveniently embedded in their routine and is fun.

Thus, internet is playing a key role in how businesses are run today. Nevertheless, it still has some way to go. An e-commerce foundation report shows that a disproportionately high percentage of businesses, even in developed countries with high internet penetration, are yet to go digital. For example, almost one in four businesses in UK has none to low digital maturity, while the ratio is reversed in some of the developing countries in Asia-Pacific, where the rate of digital transformation is much higher.

The attached infographic presents a quick glimpse of how business landscape is rapidly changing. It implores, with substantial evidence, why business (and social) organizations must have an online presence to survive and thrive in this third millennium.

What has been your experience of going digital? We would love to hear.

The Digital Transformation Imperative

The digital transformation imperative

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References:

Related Posts:

<– Data Science: The next frontier for business competitiveness

What does Digital Maturity really mean –>

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Data Science

Data Science: The Next Frontier for Business Competitiveness

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This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India - Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July))."

Data Science means extraction of knowledge from data. The key word in data science is not data; it is science[1]. Science of something means study of that thing to extract knowledge about it. In most generic sense, the purpose of every data science project is to answer a question (or a set of questions) backed by hard-facts. Academicians and researchers apply scientific principles to get specific answers about a research subject. Similarly, businesses employ data science principles to improve customer engagement, devise growth strategies, optimize operations, and build competitive advantage. This article shares a perspective on what data science really is, how it impacts various industries, what benefits does it offer to organizations – both for-profit and not-for-profit, and what are the key data science trends prevalent today.

DATA SCIENCE: WHAT IT IS (AND ISN’T)

Apparently Peter Naur and John W. Tukey seem to be among the first ones to have treated data analysis within the precincts of science[2]. John W. Tukey, who coined the term ‘bit’, has mentioned it in his 1962 paper ‘The Future of Data Analysis’. In my view, while the term ‘data science’ is relatively young, its application is not. There is an early evidence[3] of 1854, of Dr. John Snow applying scientific principles of data analysis to detect the root cause of The Cholera Epidemic in London. So data science has been around for a while albeit in different forms.

While we tend to associate data science with several other terms such as artificial intelligence, machine learning, data mining, analytics, statistics, computer science, and operations research, each has its own specific meaning that is different from another. Artificial intelligence is intelligence exhibited by machines and it pertains to the creation of a software system that simulates human intelligence. Machine learning is a science that involves development of self-learning algorithms which can be used to make data-driven predictions in a similar but unfamiliar environment. Popular examples include self-driving cars and web searches. Statistics is a study of collection, organization, analysis, and interpretation of numerical information from data. Data mining is the practice of analyzing data using (machine-learning) algorithms and statistical techniques in order to solve a problem. Computer science covers computational complexity, distributed architectures such as Hadoop, data compression, optimization of data flows, and not to mention computer programming languages (such as R, Python, and Perl). Advanced analytics or Analytics is just a marketing driven terminology that applies many of the data science principles to solve complex problems faced by businesses and society. So while the differences are subtle, each one has its own application in industry and academia. Nevertheless, data science overlaps with computer science, statistics, operations research, and business intelligence in many ways and almost completely encompasses data mining and machine learning.

The subtle differences notwithstanding, data science is an independent discipline which amalgamates statistics, computing skills, and domain knowledge. At the core, data science helps in deriving valuable insights from data. The data science process involves data collection, data pre-processing and cleaning, data modelling and analysis, and insights generation which are applied within a given functional domain to make decisions. Although the process is similar to knowledge discovery and data mining (KDD), a data scientist requires computing skills and domain knowhow to arrive at context-specific decisions. The person working in data science needs to exhibit three distinct skills applied in the different phases of a data science project. As shown in EXHIBIT-A[4], an individual with data science expertise possesses (or needs to possess) a combination of mathematics and statistics knowledge, hacking skills, and substantial domain understanding. The hacking skills include familiarity (but not necessarily proficiency) with software programming but more importantly, a propensity at being able to manipulate any type of data. This is because real-world data hardly exists in a nice tabular format. It[5] is scattered in thousands of text files or on hundreds of web sites or in numerous unstructured excel sheets at best. True data scientists that possess all the three skills are not abundant; because the role entails making sense of amorphous data, deriving bespoke models, and developing algorithms to analyse a complex problem specific within a domain.

Data Science Venn Diagram

Unfortunately, simply churning out numbers or fiddling with inefficient models rarely solves a problem. This is the reason data scientist is one of the most coveted roles in industry today.

Data science is being applied in many industries. Some of the uses in various industries include weather forecasting, intuitive search in online search technology, customer engagement in retail and consumer products and services, fraud detection in banking and credit cards, prediction of sources of energy in Oil and Gas, evidence based medicines in healthcare, and sentiment analysis from social network feeds. Some fields that are routinely implementing analytics services are eCommerce, retail, consumer products and services, financial services, insurance, pharmaceuticals, manufacturing, telecommunications, and high-tech.

HUNTING PEARLY INSIGHTS IN THE OCEAN OF DATA WITH DATA SCIENCE

More and more businesses are embracing data science and analytics in multiple organizational functions. There are mainly three most common ways in which data science is deployed depending on the size of an organization. Large corporations usually deploy their own in-house analytics departments by recruiting data analysts. Business leaders in large corporations typically have humongous quantities of data to sift through in order to make decisions that are important for their business growth. While having an in-house analytics team may not always be an ideal way for institutionalizing data science, even for large corporations, they seem to be driven by large amount of resources at their disposal. Secondly, some companies prefer to buy a COTS (Commercial-Off-The-Shelf) product to cater to some standard requirement. Thirdly, many mid-to-large sized companies prefer to employ customized data science or analytics services to solve their specific data analysis and business operational requirement. This option seems ideal for businesses looking for the flexibility to hire precise services for their bespoke needs.

While the data science projects in most for-profit organizations are getting more and more complex, the fundamental purpose underlying these projects remain the same – to achieve sustainable growth and improve profitability for their businesses. To that effect, the companies put data science into action to gain meaningful insights into their customers, operational processes, supply chain and logistics, product and/or service usage, financial aspects, and future business performance. Conventionally, data science has mostly been applied for market research and market segmentation. However, businesses have a lot more at stake with every business decision as competition has become more and more intense. Gone are the days when business decisions used to be taken on gut-feeling. In today’s globalized world, every major business decision needs to be data-driven. Data science assists organizations and individuals in making fact-based decisions that they can take and defend confidently. That is why it has become essential for organizations, business or otherwise, to deploy data science projects in every division responsible for making any kind of decisions. Some of the types of data science and analytics projects include customer focused analytics through clustering, recommendation engines, root cause analysis, automated rule engines, conjoint analysis to quantify perceived value of features offered, process simulations for operational analysis, predictive modeling for business forecasting, and clustering analysis to identify anomalies, just to name a few.

BENEFITS FROM IMPLEMENTING DATA SCIENCE INITIATIVES

There are some fantastic examples of business organizations gaining huge benefits by systematically and strategically deploying analytics initiatives that involve data science and ethnographic research. Procter & Gamble has institutionalized the data and design thinking approach to such as extent that it is now ingrained into their DNA. The result is that P&G boasts of more than 20 billion-dollar brands in their product kitty. Amazon, a technology company and not just an eRetailer, is really surviving and thriving by understanding customer preferences through the implementation of numerous algorithms. It has helped them to grow quickly from selling just books online in 1996 to target-selling twenty million products in countless other categories. There are many examples of smaller companies that streamlined their processes and implemented analytics based strategies to grow and enter into the big league. Data science initiatives within companies have rendered meaningful insights to drive their firm’s customer experience. These companies have utilized the insights to define their business growth strategies and pursue a culture of data-driven decision making. The benefits include getting pointers to new growth areas, generating ideas to introduce innovative new products, decreasing cost bases and improving productivity to boost profitability, identifying risks of obsolete technologies in their processes, detecting bottlenecks in supply chain, and streamlining inefficient operations.

Even as data science is rapidly changing the business world, it is also spreading its influence on other sectors such as academic research, governments, and social organizations. While the data deluge has increased the complexity for these sectors to analyze the data in a timely manner, it has also opened a plethora of opportunities for them.

Academic institutions in regions such as US, UK, and some countries in Asia are facing sustainability issues due to severe cuts in funding and grants. They are able to apply data science within their own institutional spheres to identify their respective competitive advantage and attract the right students to strengthen their reputation further. Similarly, medical research institutions are now able to work on projects like genome research, DNA sequencing, and stem-cell research for treatment of fatal diseases such as cancer and AIDS. Economists are able to analyze the publicly available data to determine relationships between income levels, education, health, and quality of life.

Governments and public sector organizations are concerned about issues such as monitoring and prevention of terrorist activities, early-detection and control of pandemics, and uniform aid distribution among the poorer countries, which they are able to tackle by sponsoring appropriate data science initiatives.

TACKLING CHALLENGES ALONG THE WAY

Data privacy and security concern has been one of the main reasons keeping some businesses from adopting data science. Moreover companies are facing real challenges in terms of bad quality of data, data inconsistencies, unreliable third party data, and information security. Nonetheless, all roads to meaningful business insights lead through data, whether it is organizational or public. Businesses need to put in place appropriate mechanisms to share data in a controlled manner with analysts and service providers in order to generate hidden insights that can be utilized for business benefits. Data breaches and data thefts remain a valid concern too. Past incidents, albeit few and sporadic, of customer confidential information getting stolen have deterred some from initiating analytics projects. However, business organizations are coming around to the fact that they are fast losing their competitive advantage to rivals due to staying away from analytics. Increasing number of organizations is taking up analytics to secure and grow their businesses as they do not want to be left behind any more. Organizations will increasingly recognize that it is not possible to operate in a 100 percent secured environment. Once organizations acknowledge that, they can begin to apply more-sophisticated risk assessment and mitigation tools. They will look to embed security at multiple levels viz. application-level, execution-level, storage-level, and even contract level. Interestingly, analytics itself is proving to be a great mechanism for security breach prevention.

KEY TRENDS AND THE ROAD AHEAD

In some of the western countries, data science has been thoroughly internalized within large corporations. Even the smaller businesses there employ analytics services to achieve specific business objectives. In India, while the (few) big corporations seem to be deploying such initiatives, most other organizations are still in the nascent stage. One survey of SME business owners cited that most common reasons for the slow pace of embracing [data science] are lack of awareness about the value offered by analytics, dearth of skilled resources, apprehension about technological complexity, cost and ROI concerns, and data security risks.

Notwithstanding the current adoption level, businesses are realizing that they may be taking a big risk not considering data science and analytics as a potent competitive strategy. There is a tremendous rise of personal data originating from social-media, sensor-originated data from wearables, and the Internet of Things (IoT) with the recent surge in the use of smartphones. More and more human actions are generating Exabytes of data today. To get a sense of the amount of data being generated, let’s just say that we will need around 50 billion trees made into paper to print 1 Exabyte of data. That’s roughly 9 huge stacks of papers, each touching Mars from Earth. This enormous amount of data will be of no use if not analyzed and utilized appropriately.

These trends are pushing businesses to re-think their business and growth strategies. There is an increased focus on teaching data science based courses by colleges and universities worldwide. Companies are realizing that the business environment has become uncertain with the fast pace of technological and demographical changes. As a result, many organizations are allocating higher budgets for deploying customized analytics for their businesses to deepen customer understanding, engage customers through multiple channels, identify new sources of revenue, improve productivity and profitability, streamline business processes, and build competitive advantage. Going forward, use of customized analytics will become pervasive. More and more organizations will develop their unique value propositions around the valuable insights they gain about their existing and prospective customers.

Implementing data science initiatives to build competitive advantage is a matter of leading and not following the pack. In an industry competing for the finite market share, early-adopters of data science best practices will be the eventual winners.

References:

[1] http://simplystatistics.org/

[2] Forbes: ‘A Very Short History Of Data Science

[3] Edward Tufte: ‘Visual Explanations

[4] Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

[5] ‘It’ refers to ‘the data’. In the modern world, the term ‘data’ is used in both singular and plural sense as per the context. Technically speaking, singular of data is datum.


Website link: https://www.veravizion.com/data-science-the-next-frontier-for-business-competitiveness/

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).”


Related Posts:

<– 3 Lessons Every Executive must Learn from Wimbledon Centre Court

Why your Business should go Digital (INFOGRAPHIC) –>

Do follow Veravizion on LinkedIn to receive easy updates.

You can also subscribe to our blog – Our Perspectives – to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.


Wimbledon Centre Court

3 Lessons Every Executive Must Learn from Wimbledon Centre Court for Business Success

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Last Sunday, the men’s singles final at Wimbledon between Djokovic and Federer was an enthralling match. Federer had hoped to become the first man to win Wimbledon a record eighth time; and Djokovic seemed to have something to prove after his recent defeat at the French Open.

The mixed doubles final, also played later that afternoon, was relatively a one-sided affair. The Paes-Hingis pair staged a clinical performance to clinch the title by decimating their opponents within 40 minutes.

During the matches, the English commentator attributed Djokovic’s win to his skill, determination, power, and accuracy, and alluded to Federer as the ’33 year old tired opponent’. Interestingly, the same commentator (or it may have been another one) ascribed Paes–Hingis’ comprehensive win to their experience, homework, and coordination.

But why did Federer really lose, despite his stellar experience of ten Wimbledon appearances and near-perfect game? And how did the ageing pair of Paes (at 42 years) and Hingis (almost at 35 years) register such a convincing victory against a much younger team? So, what does it take to succeed at the highest level in sports? And what lessons, if any, can businesses take from Wimbledon?

The Centre Court at Wimbledon provides three crucial lessons for businesses to succeed:

1. Do your homework thoroughly. Federer’s serve is the key to his game; he tends to serve deep and aggressively goes for the kill on the return of the serve. During the semi-final clash with Andy Murray, Federer’s winning points came off 5 rallies or less (on an average); whereas Murray’s winning shots came from at least 8 rallies. Federer outplayed Murray winning more than 80% of points off his first serves.

While Djokovic’s own serves were lethal, he must have studied his opponent well and had perfected his returns too. He forced Federer to play more rallies by returning his serves into areas where Federer could not attack back. The longer the rallies continued, the farther Federer had to run, and the more he became prone to making unforced errors thereby strengthening Djokovic’s chances at converting them into winners. Djokovic’s most winning points came off rallies that lasted 8 or more shots.

EXHIBIT-A shows Federer committed unusually higher number of errors and had lower serving percentages against Djokovic as compared to those against Murray.

Wimbledon 2015 Semi-Final and Final Match Summaries

Similarly, companies operating in the marketplace must thoroughly understand their competitors. Companies must first comprehend how their serves (like introduction of new product, feature, or category) are going to be hit back by their competitors, and then plan their strategies based on those insights. If the retaliation is weak, the market leader wins the market share; whereas if the competitor does tit for tat, then the market leader is forced to choose between carrying on the duel (with further investments) and conceding the share to the competitor. While this is true in all industries, it is most evident in oligopolistic industries like FMCG, where there usually are two dominant players. For example, similar duel happened between P&G, which introduced Crest fluoride toothpaste in 1955, and Colgate-Palmolive, which had launched the world’s first commercial toothpaste.

2. Execute well. Djokovic executed his plan [to play long precision shots] perfectly. Many of his winning shots were executed so accurately that they scraped the outsides of the baseline and the sidelines.

In the mixed doubles final, Peya and Babos showed lack of coordination early in the game by crossing each other’s paths and getting mixed-up in returning shots. On the contrary, Paes and Hingis displayed an absurdly good performance by hitting powerful returns and playing deep cross-volleys at the nets. EXHIBIT-B displays their high serving percentages (in the 80s) and zero errors, which reveals a clean and flawless execution.

Wimbledon Mixed Doubles Final Match Summary

In business context, perfect execution of strategies is a pre-requisite to achieving long term success. There are innumerable examples of brilliant businesses going dud due to botched executions. Kodak, despite inventing the core technology in the digital cameras, failed to execute the strategy and went bankrupt. Few other examples of companies that fell due to failed executions are Atari, Research in Motion, and Woolworths.

3. Play to Win. The two finals played at the Centre Court made this third lesson very evident. In men’s singles, Djokovic lost the second set in tie-breaker because he seemed content to passively return Federer’s serve playing from outside the baseline. He just didn’t appear to be playing to win and that cost him the set.

However, the brief rain gave him an opportunity to clear his mind and bring back his focus on winning. In the third set, there was almost a different – calmer and more focused – Djokovic playing within the baseline only to win.

Likewise in the mixed doubles, Hingis and Paes were so focused on winning that they were actually enjoying the game right from the word go. Every rally and every return was confidently played by them to win the point (and eventually the title).

This is how great businesses compete too – to win! They take bold steps and confident actions in planning and executing their strategies. They strive very hard to grasp the real needs of their customers. They go all out in devising solutions that they know will address the real needs of their customers. They leave no stone unturned to market their offerings. For example, Steve Jobs was so badly persistent on winning that he stretched himself and his team members to no measures.

Djokovic summarized this point well when he said in the post-match conference,

“I am gonna [sic] have to win, he’s not gonna lose.”

References:
www.wimbledon.com
John A. Quelch, Jacquie Labatt-Randle: Colgate Max Fresh – Global Brand Roll-Out.

Related Posts:

<– Strategic Transformation Part-2

Data Science: The next frontier for business competitiveness (External: CSI) –>

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achieve strategic transformation for enduring growth

How do you achieve strategic transformation for enduring growth of your company? – Part-II

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Thanks for your overwhelming response to the ‘achieve strategic transformation for enduring growth – part-I‘ of this two-part series. In the part-I of this article, we agreed with Mark that strategic transformation is indeed the key to enduring growth. However, we differed on the way to achieve transformation.

So how do you achieve strategic transformation for enduring growth of your business?

In part-I, we asserted that the secret to achieving strategic transformation is to be fiercely customer-centric.

In this part, let us discuss how you can transform your business and grow it further keeping customer-centricity at the core of your business strategy.

When you become passionately customer-centric, you make your customers’ delight the overarching goal of your organization. ‘Customer delight’ becomes the Shinbashira – the central pillar – on which your organization stands. You listen to your customers and place your customers (and consumers) at the centre of everything you offer. You direct your resources toward satisfying (and exceeding) the needs and wants of your consumers. As their needs change, the solution you offer changes, and the business strategy behind delivering those solutions changes too. You become so agile in responding to your customer’s changing tastes that you barely notice the incremental changes taking place in your organization. This continuous change becomes second nature to your organization.

And when you look back over a period of time, you realize that you have moved so significantly from your original business situation that there is hardly anything common between the old face and the new face of your organization. It is like the organization undergoing morphing.

This is when you have achieved transformation, without consciously embarking on doing that.

You don’t TAKE disruptive actions for strategic transformation; you take well-conceived actions with specific goals that BRING ABOUT ‘disruptive transformation’. There is a difference!

But this is a very simplified view of an organization going through transformation.

What actually happens (or has to happen) on ground is far more complex and far too deliberate.

Let us see how.

Our analysis of the top-100 brands revealed that when organizations commit themselves to becoming customer-centric, they tend to take these five steps so as to focus on creating maximum value for their customers:

  • They meticulously track usage of their products and services to figure out their customer’s usage patterns. This may involve analysing usage data from customers and in some cases their customer’s customers, who are using products that incorporate your product.
  • They strive to understand their customers thoroughly to comprehend the real reasons behind their customers’ purchases and apply that knowhow to identify real profitable customers. They perform detailed analysis of the customers’ data to generate deep insights into their purchasing behaviour and to spot any changes in them.
  • They incorporate their customers’ feedback into R&D to continually improve their products and services in line with the changing consumer expectations. Once in a while, this may result in conceptualizing an innovative product not yet in the market.
  • They put in place mechanisms to refine their operational strategies and organizational processes to address the changing priorities.
  • Most importantly, they use the feedback to calibrate their future roadmap and formulate the long-term growth strategy for their business.

These actions, when deployed effectively, enable these companies to embrace one or more of the three growth strategies: Market Expansion, Product/Industry Expansion, and Operational Improvement.

Let us briefly look at each of them.

1. MARKET EXPANSION – Business growth by entering into new market(s):

When you are customer-centric, you strive to know the face of your most profitable customers and the benefits for which they are buying from you. Your growth objective is to identify more of such customers globally who may benefit from your offering. This insight assists you to explore new customer bases in newer markets. The way this objective is achieved varies from company to company. Some companies merge with or acquire other businesses having presence in the target market, and leverage that presence to market their products. Others may form joint-ventures on the agreement to share technology and markets. Yet others may plan to grow organically by entering a new market on their own.

Coca-Cola, MTV, Starbucks, Nestlé, Heineken, and scores of other companies have successfully located their target customer segments across the world. They achieved international expansion over an extended period of time transforming their businesses from being local entities to becoming global market-leaders.

2. PRODUCT OR INDUSTRY EXPANSION – Business growth by introducing better products and services:

As your customers’ preferences change, the products or services they employ to do their job (need to) change too. When you are constantly tracking this change, you get innovative ideas for improving your existing product lines and introducing new ones. A good grasp on customers’ needs in new areas can propel you to launch new product categories that didn’t exist previously.

Procter & Gamble has developed this expertise in consumer research through ethnographic methods; they claim to co-create new products or new line of existing products by closely observing how their consumers use the existing products.

Amazon is an excellent example of growth through product or category expansion. Pursuing the vision of becoming ‘earth’s most customer-centric company’, Amazon is single-mindedly striving to make customer’s (online) shopping experience easy, comfortable, and pleasant. In fact, they are in the process of disrupting the entire book publishing industry by planning to publish and sell every future book online. The established book-publishers lost touch with the changing consumer need of ‘reading books anytime-anywhere without having to spend time buying it physically’, and now their survival itself is threatened by the digital publishing industry; whereas the likes of Amazon, with relentless focus on the consumers, are thriving.

3. OPERATIONAL IMPROVEMENT – Growth by developing unique competitive advantage:

This strategy involves creating differentiation in your business model and/or operations. Companies are leveraging their organizational data by applying the latest analytics techniques to mine insights. These insights are utilized to devise new distribution channels, optimize logistics and supply chain management, improve operations by deploying superior technology, and streamline existing processes. Relentlessly pursuing these strategies eventually renders them distinctive advantage over their competition, and helps them grow in the marketplace.

FedEx addressed the customer need of next-day courier deliveries and launched ‘FedEx Express’.

eBay provided easy B2C and C2C sales services to consumers via its (online) eCommerce platform.

Michael Dell grew his company by sensing an opportunity among PC-savvy consumers who enjoyed the convenience of customizing their PC and buying it online to be delivered in days. Thus Dell has developed an innovative business model of ordering PC online which enabled them to lower their PC prices to impossibly low levels.

These strategies have the potential to propel your organization into a bigger growth curve.

And the key to doing this is to understand your customers as thoroughly as you can!

In this world of disruptive innovations, business owners and CEOs must focus all their efforts in deeply understanding their customers. When you become ardently customer-centric, you set yourself up for strategic transformation that is key to enduring growth of your business.

Related Posts:

<– Strategic Transformation Part-1

3 Lessons Every Executive must Learn from Wimbledon Centre Court –>

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Get your job done in focus

Get Your Job Done: Belief Reinforced

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I   recently wrote an article in which I spoke about the three options available to any organization embarking on an analytics initiative. The three options are:

  1. Buy a readymade product
  2. Build the solution in-house
  3. Simply get your job done by the external experts

While the article discusses these choices specifically in the context of a business intelligence initiative, an incident happened about a fortnight ago reinforced my belief about focusing on ‘getting the job done’ while approaching any problem or a new purchase.

Let me share that incident with you.

That Monday early morning, I woke up with the ringing of my cellphone. It was my good friend who had recently shifted with his family to a new flat in the same suburb where I live presently. The place was new to them so I was glad to help with little things during their shifting. Sounding a bit flustered, he asked for phone number of some ‘reliable plumber’ who can solve the water leakage problem in their shower. I was surprised to hear that, as I thought he had already got it resolved by installing a brand new faucet in his shower. On inquiring about it, he reluctantly mentioned that the previous plumber had advised to replace the faucet but apparently it hadn’t solved the leakage problem. Not wanting to agitate him further, I quickly shared the contact details of our regular plumber without getting into more details.

Later that day, my friend called back to explain how the dirt in the overhead water-tank drifted into the shower-pipe, which was out of use for some time, and clogged it. That caused the pressure to build-up near the joint where the faucet was connected to the pipe, and that’s where the leak was. Apparently, the root cause of the problem was something else and it had nothing to do with the faucet at all. My friend was frustrated and felt cheated by the first plumber who dazzled him with the stylish-looking faucet and ended up selling him the expensive spout which was obviously not required in the first place. He was now relieved that the second plumber had fixed the leak by cleaning the whole line including the dirt in that tank.

This is what usually happens with many of us in different contexts. Whether it is a plumbing or a business problem, we usually prefer a quick-fix solution to it. It is astonishing how time and again we forget a simple fact of life: that each problem is unique and the right solution involves addressing the root cause rather than applying the standard quick-fix.

When my friend was narrating the steps which solved the problem, I was thinking about the jobs-to-be-done principle.

Get Your Job Done – The Principle

My first encounter with this principle came from a lecture I attended while at Oxford. It was delivered by Prof. Clayton Christensen, the Harvard Business School Professor who is recognized as the number 1 management thinker in the world. He put forth that customers often buy a product or a service because they find themselves with a problem they would like to solve; so they “hire” a product or a service to do the job that will solve their problem. He gave examples of many successful businesses, like IKEA, P&G, and FedEx to name a few, to bring home the point that companies need to think about the customer jobs-to-be-done whenever they are selling a product or a service.

The above incident is an example of looking at this principle as a buyer. Whenever you are purchasing something, buy only that product or service that satisfies your true needs.

But how will you decide which one is best for you?

Think about the job to be done!

Are you buying a car? Think about whether it is for daily office-commute, or for long cross-country family outings, or for transporting your small-business inventory? You will need a different vehicle (not necessarily a car) for each of these jobs.

As a business executive, are you thinking of buying that off-the-shelf product that churns out impressive and colourful charts? Think about what is the job you want to get done with it. Are you looking to solve your (one-time) business problem? Do you want some tracking dashboard tool to see ongoing business trends? Or is it because your boss is breathing down your neck to get ‘the most popular product out there’ just because your competitor has it? Each business has unique problems and needs. You may buy a branded, matured, and sleek but a very expensive product that addresses 80% of your key requirements; or you may go for a simple, convenient, and a very useful service at a cost-effective (but not necessarily cheap) price. Both options would work if it helps you to do your job; and neither would be useful if it doesn’t.

That’s why every buying decision must be made with a keen eye on what job you want to get done. This is especially true for businesses because business decisions cost shareholder money and are not easily reversible. So next time you are out to procure something, think about the job-to-be-done.

Related Posts:

<– Most important thing in analytics

Strategic Transformation Part-1 –>

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The most important thing in analytics - DART hitting dartboard at the centre

The Most Important Thing in Analytics is… No, It’s Not Data!

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L  ast Friday, I was having a very productive give-and-take with a group of business people on the benefits of business analytics. During our conversation, I had an interesting observation about what those people considered the most important thing in analytics. They seemed to think that data is the most important element in any (business) analytics project. I am sharing here the gist of that conversation. Hope you too find it interesting.

I believe data is the second most important thing in analytics or data science.

The most important thing in analytics is ‘the question to be answered’.

Let me explain why.

Typically analytics initiatives are undertaken to achieve one of these four objectives:
1. To solve a problem
2. To accomplish a specific (business or non-business) goal
3. To prove or disprove a hypothesis
4. To answer a question

Unless you have a specific question that needs answered, any analytics initiative will at best be a random analytical study with no direction. The specific question or the problem statement gives it a definite goal. More importantly, the goal will determine what data to use to arrive at a fact-based conclusion.

I like to think of it as ‘a ship sailing in an ocean’ analogy. An analytics initiative making way through the huge organizational data is analogous to a ship sailing in an ocean. If the ship has a pre-determined destination, then it takes the intended route to reach that destination and achieves its objective. To a bystander, mere existence of a ship creates a perception that it is going somewhere. However, if the destination is not clearly defined, the ship might just have a fun time cruising the ocean and figuring out what the ocean has to show. Worse, it might just flounder in the huge ocean and may not reach any meaningful place.

A ship like that is like a holiday-cruise-liner – expensive to sail but not tasked with any objective to reach a place.

Likewise, many organizations launch their analytics initiatives without any specific target. They just start with a tentative goal of let’s figure out what the data shows along the way. In the absence of any pointed objective to pursue, the analysts running the initiative either have great fun cruising with unbounded data or they just lose their way in it. Such an initiative renders no meaningful results.

An analytics initiative like that ends up becoming an expensive proposition like a luxury cruise-liner.

That’s why the most important thing in analytics is the question to be answered. The question determines what data to use to reach a definitive answer.
For example, to determine an organization’s target customer profile and their buying habits, the customer data will need to be analysed. Similarly, the sales history data will be required to ascertain a company’s sales trends.

The data serves as the route to finding the answer to the question.

The data to be analysed will change with the question to be answered. In our analogy, it is the route taken by the ship to its destination. Different destinations will warrant different routes to be pursued.

The selection of the right data becomes important after determining the objective(s) of the analytics initiative. The right prioritization will not only help you reach your destination faster, but it will also be very cost-effective as you save yourself from expending time, money, and manpower in analysing needless data.

In a nutshell, make sure that you do not end up getting lost in the ocean of data with wrong prioritization. Data-cruising is an expensive way to have fun at the expense of an organization’s usually scarce resources.

Related Posts:

<– 2015 analytics trends

GYJD: Belief Reinforced –>


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2015 analytics trends - a rising trend pic

2015 Analytics Trends for SME businesses

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2  015 arrives on a promising note for analytics – both as a technology and as an industry. 2014 proved favorable for analytics in getting wider acknowledgement as a serious strategy to adopt for long term competitive advantage. In that sense, 2014 was the year of acceptance and 2015 augurs to be the year of growth.

In this paper, we take note of the emerging trends in analytics with a pointed focus on their applicability to SME businesses. We began by collecting the trends data released by leading publishers. Our analysis of the aggregated information revealed some interesting bytes about the direction of these trends. Here we share with you our own perspectives on the top trends predicted by the external industry experts.

SMEs embrace analytics to deepen customer understanding.

More businesses are starting to anticipate their customers’ needs and act on it proactively. Personalization is the key. New technological advancement is making it possible to allow metrics to be tracked across even more areas for each customer, thus enabling SMEs to understand their profitable customers like never before. More screen time per consumer is creating even more data to be analysed and mined for deeper insights. Smart SME businesses looking for growth and customer loyalty are going all out to invest in cost-effective analytics solutions to get closer to their customers, drive cost efficiencies, and improve their product and service offerings. Larger organizations are implementing it to improve their customer service and call centre processes. B2C businesses like the consumer product, retail, and wholesale companies predict the fastest growth in the use of analytics to help improve customer insights and meet real-time customer demands. SMEs are implementing analytics to transform their marketing approach to connect with more prospects and customers, to provide them with the right information at the right time in their buying journey, and to create favourable perceptions about their offerings. After all Marketing is a battle of perceptions, not products.

Use of customized analytics becomes pervasive.

More and more human actions are generating Exabytes of data today. To get a sense of the amount of data, let’s just say that we will need around 50 billion trees made into paper to print 1 Exabyte of data. That’s roughly 9 huge stacks of papers, each touching Mars from Earth. So we now have developed massive capability of data generation; and the rate is only increasing with the advent of wearables, smart machines, and the ‘Internet of Things’. But this enormous amount of data will be of no use if not analysed and utilized appropriately. Anytime anywhere analytics will be the only way forward to satisfy the pressing need to analyse this vast pools of structured and unstructured data inside and outside the enterprise. Data analytics will soon become so ubiquitous that it will be deeply and invisibly embedded everywhere.
For SMEs looking to break away, micro-segmentation and micromarketing at the consumer level can provide a big competitive advantage. You can’t compete, if you don’t have competitive advantage. Information week survey reveals that there is fall in the trend to standardize analytics. For SMEs, this means they are not seeing competitive advantage in standardized analytics products. Instead, more companies are now hiring customized analytics to satisfy their unique needs. SMEs across all industries cited ‘ensuring information accuracy and reliability’ as a top concern. 2015 is likely to see a democratization of data throughout the organization, meaning that more departments will become adept at using the insights generated by experts. Day-to-day activities will be based on data and the insights created from it.

Rise of sensor-originated data from wearables and the ‘Internet of things’.

Wiki defines the Internet of Things (or IoT) as the interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure. For example, your smart watch continuously measuring your pulse while you are running, and prompting you to stop when it senses that your pulse is beating abnormally. The ‘internet of things’ has facilitated the innovation of wearables that can capture data using sensors and communicate it instantly over the internet. A few examples of wearables are fitness trackers, heart rate monitors, and smart watches. These wearables and IoTs will empower humans to take predictive (not just proactive) actions in a timely manner. The sensor based data collection will further boost application of analytics to analyse all these data. However, in the context of wearables and IoT, we are still in the stage of figuring out its possible real-life applications to individuals and businesses. In 2015, the IoT is expected to venture beyond wearables into your homes and surroundings.

Cloud becomes main-stream with greater cloud-based ecosystem.

Cloud has already crossed the stage that IoT is currently in. It has made deep inroads into datacentres, data warehouses, centralized storages, and servers. The cloud ecosystem is likely to penetrate further with the growth of centrally coordinated applications being hosted on cloud infrastructures around the world. 2015 will see horizontal spread of cloud-usage in device convergence and application portability across devices. Strictly focusing on SMEs, companies like Amazon and Google have rapidly matured their cloud-based offerings to SMEs with freemium and usage based models. However, monetization models of cloud based services remain complex. Despite that, cloud adoption is likely to increase among larger businesses as they strive to contain their infrastructure costs. Whereas the smaller businesses will (and probably should) expect the cloud-offerings to improve in terms of security, pricing-models, understanding of its benefits, and real ROI, before jumping the cloud bandwagon.

Better acceptance of open source technology like Hadoop and NoSQL databases.

If there is one trend which has high standard deviation with its long term sustainability, we believe it is this one. Hadoop is becoming popular because of “its ability to store and process semi-structured, unstructured, and variable data”. With the burgeoning unstructured data (from social media), it is no surprise that Hadoop is getting good acceptance among (larger) customers. But we hasten to add that Hadoop and NoSQL may not be in the priority list of most SMEs, simply because SMEs, unlike larger companies, have a tight purse to operate. They already have other higher priority strategies to undertake leaving not much scope for investment in the likes of Hadoop. It would be interesting to monitor and track how this trend pans out in the months to come.

SMEs will incorporate mobile-based solutions in their IT strategy.

A mobile phone is nothing short of a computer these days. Every passing month, millions of consumers are switching to a smart phone and relying on it for their daily chores. Referring a to-do list, searching for a Thai restaurant in the vicinity, buying books or fashion garments, downloading songs, and pretty much every other activity is increasingly shifting to a mobile device. Companies are also reinforcing their online presence with mobile-based solutions. The sale of phablets is likely to grow to 60%; thus SMEs will find it prudent to be in tune with this trend in order to be able to tap tech-savvy consumers.

Optimizing business gains from social media presence.

Businesses see social media as a mechanism to attract more customers, create buzz about their products and services, seek feedback to better customer service, and improve service offerings. Many SMEs seem to employ social media in a disorganized manner rather than design a systematic campaign to deploy a social media strategy for say, digital marketing. They hope that it will help them in some way. Hope is rarely a good strategy. In the coming year, SMEs are likely to rely on social media rather than just use it. However they seem to lack the means or resources to measure the gains from their social media strategy. This is where professional services firms can assist in devising a well-planned strategy to leverage real gains from social media.

Integration woes.

Frankly, we are as surprised (or unsurprised) to see integration woes as an ongoing trend as you possibly are. If there is one trend that has continued over decades, it is this one. Over the years, the ‘entities’ to be integrated have changed but the integration-related troubles continue. In the 1980s and 1990s, integration of legacy systems with open systems gave pain. In the 2000s, integration of different software architectures was cause for grief. Today, analytics tools and techniques are being developed around the Internet of Things, but the integration of these systems is lagging. More specifically for SMEs wanting to embrace analytics, integration of structured, semi-structured, and unstructured data is a source of much anguish. Worse, they typically lack the expertise and resources to manage the entire integration process despite employing a team of analysts. This is another reason that in-house analytics teams are increasingly not being preferred because of the need of specialist treatments of data.

Data security and privacy concerns.

Data privacy and security concern has been one of the main reasons keeping SMEs from adopting analytics. All roads to meaningful business insights lead through your data. You need to share data with analysts and service providers in order to get hidden insights that can be utilized for business benefits. However, SMEs have been wary in sharing their data, probably rightly so, in order to protect their customer identities, even at the cost of losing their competitive advantage to rivals. But now, with increasing number of SMEs taking up analytics to secure and grow their businesses, others don’t want to be left behind. Organizations will increasingly recognize that it is not possible to operate in a 100 percent secured environment. Once organizations acknowledge that, they can begin to apply more-sophisticated risk assessment and mitigation tools. They will look to embed security at multiple levels viz. application-level, execution-level, storage-level, and even contract-level. Interestingly, analytics itself is proving to be a great mechanism for security breach prevention.

Organizations moving away from in-house analytics.

Organizations hired staff to set-up their own in-house analytics teams primarily for two reasons: First, they intended to take up analytics as an ongoing business strategy and thought that hiring in-house analysts was the inexpensive way to achieving that objective. Second, they were guarded about sharing data with external service providers. The arrangement of in-house analytics department should have worked had it consisted of the right skilled-resources, such as data scientists and analytics experts, who came from analytics background. In many cases, these data analysts might have delivered some initial benefits despite coming from the business-side. However, companies seem to have realized that they are spending way too high to achieve that purpose. Not only do they not have the ‘right-skilled’ resources to take up the ongoing analytics challenges, but it also takes their focus away from their core business. Moreover, setting up an in-house analytics team is a costly and risky proposition requiring the necessary infrastructure to operate. Therefore, SMEs seem to be moving away from heavy in-house big data infrastructure to external analytics service providers. In some cases, they are even going for big data-as-a-service in the cloud. This allows them to have a lean in-house analytics team of just analytics officers or data officers and get their stuff done through the external knowledge workers.

In sum, most trends are pointing toward ‘ubiquity of analytics in business’. Which of these trends will realize in a big way in 2015? Our guess is as good as yours! Meanwhile, we would love to know your thoughts on how analytics has contributed to your business in 2014, and more importantly how do you perceive it to perform this year. Please do share!

Related Posts:

<– Buy, Build, or Get the job done?

Most important thing in analytics is not what you think –>


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Buy or build or just get the job done

Buy, Build, or Just Get The Job Done?

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A  s a CxO considering analytics solutions for your organization, whether to buy or build is invariably the first perplexing question you will face. There are many reports out there that evaluate these options and propose one of them.

For a business, it is of paramount importance to get a system designed and developed that meets the organization’s strategic goals. On the Buy↔Build spectrum, ‘buying a solution and twisting it to force a fit with your requirements’ is rarely an ideal way for most clients to meet their real requirements. On the other hand, it is often costly to ‘build your own thing’ in terms of time, money, and effort.

We would like to look at the buy-or-build decision from a broader perspective.

We believe that this decision depends upon two main questions:
1. Who is making the decision? The size and type of organization making the decision.
2. What is their purpose? The benefits for which the organization is making the decision.

The business needs of a multinational corporation are vastly different from those of a small and medium enterprise (SME) or those of a neighbourhood store. Moreover, the purpose for which such a solution is required by these businesses is also very different. While a large corporation may be willing to invest huge amounts of resources in such an initiative to seek strategic benefits, an SME might just want to implement it as a one-time project with tactical benefits in mind. In short, it depends on what job each business wants to get done. Therefore, we have identified a third option, which is becoming more popular, to acknowledge the real needs that most corporations have but tend to think only in terms of ‘Buy’ or ‘Build’. We call it the ‘Get-the-job-done’ option. So before we delve deeper into the details of buy, get-the-job-done, and build, let us start on the same page in terms of what each of these decisions mean:

A] The ‘BUY’ decision: Buying a product off-the-shelf or buying a license to use a product with little or no customization. In general, the product is licensed by the vendor or a subscription-fee is charged for its recurrent usage.

B] The ‘BUILD’ decision: Setting up an in-house team of technically skilled resources to develop a product yourself from scratch. This decision entails upfront investment in terms of recruiting the right-skilled resources, training them, deploying the necessary technical infrastructure, and having an overhead team of managers to supervise the development efforts to ensure that the product is developed as envisaged.

C] The ‘GET-THE-JOB-DONE’ decision: This decision involves hiring an analytics services provider to achieve your specific requirements, be it strategic or tactical. Such initiatives usually begin as a tactical project but go on to become strategic in nature once the initial results are visible.

In order to make this decision, you need to evaluate your requirements on the following five mutually exclusive but collectively exhaustive parameters:
a. Scale of operations
b. Benefits sought
c. Total cost of ownership (TCO)
d. Availability of resources – skilled people, time, infrastructure
e. Risk tolerance

So let us look at each option in detail.

A] When and why should you consider the BUY decision?

The BUY option should be pursued whenever the focus is on acquiring a product with standard specifications, quickly.

a. Scale of operation is very small. For a small business operating in a neighbourhood, the requirements are usually generic. If you are running a corner-shop, like a coffee shop or a sandwich-bar, some standard products are generally available. These ready-made solutions address most of your broad requirements. Level of customization sought is low. So you can run your business effectively with least modifications in a standard product as it matches most of your needs. Your customization requirements are not high enough to warrant investment for a customized product.
b. Benefits expected are generally (but not necessarily) tactical in nature. A small business, like a fashion accessories corner-shop, is generally looking for a quick turnaround of inventory or brisk sales during festival season. As time is of essence, you do not have the luxury to build a custom solution. In such cases, you should go for a ready-made product.
c. Lack of skilled technical resources may be the key factor in deciding against going for a custom solution.
d. Micro businesses typically have budget constraints. As a result, it is not economically feasible for you to build a proprietary solution. You may find it more logical to buy a cheaper commercial off the shelf (COTS) product instead.
e. Risk tolerance is not high. A couple of wrong decisions might result in big setbacks to a micro-business hence it is always prudent to go for a well-proven solution that does not cost a lot of money.

B] When and why should you consider the BUILD decision?

The BUILD option should be pursued whenever the focus is on addressing the strategic objectives of the large organization and cost is not the immediate concern.

a. Scale of operations is enormous. A multinational corporation with global operations, like a financial institution or a telecom company, generally has numerous product lines targeting multicultural consumers worldwide. These businesses require a solution that has functionalities appropriate to satisfy the disparate needs of their diverse customer segments. So a high level of customization is required by large corporations. COTS products with standard specifications are rigid to modifications and cannot meet these specialized needs. Hence a proprietary solution may be better equipped to address your business scalability concerns.
b. Benefits expected are predictably strategic in nature. As a CEO of a large corporation, like a multinational retail chain or a consumer products company, you are constantly looking for distinct competitive advantage to outperform your rival(s). To gain that edge, you cannot rely on the same off-the-shelf product bought by your competitors; you need an analytical solution that is tailor-made to optimize your business specific processes and operations. Devising your own customized system is likely to give you the competitive advantage in the long run.
c. Availability of resources – people, infrastructure, and time – is pivotal to building a truly productive solution. You must recruit right-skilled people viz. data scientists, analysts, and analytics experts, and train them to form a cohesive team. This team will have to be deftly led by able managers in order to build great analytical systems and tools, in a timely manner. In today’s fast changing world, the development team will have to be agile in incorporating changes in the system to keep with the technological pace in order to outplay the competitors.
d. Total cost of ownership (TCO) in case of custom-built solution is typically high. A large organization going for the build option will need to invest heavily in order to extract the strategic benefits to the fullest. These big companies are able to do so thanks to their deep pockets. That’s why the in-house ‘build’ option is suitable primarily for the large corporations.
e. There is a discreet risk involved in heavy upfront investment. Large corporations deploy an in-house team of developers expecting large gains at a later date. However, many things can go wrong, say, the technology may itself become obsolete, or the actual gains may not be worth the time and efforts. Moreover, in-house analytics team may distract the company away from its focus on the core business. Nevertheless, large cash-rich corporations acknowledge these risks and have a high tolerance to bear them.

C] When should you decide to just GET THE JOB DONE?

This option is fast emerging as the preferred alternative among SMEs and even some large organizations as it takes away the complexity and enables you to compete on analytics in a cost effective way. You should decide to hire analytics service provider whenever the focus of your organization is on getting results – cost-effectively and with lower risk.

a. Size of operation varies between a micro-scale and a global business; small and medium sized enterprises (SME) fall in this category. For an SME operating at a regional or a national level, the business needs vary tremendously because the target customer segments differ a lot. An off-the-shelf product that is rigid to modifications is invariably unsuitable for your needs. A ready-made product only means you have to forcefully fit your requirements to the features provided by it. Hence buying an off-the-shelf product is not a sensible approach because you do not want to end up paying your hard earned money for features your business does not want. If you are running a small or medium sized enterprise, like a retail furniture store, a retail consumer goods shop, or a B2C services company, level of customization required by your business is quite high. You need a solution that is flexible to frequent changes. Therefore, an analytics partner is ideal for you to help meet your true needs.
b. Benefits are expected quickly and in a cost-effective manner as the focus is to win quick results. As a business leader running an SME, you want to keep your focus on your core business. You really need a partner that provides flexibility, gives option to customize your requirements, and works for your success while ensuring good customer service.
c. Total cost of ownership (TCO) is comparatively low if you are employing an analytics service provider. They will offer customer service in deploying the solution and training your staff, so you save on the maintenance and training costs otherwise incurred in case of buying or building a product. That’s how the get-the-job-done option reduces your total cost of ownership.
d. Resource requirement is minimal for you if you go for analytics services partner. If you do not have a team of skilled resources to develop your own analytics solution, then it would be a wise decision to hire an analytics partner who will have expertise to productively work towards achieving your business objectives.
e. You can choose to share the risk with your analytics partner by opting to outsource your organization’s analytics activities. The get-the-job-done decision is attractive for its feature of sharing the risk between the business and the analytics partner.
Having said thus, it is not written in stone that an SME cannot build its own custom solution or a micro-business cannot opt for analytics services option. There are no stringent rules as such. The following table only illustrates a one-glance view of the merits and demerits of each of these options for most cases. In a nutshell, the decision of whether to buy or build or get the job done depends on your specific requirements and your preference for each of these options.

Pros-Cons

Related Posts:

<– Shifting focus of universities

2015 analytics trends –>

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Shifting focus of universities - analogy of flowers

Shifting Focus of Universities

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M  any educational institutions, that were founded long ago, would have started as a class of few students taught by a single master, covering maybe a couple of subjects. Harvard University started with nine students and a single master. Likewise, Samuel Johnson held the first classes in Columbia University with eight students in the class. Gradually, these institutions came to be revered next only to religious institutions as they grew in stature, size, and the number of disciplines offered. Over time, most of these institutions were offering all of the same courses; some of the courses might have lost their relevance but they still stayed.

Shifting focus of universities

Innovation in technologies and increasing globalization are the two major factors changing the face of education in the 21st century. The change is visible on both sides of the university admission counter. On one side, the student profiles enrolling in universities are changing rapidly – becoming more diverse demographically and geographically. On the other side, the universities are looking to keep-up with time, scrambling to stay relevant in this constantly-moving world. They are doing this by shifting their priorities on two key fronts:
The programs and courses being offered
The way these programs and courses are being delivered

The first point of concern for universities is the relevance of their programs and courses on offer.

Old technology or concepts either become obsolete or take a new shape and form every few decades. The first decade of twenty-first century has seen a sea-change in computing technology that has had a cascading effect on a lot of businesses. For example, social media and e-commerce – virtually non-existent over better part of the last century – are mainstays of today’s business world. With the advent of new technology, companies are under pressure to produce more for less to be able to compete effectively in the global marketplace. Companies continually question the relevance of old concepts, and try to invent new ones that can result in more quality and productivity. Industries share their feedback with universities on curricula either directly through industry-academia forums or indirectly through reduced job-offers to their students. Besides, they explicitly mention their requirements for the right skills in various job advertisements aimed at attracting prospective employees currently studying in universities.

For students seeking admission in prestigious universities, employability-post-education is a primary concern. Naturally they are looking to learn the skills expected of them by their future employers. So, the students prefer skill-based courses, which will make them ready for the job market, to basic knowledge-based courses, which are apparently lesser in demand by most industrial employers looking for ‘job-ready’ employees.

The academic institutions are expected to actively listen to feedbacks from students and employers, their prime stakeholders, and offer curricula in line with their requirements. A number of universities have swiftly responded to these changing preferences, and have undertaken comprehensive reviews of their programs and courses. For example, some universities are shifting their focus from courses based on art and culture to vocational courses. One such data point, observed by The Guardian, is the rate at which language degree courses are closing in the U.K. universities. Exhibit A shows the steady rate at which the courses pertaining to French, German, Italian, and Spanish languages have been closing down since 1998.
Shifting focus of universities - Language courses shifting trend

Universities cannot sustain the ongoing costs of modules that are less in demand. Unfortunately, easy targets for culling are those modules that are less biased in fetching jobs and are more expensive because you cannot replace say, a language teacher by a music teacher. Although, this shift in the focus is strongly debated by experts who say it is not a right thing to do, it seems to be the order of the day.

The second consideration for the educational institutions is the way their programs and courses are delivered.

The emergence and rapid percolation of internet worldwide has enabled the delivery of knowledge through a new channel – online – that was unheard of until the last decade. The name given to such a course delivered online over internet by education service providers is massive open online course (MOOC). One such organization that has taken MOOCs to a new level is Coursera (see www.coursera.org) that was launched barely a couple of years back. They have shown impressive growth within a short time-span and have already crossed enrolments of 10 million users. This splendid arrival can be attributed to the birth of a new demographic profile of students – an older professional desiring to pursue education but unable to do so in a traditional classroom setting. This new breed of students wants to be able to study anytime, anywhere at their own pace and convenience. Money is not their first concern as they are working professionals but moving away from job and family is, as they have family and kids.

Despite this overwhelming evidence, very few universities are offering online or even a blend of classroom and online teaching in their curricula. Universities simply can no longer close their eyes to the online presence. The new adult learner has money to dispense on tuition and fees, something that the universities are looking for with increasing privatization. What’s more, this new student is ready to learn at a fraction of the costs required of universities to spend on a traditional student learning in a college classroom. Suddenly, this seems like a very lucrative proposition for the universities. Also, they have the incredible performance of Coursera to vouch for. But hold on before you hurriedly join the MOOC bandwagon! Many universities have made a name for themselves because of a number of factors that are closely associated with physically attending the university. In some reputed campuses, it is more about the overall ‘student experience in these places’ than just the classroom learning. So, hastily shifting the scale in favour of MOOCs can spoil the party even before it can start.

This is where universities differ from one another and need to employ analytics to do a thorough study of their competitive advantage. The deep insights on why students prefer them can help them identify the right course offerings to be delivered online without cannibalizing their flagship courses. It will pave a way for their continued success in the future.


Related Posts:

<– Changing Student Demographics

Buy, Build, or Get the job done? –>

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Changing Student Demographics

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U  p until the nineteenth century, higher education used to be pursued primarily by those from elite background. So demographically speaking, young men predominantly from affluent well-connected families largely formed the pool of such students. Many factors have caused this demographic profile to shift from privileged few to general mass. It is anticipated that by 2020, almost 50% of students studying in the U.S. and U.K. universities will be from diverse cultures, backgrounds, age-groups, and nationalities.

Latest UN data indicates that there is a direct correlation between mean-years-in-school and income-per-person (GDP per capita). As shown in exhibit 1, people from developed countries having better avenues at taking up higher education are generally better-off financially. They appear on the upper-right end of the chart. On the other hand, people from poorer countries with lower education levels languish at the bottom-left of the chart. There seems to be a widespread perception that higher levels of knowledge and skills open doors to higher-paying jobs and thus result in better quality of life.
Changing Student Demographics - Mean_years_in_school chart

The outcome is that people from diverse cultures worldwide are pursuing higher education in the courses relevant across the world today. There is an increasing trend to study in prestigious universities in the developed countries, especially in the U.S. and the U.K, for higher education.

Let us briefly analyze how the higher education landscape is panning out in terms of nationality, age, and gender diversity.

Compared to a decade ago, 30% more international students were studying at U.S. colleges and universities, and 5% more international students were studying at U.K. universities in 2011-12 (see exhibit 2 for the trend in U.K. universities). The most noticeable increases in international students are from Asia, the Middle East, and other emerging economies. This trend of greater participation of ethnically diverse population points toward globalization in the education sector.
Changing Student Demographics - International-students_trend over time

There is one other interesting trend that has been noticed recently. Universities in the U.S. are witnessing an increase in the older (typically aged 25-34) students than in the past. In today’s information age, innovation is happening fast and things are becoming obsolete faster. People are feeling the need to learn new technology, new business models, and new skills, to stay tuned with the changing times. This perceived need to keep the knowledge current and to remain relevant in the job market is prompting students from various backgrounds to return to school. These are the older students, a.k.a. students of non-traditional age-group, that could not continue their education earlier due to various reasons such as low income, lack of suitable avenues, family obligations, and other socio-economic reasons. With the advent of internet and online education options especially in the U.S., there are an increasing number of older, even married, students with more varying demographics joining higher education, thereby skewing the overall demographic profile of students. By contrast, universities in the U.K. are experiencing an increase in the number of younger students and decrease in those aged over 30. This may be explained by the fact that online channel as an effective medium of learning is yet to get widespread acceptance in the U.K. Most of the well-known providers of online education today, for example, Coursera, Udacity, and edX, are based in the U.S. Online medium of education is typically preferred by older students having jobs and families.

Finally, gender diversity is playing a big role in the changing trends. Female students are fast occupying the seats in classrooms that once were occupied by their male counterparts. In the latest report published by HESA (UK), more than 50% of students studying in U.K. universities in 2011-12 were female. The trend is not much different in the U.S. universities either. There were interesting trends within region-wide distributions too, such as lower female students population from countries considered conservative. However, in general, there is an evidence of definite increase in participation of female students in higher education.

But why are student demographics important to universities?

Students are the main stakeholders in the success of an educational institution. Students’ curricular and extracurricular activities, interests, and opinions are driven by their beliefs, faiths, likes, and dislikes which in turn are based on their cultures and ethnicity. Thus, their demographic profile directly affects the way their experiences will shape-up in any educational institution. The universities can no longer assume that students from diverse communities will participate in courses and activities traditionally offered by them. This calls for detailed analysis and deep reflection on part of university leaders to design and offer an ideal educational experience for this shifting student demography.

How leaders are acting in response to the changing demographic trend?

The student population will become more ethnically diverse in the years to come. Educational institutions are now getting accustomed to the rapidly changing student demographics in the sector. Leaders from the top universities are making the most of this opportunity by putting in place such mechanisms that will ensure responsiveness to the needs of these new learners. The development of this yet untapped student demography means new avenues of revenues for the universities already grappling with funding issues. One of the first steps the leaders are taking is to institutionalize analytics at individual student and course level. This will help them in the following three ways: First, student-level-analytics includes a well-defined feedback seeking mechanism from students that let the leaders feel running pulse of the students’ preferences, likes-dislikes, and attitudes. Second, course-level-analytics gives inputs on performance of teaching faculties on all courses, research activities, and course-effectiveness index, and checks the courses’ continued relevance. The combination of these two analytics can be very effective in keeping a tab on ‘drop-out-risk’ of students. Last and most important, the leaders now have hard-data to take quick information-driven decisions with less gut-feeling and less uncertainty. Better still, they are able to be accountable with more confidence and can utilize the information to win over their sponsors and stakeholders. They are able to apply it in a day-to-day decision making so that the educational institutions become ready to embrace the future with welcoming arms.

Related Posts:

<– Universities: Funding and ROI challenges

Shifting focus of universities –>

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Funding and ROI Challenges: How Universities Can Respond

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T  raditionally, universities in the U.S. have earned their revenues through state (government) funding, tuition and fees, research and development grants, returns from endowments, and philanthropic donations.

Funding and ROI Challenges

The prolonged cash-crunch due to the long-winded recession of 2008 forced the policymakers to make some nearsighted (and some say misguided) policy changes like cut in public spending on higher education. The policy changes caused steady decline in state funding to universities and many universities found themselves grappling with financial sustenance. The slump in economy also caused research grants and philanthropic donations to shrink. In the face of volatile and unreliable nature of returns from endowments, universities were compelled to shift the burden of additional costs to students in the form of increased tuition and fees.

Universities in the U.K. too are facing similar funding challenges. According to the data published by Higher Education Statistics Agency (HESA) and the Universities UK (UUK), the public spending in U.K. on higher education reduced by 6.9% post the recession. Over the last few years there has been a reduction in the proportion of income from funding body grants to total income. The funding from Higher Education Funding Council for England (HEFCE) is expected to reduce further. The recession has also contributed to a large decrease in the ratio of research income from research grants and contracts. Nevertheless, the past three years have seen relative stability in terms of the total amount of money flowing to institutions. HESA data also points to 38.6 per cent decrease in endowment and investment income over 2009-10 and it decreased by 25 per cent across the U.K. over the period 2000-01 to 2009-10. Gradually, the universities in U.K. too increased tuition and other fees to cover for the costs. One major difference between universities in U.S. and U.K. is that, the private expenditure on higher education is much greater than public expenditure in the U.S. universities.

Overall, the balance between the funding grants, and tuition and fees is moving towards fees, a trend seen in the U.S. universities too. Gradually, the rise in tuition and other fees are becoming unsustainable, especially for postgraduate students, already under huge debt from undergraduate studies. Exhibit 1 shows the comparative trend between changes in college tuition and fees vis-à-vis the changes in the cost of all consumer items in the U.S.. Starting at the same level in 1978, the tuition and fees cost seems to have increased five-fold as compared to consumer prices over the last few decades.

Funding and ROI Challenges: Exhibit-1 college tuition and fees cost trends

Moreover, the rates at which people’s incomes have gone up have not been able to catch up with this high rate of increase in college fees. The rate of change of college tuition has overshadowed the inflation rate consistently since 1981 as shown in exhibit 2. Considering that students join higher education primarily for higher pay packages, this unsustainable rise in the cost of college fees in the face of high unemployment is impacting universities’ enrolments adversely.

Funding and ROI Challenges: Exhibit 2 - college tuition vs inflation graph

From the perspective of the universities, the costs are steadily increasing. The costs such as faculty salaries, college infrastructure budgets, administrative expenses, IT infrastructure costs, and marketing overheads form the fixed costs that are incurred irrespective of any student taking admission. If the number of students enrolled goes down, the average cost per student goes up. This situation makes it unsustainable to run the famed institutions delivering the same level of quality. This results in pressure to achieve surplus funds, after accounting for staff, administration, and operating expenses. While the universities in the U.K. have managed to achieve a surplus in the last couple of years, it is not before raising the income from other services rendered such as, residences and catering operations, grants from local authorities, income from health and hospital authorities, and income from intellectual property rights.

So how can universities respond?

Universities can take a series of steps that will help them stand-up to these multi-dimensional challenges to save costs and increase incomes.

Firstly, in order to save costs, university leaders need to improve productivity on teaching related activities. This can be done by rationalizing the programs and courses offered, integrating departments to normalize instructional costs, streamlining operational processes to leverage synergies, and outsourcing non-instructional activities to specialized vendors. Many universities accept that the programs and courses offered by them have experienced proliferation over the period of time. Thus, merging similar programs will not only make them more effective but also save costs for the colleges. On the same lines, integrating departments and streamlining operational processes that are similar in nature will help making the operations leaner, faster, and cost effective. Most importantly, institutions must focus on providing value in the area of their competitive advantages. This entails outsourcing non-core activities to reduce non-instructional expenditure. Analytics can come very handy in helping to understand the potential levers to realize savings from taking up these activities.

Secondly, ensuring enrolments of the ‘right’ students will pave ways for improving incomes. In addition to bringing tuition and fees, the right students can help make the universities look good through their research and development activities, which in turn will help the institutions to attract funding from existing and other green-field sources. One big aspect of improving income is to retain existing students from dropping out. High drop-out rates has become an area of serious concern for many universities. Reasonably so, every dropped-out student creates a hole in tuition fees that invariably remains unfilled. Moreover a high drop-out rate impacts twice, in lost revenues and sunk costs. Therefore, student retention should be looked-at through the same lens as businesses look at customer churn, and earnest measures should be implemented to control the high drop-out rates.

To summarize, universities need to take unconventional actions to effectively overcome the funding challenges and strive to be lean in order to embrace the opportunities of the future.

Related Posts:

<– MOOC and the education system continuum

Changing Student Demographics –>

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Veravizion analytics

Veravizion analytics – A Warm Welcome!

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W  elcome all to the first blogpost at Veravizion analytics!

We are very excited to be able to offer our services to you and to make a sustainable positive impact on your business and on the businesses and lives of those you serve. The opportunities are limitless. We are also thrilled to have the opportunity via this blogpost to share our perspectives and your thoughts on topics that are as much crucial for your business as they are dear to our heart.

All these preceding years, I have observed one thing consistently. Like Newton’s observation, this one too is as common as an apple falling down a tree, and one that most of you have noticed too. However unlike Newtonian one, this one concerns matters of business. It is that, the data and time at hand, for a business executive, are inversely proportional to one another; and the proportion is worsening every passing day. BBC cites IBM to say that, “2.5 exabytes – that’s 2.5 billion gigabytes (GB) – of data was generated every day in 2012.” We are already 700-odd days ahead of that.

The enormity of data carries grave implications for a business (and for our business executive). The top three concerns that instantly come to mind are:
1. Additional cost to analyze the data in a rushed manner
2. Risk of missing the main point
3. Impact on bottom-line and business strategy

From my own professional experiences, I have seen C-suite executives asking senior-managers for instant reports on a variety of business parameters. Naturally, the top-guys need it to keep the business profitable and chart future course of the business. The senior-managers – the business executive in our observation – rarely have an easy way out. So under severe pressure, they are forced to employ resources, at times unskilled, to quickly run queries and generate reports. Many a times, the hugeness of data and paucity of time make the analysts churn out some reports to satisfy the top-execs’ invariably urgent requirements. The entire process becomes quite frustrating and traumatic for the managers. Moreover, there is a high risk of such reports being incomplete and/or inaccurate. It will be nothing less than a gamble to base critical business decisions on such reports. Worse, the managers can be accused of misrepresenting the facts for no fault of theirs.

Fortunately, there is a large upside to this situation. The senior managers can put in place mechanisms, like management dashboards, frequently required charts, and a few useful reports pertaining to any and every information on their customers. These proactive actions on their part can go a long way in helping them and their organization institutionalize a new competitive strategy to keep a step ahead of their competition.

Veravizion was born to help executives with these requirements.

The objective is to have something simple, yet something incredibly useful. These tools can not only save the managers from stress but also help them create win-positions for themselves in their organizations. The business executives can actually differentiate themselves and their organization with this strategy.

I look forward to interacting with you in the coming weeks through future posts and your comments.

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