Tag Archives: Trends

Analytics is the process of discovering, interpreting, and communicating meaningful patterns in data. It helps us make decisions based on data and hard facts.

Most companies now use analytics to make data-driven decisions. They expect that good insights can really take their business to the next level.

Unfortunately, good insights rarely emerge.

Is analytics all hype then?

Here is a startling finding.

Research by PwC and Iron Mountain indicates that three in four businesses extract little or no advantage whatsoever from using analytics. According to the study, 43 percent of companies surveyed “obtain little tangible benefit from their information,” while 23 percent “derive no benefit whatsoever.”

Now, everyone and their uncle is claiming to use analytics in their business. If it was really useful, the world’s GDP have gone through the roof.

So, why are businesses not able to leverage analytics?

This HBR article and the PwC research discuss the root cause. Both studies point to lack of the right capabilities and competencies required to make good use of the information companies have.

This finding really warrants the question. What capabilities and competencies do we need to extract real value from data?

In fewer words, what makes analytics valuable and beneficial?

The answer is STATISTICS.

Statistics makes analytics insightful.

Analytics without statistics is bland, blunt, and bootless. It is like Ron’s broken wand in (the Harry Potter movie) The Chamber of Secrets.

Analytics without statistics, at best, gives us dull observations like ‘focus on the millennials.’ At worst, we get costly and impractical recommendations like ‘redesign the entire supply chain.’

This lack of benefits from analytics leaves businesspeople disappointed.

On the contrary,

Analytics + Statistics = INSIGHTFUL Recommendations

What is meant by insightful?

An analysis is ‘insightful’ when it goes beyond the superficial. It gives an accurate and deep (hitherto unexplored) understanding about the subject. Besides, an insightful analysis helps us break our long-held beliefs and preconceived notions that hold us back.

Here are two diverse examples of insightful findings.

Example 1: who revolves around whom?

The western world during Aristotle’s time (c. 384 B.C. to 322 B.C.) believed that the Sun revolved around the Earth. For 1,000 years, Aristotle’s view of a stationary Earth at the centre of a revolving universe dominated the studies of the universe. Surya Siddhanta and later Copernicus’ work showed that it was the other way round. That ours is a heliocentric solar system in which the Earth and the other planets revolve around the Sun.

That is insightful.

Example 2: how do you shave?

Gillette first entered the Indian market in 1984. But they failed to sell razors despite trying for many years. They even launched their newest triple-blade system in 2004. However, sales were flat for a long time. Why? Gillette did not understand the Indian consumers. They had tested the product with only a few Indian Students at MIT and hence had missed crucial insights about shaving habits in India. A large part of Indian men did not have access to running water and had longer and thicker hair (than Americans). Based on these enlightening insights, they launched Gillette Guard for the Indian market, tasted success for the first time, and never looked back.

Connecting the dots…

So, how does statistics make analytics insightful?

Statistics solves two problems: ‘isolated evidence’ and ‘random variation.’

It does so by employing systematic numerical methods to analyse enormous quantities of data representative of the entire population. Statistics helps us make inferences on the whole population from those in a representative sample. The representative sampling assures that inferences and conclusions can extend from the sample to the overall population.

Ideally, everyone using analytics must incorporate statistical techniques.

But there is one hitch.

In fact, there are three:

• Statistics is complex. If there is one subject which is universally hated, it is statistics. Advanced statistics can get overly complicated. If they must, people use only the descriptive statistics which is easier. They tend to stay away from inferential statistics which is responsible for drawing inferences and conclusions.

• Use of statistics needs expertise. One needs in-depth understanding of statistics to be able to apply it completely and correctly. Moreover, there are different statistical techniques for different data types. One must identify the right techniques to use depending upon the nature and quantity of data available. Many a times, we need to apply statistics in multiple stages (like the Bonferroni correction) to get more accurate results.

• Building expertise takes time and efforts. Naturally, it is costly. It involves having the right people with deep level of knowledge and experience to apply analytics. Most people stop at the basics.

Due to this, it is rare to see use of statistics in analytics. Hence, despite being beneficial for business, useful analytical insights are hard to achieve.

Thus, analytics without statistics is anything but useful. But analytics with statistics is powerful. It delivers meaningful benefits.

That is why, at Veravizion, statistics is the indispensable part of all our analytics and consulting work.

There are several instances where the right kind of analytics (that include statistics) have rendered spectacular results. Analytics is reshaping industries like retail, consumer goods, healthcare, banking, and agriculture, among others. But that is a topic for another Veracle.

What has been your experience of implementing analytics?

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<– What are isolated evidence and random variation

Fields Medal, Open Problem, and Business Decisions –>

Cover Photo courtesy: Online stat psu edu

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We know that inferential statistics helps people to make intelligent and accurate conclusions about a greater population based on analysis results of a small sample. Simply put, we can make estimations about populations based on a small sample of people.

One example: if we met a small group of doctors and find that the cardiologists among them earned more than general physicians, we could infer that cardiologists, generally, earned more than physicians.

Another example: In exit polls, the pollsters ask a small group of people at polling stations about who they have voted. Based on their responses, the pollsters make a generalised estimation on who is likely to win from that constituency.

However, there can be two problems in here.

1. Isolated evidence
2. Random variation

Isolated evidence

The problem of isolated evidence happens when we draw inferences based on only a few cases. Such inferences might not be accurate.

Like the above example, if we happen to know only the top cardiologists who earn high salaries, we might be tempted to generalise that all cardiologists earn high salaries. This is because we personally know a few that earn high salaries. Here, we have isolated evidence of only a few known cardiologists that do not represent the entire population of cardiologists.

In case of isolated evidence, we generalise based on known cases. So, there is an element of cognitive bias. Since cognitive biases strongly influence our decisions, we tend to generalise based on these cognitive biases. It influences so much that we look at every evidence in the light of our cognitive biases.

This problem is more likely to occur in the context of personal experiences.

For example, if we do not have a good experience of a certain product (or a service or an institution), we will desist our friend from using it. Ours may be a case of isolated evidence of bad experience with that product (or service or person). Most other people might have had other experiences. In short, our isolated evidence is not enough to conclude whether something is good or bad.

Random variation

The problem of random variation happens when we have insufficient sample data which may not be representative of the population. Here, we are likely to make inaccurate predictions about the entire population based on inadequate data. In such cases, any observed trend is out of randomness.

Random variation is independent of the effects of cognitive and systematic biases. We must aim to collate sufficient data points to nullify the effect of random variation. In general, the larger the sample size, the smaller the effect of random variation on our estimation. As the sample size increases, the random variation decreases, and the estimation accuracy increases.

In the above polling example, the pollsters may survey only a few people from a tiny number of polling stations. The variation thus obtained is more likely to be random than indicative of the entire population.

Don’t they sound the same?

It is easy to confuse between isolated evidence and random variation.

We can even say that isolated evidence is a special case of random variation.

However, there is one key difference.

Isolated evidence is rooted in the form of personal bias. Whereas the random variation comes purely from inadequate sample data.

That is why isolated evidence is more prevalent in people’s personal experiences. So, a friend asking us not to purchase a product is a case of isolated evidence. The star rating of the product on an e-commerce platform is statistical evidence and solves this problem if the rating is given by thousands of unknown people.

So, the next time we are tempted to make a conclusion based on a small sample size, check whether it is statistical evidence, or a case of isolated evidence or random variation.

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<– How can we make difficult decisions?

Is analytics all hype and no substance? –>

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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

<– Analytics in Healthcare: A Veravizion Case Study

Is business strategy really indispensable? –>

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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:

“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.

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 14^th 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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“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

Share this Infographic On Your Site

<p><strong>Please include attribution to https://www.veravizion.com with this graphic.</strong></p><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <p><a href=’https://www.veravizion.com/the-digital-transformation-imperative-why-businesses-must-have-online-  presence-an-infographic/’><img src=’https://www.veravizion.com/wp-content/uploads/2015/10/INFOGRAPHIC-Why-Businesses-Must-Have-Online-  Presence.jpg’ alt=’INFOGRAPHIC – The Digital Transformation Imperative: Why Businesses Must Have Online Presence’ width=’600px’ border=’0′ /></a></p><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <p>
References:

• http://www.retailingtoday.com/article/study-81-research-online-making-big-purchases
• HBR, hbr.org, #The Future of Shopping
• https://en.wikipedia.org
• Featured image credit: http://unsplash.com

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• Top Analytics Trends 2017 – An INFOGRAPHIC
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<– Data Science: The next frontier for business competitiveness

What does Digital Maturity really mean –>

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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:

• Top Analytics Trends 2016 for SMBs
• Top Analytics Trends 2017 – An INFOGRAPHIC

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