Tag Archives: Business Success

Two Finals, Two Ties and a common winner

Two Finals, Two Ties, and the Common Winner

Veravizion 1 Comments

“Fed-ex is doing great; is gonna win the finals this time [sic],” said M.

“If the match goes beyond 4 sets, then Djoko will surely lift the Wimbledon trophy,” bet I.

M, a die-hard Federer fan with whom I am having almost fortnightly consulting sessions these days, and I were discussing about the possible Wimbledon-2019 winner. We had an unusual start of the day this week, playing the ‘let’s predict the Federer-Djokovic finals winner’ game, after India crashed out of CWC-19.

Suddenly, the Federer-Djokovic finals seemed more entertaining to watch.

In the end, both of us were right.

Fed-ex indeed did do great coming close to winning the title on two occasions. Djoko indeed lifted the trophy with Federer’s massive mis-hit in the end.

There he squats down to follow his winner’s routine of nibbling on a few blades of Wimbledon grass – the sweet taste of success!

. The Championships 2019. Held at The All England Lawn Tennis Club, Wimbledon. \{year4}{month0}{day0}\. Credit: AELTC/Simon Bruty

No, I do not intend to write this post telling how predictive analytics has helped predict Djoko to be the winner against Federer in today’s match.

Except, that is what this might turn out to be!

For the starters, a Federer-Djokovic match has gone beyond the realm of simple analytics.

Why?

Till date, Federer is the only gentleman (ahem… Wimbledon effect, you see!) to have beaten Djokovic in all four Grand Slam tournaments.

Similarly, Djokovic is the only man to have beaten Federer in all the four majors.

Federer has an all-time high number of 20 Grand Slam titles under his belt, while Djokovic, with today’s win, is not too far behind with 16.

Federer has held the world No. 1 spot in the ATP rankings for a record total of 310 weeks, while Djokovic continues to do so for over 250 weeks now.

The playing surface hasn’t been a decider either; both of them have beaten each-other on all surfaces.

Federer is marginally better on 1st serve win % (which is usually > 70%), while Djokovic only slightly trumps on 2nd serve win % (hovering around 60%).

And both gentlemen have achieved a career Grand Slam, two of only eight people to have done so, ever.

With such complex statistics behind the two greats, how do we conclusively analyse, and say that Djoko has a better chance at winning 2019?

Let’s consider these facts:

Federer and Djokovic have faced head-to-head 48 times, with Djoko winning 54% of the times (or 26 games) overall.

Federer dominated Djoko in 13 matches out of 19 (with a win % of 68.5) until 2010.

Since 2011, Djoko has beaten Federer 20 out of 29 times (with a win % of 68.9).

Isn’t this amazing?

Roger ruled the Wimbledon centre court until 2010, winning it 6 times.

Djokovic dominated (well, almost) the Wimbledon since 2011, winning it 5 times.

What comes next is even more amusing.

I had written in this 2015 Veracle on Lessons from Wimbledon Centre Court, how Federer’s winning points came off 5 rallies or less. And the more rallies he played (against Djokovic), the more likely he stood to lose.

Switch to 2019, Djokovic’s winning points came from 8 rallies or more; the more rallies he forced on Federer, the more unforced errors Federer rallied.

In 2015, Federer had 35 unforced errors against Djokovic’s 16.

In 2019, Federer rallied 62 unforced errors, 10 more than Djoko.

In 2015, Federer ran 5 meters more than Djokovic, for every point scored.

In 2019, Federer covered 7 meters more distance than Djokovic, for every point scored.

The Two Finals

Then, there is an uncanny resemblance in the match stats between 2015-final and 2019-final.

And I found it fascinating to compare the score-lines from the Wimbledon finals of 2009 and 2019.

2009 Wimbledon gentlemen’s final score-line:

Federer d. Roddick 5–7, 7–6(8–6), 7–6(7–5), 3–6, 16–14

2019 Wimbledon gentlemen’s final score-line:

Djokovic d. Federer 7–6(7–5), 1–6, 7–6(7–4), 4–6, 13–12(7–3)

See the only slight twist in the two score-lines? Just flip the scores of first two sets.

So much for the analytics…

Having said that, what a final it was! The longest one in Wimbledon history – and probably one of the most entertaining ones too?!

In the other tie, England defeated New Zealand with a margin as wide as a thin blade of grass.

In sum, when it comes to such close calls, nerves win. And DATA!

Just saying…

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

Do follow Veravizion on LinkedInTwitter or Facebook to receive easy updates.

Cover photo credit: Wimbledon.com

Can a business still win with a Nope Strategy

Can a business still win with a Nope Strategy?

Veravizion 2 comments

The Nope Strategy is first of the three types discussed in the previous Veracle titled “Does your business have a working strategy”. It raises a pertinent question, “Can a business still win with a Nope Strategy?”

To give you a quick recap, we read that a business suffers with Nope Strategy when its business-executives:

  • do not want to lose the status quo, so they just want to protect their turf rather than look for new areas of predictable growth, and
  • do not commit any resources for new growth strategy

What happens at a business that has no growth strategy?

Three things ensue –

When a business has no clear strategy, it does not have a specific direction to pursue nor a plan to communicate to its employees.

In such a scenario, the employees do not feel connected by any common long-term objective (even if there is one). Employees tend to work in a silo and perform their tasks on a tactical day-to-day level.

Gradually, the organisation loses track of what customers want. As the business lacks any formal strategy, it is not able to cope when there is an external change (in the micro- or macro-economic environment) like change in customer buying preferences or a change in technology.

When that happens at a company having Nope Strategy, the change triggers the downfall and eventual close-down of the business.

Let me explain this situation with “the ship’s voyage” metaphor.

When a ship sets sail on a voyage, its port of destination is known. And, the captain must work out and navigate the right route to reach the port of destination.

Similarly, a business operates with an aim to achieve certain objective. And, the business executives must design and implement the right strategy to achieve the objective.

A ship must keep sailing along the planned route to reach its port of destination. A ship without a planned route slowly strays eventually losing steam, stalls, and sinks.

Likewise, a business must keep operating with the right strategy to achieve its objectives. A business without a strategy eventually runs out of resources, falters, and fails.

Unfortunately, there are many examples of once brilliant businesses failing and going bankrupt because of having no credible strategy.

that’s cute – but don’t tell anyone about it.

Kodak was predominantly a film-based business. Kodak engineer invented the first digital camera. Nonetheless, the management did not see the need for any growth strategy nor did they invest any resources in it. They were insistent on protecting their film-based business. Even when customers started dumping film for digital cameras, Kodak refused to have any strategy to tackle the change, and eventually went bankrupt in 2012.

Similarly, Hitachi, and Macy’s had no credible digital strategy, so when the digital revolution happened, these once immensely popular brands lost their way.

Sears was a huge success running its chain of general departmental stores. However, when Walmart and Kmart made the large retail stores popular, Sears had no strategy to adapt to the change, and faltered.

How Nokia killed Nokia?

Let us take a deeper look into an example of a well-known business that hardly had a strategy when faced with a market change.

In October 1998, Nokia became the best-selling mobile phone brand on the planet. As shown in Exhibit 1, Nokia’s operating profits steadily rose (along with its share price) from 1995 to 2000. During the early years of the millennium, early smartphones started crowding the mobile market, which somewhat hit the Nokia share price. Nevertheless, the share price kept rising with the popularity of Symbian-OS.

Until 2007, Nokia’s Symbian-OS was the undisputed market leader with 60% smartphone market share (see Exhibit 2).

What happened after that is interesting!

On June 29th, 2007, Apple launched iPhone, and in mid-2008, Google partnered with other smartphone manufacturers to capture the smartphones market via Android-OS.

This was the start of the steady decline for Nokia smartphones.

As shown in Exhibit-3, Android (in rising blue line) was grabbing the smartphones market so far dominated by Nokia (in declining yellow line). Over the next few years, Nokia witnessed one of the most painful declines in business history. While this was happening, Nokia hardly had any strategy to stem the fall (even considering the launch of Nokia N97, dubbed the iPhone killer).

Nokia just appears to be at a loss about its strategy.

The downfall and eventual demise of Nokia’s smartphone business (in 2013) lies in the fact that they did not have any strategy to counter the onslaught of newer smartphones.

This observation is echoed by Dr. Yves Doz, INSEAD Emeritus Professor of Strategic Management, in his book Ringtone: Exploring the Rise and Fall of Nokia in Mobile Phones. The reasons mentioned in the book allude to an absence of a unified growth strategy and deterioration of strategic thinking within Nokia management.

According to Dr. Doz, Nokia was in strategic stasis that was visible in symptoms such as dysfunctional organisational structure, growing bureaucracy, and management infighting.

This implies that a business cannot survive for long without a strategy.

Does this mean, then, that a business is likely to do well, as long as it is able to “keep sailing the ship in some direction?”. This will be discussed in the next Veracle on “Hope Strategy”.

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

Do follow Veravizion on LinkedIn, Twitter or Facebook to receive easy updates.

Cover photo credit: tracy morgan on Unsplash

Does your business have a working strategy

Does Your Business Have a Working Strategy?

Veravizion 2 comments

The previous Veracle discussed whether strategy is really indispensable for businesses. It ends with a few contemplative questions for businesspeople.

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 try to discuss more about what makes a working strategy, let us first understand the strategies businesses typically employ.

A close observation of businesses, including e-commerce companies, reveal 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 (and yet what many businesses in reality do!).

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

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

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

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

Kodak is an example where the top management was cost-saving oriented; they were reluctant to look beyond film as an area for future growth.

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

Xerox and Sony help us explain this phenomenon.

Xerox was actually the first company to invent the PC and yet did not commit resources to its advancement thereby losing in the marketplace 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.

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 example is discussed at length in the next Veracle.

In “Hope strategy” approach, business-executives are keen for the positive growth outcome but are not inclined to commit 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.

Deliberate strategy, on the other hand, is interesting. In this approach, an organization devises a plan of action that includes the components 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 of the last decade is an 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 which assists an organization achieve their business objectives in a predictable manner.

Predictability is key here!

That is why deliberate strategy is important!

It allows an organization to drive their business objectives in a predictable manner.

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

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

Do follow Veravizion on LinkedInTwitter or Facebook to receive easy updates.

Cover photo credit: photo by ricardo frantz on Unsplash

Is business strategy really indispensable?

Is business strategy really indispensable?

Veravizion No Comments

Why do I need a business strategy?

Recently, this question was asked of me by the chief executive of a retail business that sells moderately well. To answer this question, let us first understand what strategy is!

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

Strategy is applied in many different contexts. Military, sports, and business are some areas where strategy is necessary to win.

In 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 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 competitive advantage, and achieve goals of financial security.

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

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

What if you are the general of an army having limitless troops and tanks? What if you are the coach of a sports team having boundless talent and practice hours? What if you are 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 may have resources in huge numbers, but they are always finite in quantity.

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

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

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

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

Strategy is that mechanism!

Strategy is important because resources are always finite!

There 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 competitive advantage against your rivals of any size, and can still win.

There is a gem of an 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 maximum possible returns.

Some Examples

There are numerous examples in 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 manoeuvres. 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 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. Because Brazil were 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 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 Soviet Union that consisted primarily of professional players.

Examples from Business World

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. 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 and 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 making Mateschitz 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?

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

Do follow Veravizion on LinkedIn, Twitter, or Facebook, to receive easy updates.

Cover photo credit: photo by rawpixel on Unsplash.

Top analytics trends 2017

Top analytics trends 2017 – An INFOGRAPHIC

Veravizion No Comments

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

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

Do follow Veravizion on LinkedIn, Twitter, Facebook, or Google+ to receive easy updates.

Most Popular Perspectives from 2015

Most Popular Perspectives from 2015

Veravizion No Comments

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!

 

 

Strategic transformation photo credit: businessinsider

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

Do follow Veravizion on LinkedIn, Twitter, Facebook, or Google+ to receive easy updates.

 

Data Science

Data Science: The Next Frontier for Business Competitiveness

Veravizion No Comments
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)).”

 


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

Veravizion 2 comments

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.

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.

Do follow Veravizion on LinkedIn, Twitter, Facebook, or Google+ to receive easy updates.

 

1

Join Your Peers for Monthly Posts

We'll never share your email Id & you can opt out at any time, we promise!

Schedule Your Complimentary Session with us Today!

We promise you will come away with insights on utilizing your business data productively and cost-effectively. This session will be free of charge and there is no obligation.