But it is still early days. According to McKinsey, “About 90 percent of the digital data ever created in the world has been generated in just the past two years, only 1 percent of that data has been analyzed”.
I think where many companies go wrong is that they do not have a clearly articulated strategy around analytics. How much extra revenue can I generate if I had access to insights that Analytics can produce? What if Shoppers Stop said that we will target a Rs 1000 crore revenue by crafting an Analytics led strategy. How do we bake this in the company’s Annual operating plan along with the micro level changes required in company structure & business processes. How do we ensure that the business use cases associated with this are sufficiently detailed to ensure that we put together the data required to actually do the analytics. The next thing companies need to do honestly, is assess where we are on this journey. One of the most critical pieces in the journey is to embed analytics folk on the business side to become translators who help the traditional business manager understand what is being done with the analytics. This is a very significant step & companies need to invest fully in this to allow for such translation to happen on the ground.

What’s the difference? Is there a category of organization which is able to leverage “data” far more effectively? Who values Analytics more? Some industries seem to believe in the power of analytics & actually base decisions on this. A bank decides whether to give a loan or not basis an Application score card or a credit card company spots a fraudulent activity basis analytics & stops the card usage. A lot of other industries use Analytics for insight generation but are they as good as the Banking & Financial services industry (BFSI) in linking the analytics to action.

Clearly an important element that accounts for how data led the organisation can become, is the “Culture”. A way of working in the business where everybody assembles data to take key decisions. When Warby Parker selected its first office location outside New York, it considered a large set of variables — Gallup’s Well-being index, talent pool, cost of living, number and cost of flights to New York, etc. — and ranked and weighted them as part of the final decision. So what is critical is that, a data-driven organization will use the data as critical evidence to help inform and influence strategy.

Data led organisations also “test” a lot. They allow the data from the tests to do the talking & they make key decisions by testing. I think the “new age” companies who are born out of the internet revolution do this very well. Could the enterprise become a full-time laboratory? Digital native companies were built for data and analytics–based disruption from their inception.

While new age companies are structured to do this very well, there are a few traditional or Legacy Companies who have also created a differentiator using Testing methods. Nigel Morris, one of Capital One’s cofounders says that the company’s multifunctional teams of financial analysts, IT specialists, and marketers conduct more than 65,000 tests each year, experimenting with combinations of market segments and new products.

Google-executive-turned-Yahoo-CEO-thought-leader Marissa Mayer declares that “data is apolitical” and that her old company succeeds because it is so data-driven: “It all comes down to data. Run a 1% test [on 1% of the audience] and whichever design does best against the user-happiness metrics over a two-week period is the one we launch. We have a very academic environment where we’re looking at data all the time. We probably have somewhere between 50 and 100 experiments running on live traffic, everything from the default number of results to underlined links to how big an arrow should be. We’re trying all those different things.”

Here is an excerpt from Carl Anderson’s book:
“It was 1998, and Greg Linden, one of Amazon’s early engineers, had an idea. Why not create recommendations on checkout? Supermarkets put candy at the checkout aisle to stimulate impulse buys. That works. Why not peek into the Amazon.com cart and make personalized, relevant recommendations that the customer might appreciate? He hacked up a prototype, got it working, and showed it around. The rest of the story is best told in his own words:
While the reaction was positive, there was some concern. In particular, a marketing senior vice-president was dead set against it. His main objection was that it might distract people away from checking out — it is true that it is much easier and more common to see customers abandon their cart at the register in online retail — and he rallied others to his cause.
At this point, I was told I was forbidden to work on this any further. I was told Amazon was not ready to launch this feature. It should have stopped there.
Instead, I prepared the feature for an online test. I believed in shopping cart recommendations. I wanted to measure the sales impact.”
And what a success this experiment was! 35 percent of what consumers purchase on Amazon comes from product recommendations based on such algorithms. Amazon calls this the “item-to-item collaborative filtering” algorithm and it’s used this algorithm to heavily customize the browsing experience for returning customers.

I guess the “new age” companies are architected to capture data far better than the legacy companies. But a lot of it also comes from mindset. A lot of the new age companies do not hesitate to ask customers for their data, secure in their knowledge that they will provide value back to the customer in this barter. One simple example is Google’s Screenwise Trends panel, which gives a US$5 cash voucher to anyone willing to simply share their Internet browsing behaviour with Google and its partners, with a further US$5 gift every three months thereafter. Or take Raptr, an app that tracks users’ video gaming habits in exchange for regular rewards, such as in-game content or free games. Online fashion retailer Zafu allows customers to buy high end jeans by asking a series of simple questions about the customers’ body type, how well their other jeans fit, and their fashion preferences.The data collection and recommendation steps are not an add-on; they are Zafu’s entire business model

But these are just the starting point. Other businesses will start to develop more creative incentives, from loyalty points through to enhanced services, to encourage consumers to share their data. And the trick lies in making that data central to your business model!

Also maybe, new age companies have younger employees who are not afraid to use data to change the mind of more senior folk. The shared economy of data lets owners capitalize on the First party data they are already collecting. First-party data can be gathered from a marketers’ site traffic, CRM database, or customer purchase history. With their Android and iOS mobile operating systems, respectively, Google and Apple know the location of every customer’s Wi-Fi-enabled phone — far more location data than any other company could access. The Silicon Valley giants aren’t allowing access to such data by outsiders as yet. The new age companies, from Uber to Facebook, hold growing stores of data about user behaviors, and that is a “customer data moat” that they are creating.

Data and analytics are in fact changing the basis of competition. Changes in the business environment affect all sorts of companies. India’s move to demonetise it’s currency was one such trigger. Paytm hit a record 5 million transactions a day starting 10th of November, 2016! In less than 24 hours, Paytm’s platform saw an overwhelming 435% increase in overall traffic — as millions of consumers across India moved to use their Paytm wallets to transact. Instamojo, the online payments service provider for SMEs, also saw a huge surge in merchant signups by 1,500 percent on its platform. And in most businesses now new age companies are fighting for market share with legacy companies. So your investment in analytics capability can pay rich dividends. Earlier Industry leaders invested mammoth amounts into factories and equipment, the new emerging companies invest heavily in digital platforms, data, and analytical talent. But in legacy companies, often Analytics is seen to be too theoretical. Not enough integration with systems has happened to push decisions to the point at which consumers interact with the business. This is far easier to do in new Online businesses which have built their systems around this capability. CIOs & technology teams in large existing legacy businesses are taking time to act on this. New age companies are doing it very well though. Recommender systems are very well integrated with the consumer buying process. As the new age companies compete more with legacy companies, the need to integrate analytics into the business fabric will become even more palpable. So a Meru cabs should be able to pivot & compete with an Ola by embedding some fundamental data & Digital thinking!

And yet,Gartner says that only 20% of enterprise will use more than 50% of the total data they collect to gain competitive advantage. So most companies are capturing only a fraction of the potential value from data and analytics. Companies are now looking at new sources of data that can bring enormous competitive advantage. In Vehicle insurance, where new companies have entered the marketplace with telematics data that provides insight into driving behaviors. Also in personal loans, a while new segment is created basis data trails that didn’t exist earlier. Similarly in Retail businesses, huge amount of Video data exists that can further bring insight that positively impact operations.
So unless companies learn to creatively marshal their data resources, they will leave a lot of opportunities on the table! This is what software architect Grady Booch had in mind when he uttered that famous phrase: “A fool with a tool is still a fool.”

In most legacy businesses, analytics as a function is often seen as a support function. The vision is limited to provide data based support to other business folk. This is a mistake as what is required is a complete disruption. Do you want your analytics team to participate in deeper strategic & longer term decisions in the company?Do Analytics folk with their deep specialist background have the skills to participate in such initiatives? Can they own a P&L & run the complete digital avatar of the legacy business.
So more debate is required before an organisation is able to clearly articulate its Analytics strategy. In fact the triumvirate of Analytics, Digital & Alternate channels go well together. Analytics can disrupt the business model & for companies who really want to compete in this data rich world, a clear articulation of Analytics strategy is very important. And then companies can debate about which analytics team structure is most appropriate. Should the team be centralised or decentralised. Or you could create an internal consulting organisation with resources embedded in user departments. Another way of thinking about this is to create a “hub & spoke” capability with a Centre of Excellence model underpinning it. But more fundamentally, how can a legacy company compete with new age companies & actually use analytics in a more holistic way.

My experience of working in the trenches has been that someone has to lift Analytics out of the mindset of a function & help seed it as a part of Business strategy itself. To do this, you have to articulate the Analytics strategy & expand on how it would impact the company Structure, Incentives, Channels& Processes. Companies need to realise that this needs a lot of involvement at the CXO level & just having an Analytics department would not enable that. An internal or external evangelist needs to push the envelope to create & sustain the strategy. New age companies do it more naturally, legacy companies need to make a solid effort!
Then again companies must realise that Analytics doesn’t need you to solve a technical problem but a “business & social” problem. And most Business analysts have not spent much time in business roles. They are super specialised number crunchers without a sufficient exposure to business reality. Even if the managers have some exposure to business through experience across a variety of analytics projects, is it enough? Analytics and data are transforming companies around the world. Yet one of the great difficulties with analytics is that it can be difficult to explain and understand; it is widely held that analytics specialists don’t communicate well with decision makers, and vice-versa. As a result, analytics adoption is still not easy within legacy companies. Analysts, at one end, are busy learning more specialised & deeply technical methods of analysing data & at the same time they are finding it difficult to get themselves “heard” within organisations. Influencing ultimate decision makers is similar to selling products or services to external customers. Analysts need to understand that when they present ideas to decision makers, it is their responsibility to sell — not the decision maker’s responsibility to buy. Does this bring the analytics career into some jeopardy? No but what it does is ask for the creation of an entirely new role often referred to as the Business Translators. Ideally for this you should take a few of your solid analysts with good communication skills & embed them in the business. Thereby asking them to play a translator role to embed analytics into the fabric of the company. Analysts need to “Story tell” to bring analytics into the fabric of the company. But analysts are too one-dimensional & not embracing the intersection of “technology, statistics & business”. So analysts struggle to tell stories. Often I see journalists do a far better job with infographics in media. But information journalists are not wanting a career in analytics & so there is a gap in “story telling”.

And finally, the Average age in the “new age” companies is far lower. Younger people are adopting analytics far faster. They are getting exposed to it in their education & they are consuming it through their “digital avatars”. They see this often as a “no brainer”. Older executives are harder to convert to this line of thinking.

Unless legacy companies completely relook at how they see analytics, they may lose to New age companies!
So will Amazon eat the legacy businesses for lunch? Time will tell but clearly the legacy companies need to take notice of how data is now a core product & leveraging it can hugely boost company performance. In general, markets now value companies more than the sum of their tangible assets. And so intangible customer data & how it is used by companies will be the key differentiator in the days to come.

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