Analytics Revolution - Why the struggle for growth?

IT majors have been excited about the convergence of Social, Cloud, Analytics and Mobility (SCAM).  It is widely believed that these will be the engines of growth in the future. Rightly so. N Chandrasekaran, CEO of TCS, India’s largest technology services provider, recently referred to the SCAM as "digital forces" and estimates that these digital forces would be a $3-5 billion opportunity in the next few years.  A Gartner study has reported that the SCAM market will be worth $107 billion by 2017.

It is true that Analytics - has generated excitement all around. Everyone can see and experience the impact that this convergence – that engenders Disruptive Innovations  - has on everyone’s life. I personally think that the hype is real and the huge revenue opportunity projected for this market space is based on solid grounds.

What the TCS Chief has not mentioned is that there are significant white spaces – industry-speak for critical gaps and blind spots in the effort to get this revenue. And the fumble, too, is very real. For example, if these projections and forecasts can be translated into revenue, why are we not seeing a Google or a Facebook or even their dwarfs in pure play Analytics?  There appear to be several reasons why IT majors have not been able to take advantage of the opportunities. The revenue is for them to lose unless they learn and take corrective action quickly.

Analytics business is a domain specific, hands-on and a devilish details game where domain expertise is all supreme. However, most global players have not been able to get the right folks to lead the practice. This has proved to be a disastrous non-starter. The problem is also compounded by lack of right skills in the marketplace. The analytics practices at the majors continue to be led by professionals who either have consulting or technology background but weak in hands-on analytics. This has blissfully insulated the practice from the analytic humdrum that businesses are experiencing. This is also reflected in the inability to identify or devise the right vehicle to exploit the surging analytic opportunities. In my view, the lack of appropriate leadership is a major roadblock to growth.

The IT majors also urgently need to revisit the internal business structure. The bunching of analytics catering to different industry segments or verticals under a single business unit may be convenient for administrative and bureaucratic reasons, but has not produced optimal results. This agglutination has come in the way of insight dominance since successful thought leadership in one vertical often has not passed muster at another. I think the analytics practice catering to each industry vertical must be a separate business unit by itself.

The outsourcing industry has mastered the art of building the business via the IT organizations of client companies. However this tested path has not helped build the Analytics business because the key players are not on the IT organization of clients. Outsources need to have a game plan for directly engaging the business side of the house.

Further the majors they are selling software products and tools that are often peripheral and non-core to generating analytical insights. Aided by an expanded definition of analytics, this may help generate revenue in the short run, but this has taken the focus off the insights business.  For example, a hypothetical solution that can build and deliver fraud detection models using large attribute set – including social media attributes – and look-up more than 10,000 datasets and yet instantly deliver accurate detections will be immensely popular.

Big data or new modeling techniques by themselves would not produce a disruptive innovation. The marriage of cutting edge technology and the resulting new innovative analytical techniques that can scale is the winning recipe. This is a keystone for success in analytics practice, yet conspicuous by its absence.

This success recipe has to be combined with a smart go to market strategy. I call it winning-with-a-thousand-cuts strategy. Instead of waiting for the dream multi-million, multi-year project, the focus must shift to building volumes through a huge portfolio of mid-sized projects. Execute several small to medium sized projects that will provide insights to the businesses in short to medium term - 6 to 12 month time frame. This paradigm has the potential for depth - to open up opportunities in every line of business, business unit or team level at clients and hence build scale in the analytics business.

Social Analytics – New uses of Social Intelligence

Article posted SmartData  -  B2C -  Analytic Bridge

We all live in a digital world or rather ‘social digital’ world. This is because so many of us have taken to Facebook, Twitter and other social networking sites/tools that this has become an obsession to many.  

Obviously, the huge social media traffic has provided companies an unprecedented opportunity to showcase and message themselves. Marketers and bankers have happily followed the traffic.  Now every company has a social media strategy,   mostly directed at messaging and feeling the pulse of the customers.  I often think many companies have a strategy more as a ‘place holder’  than to deduce any ‘monetizable’  intelligence ( if there is no such word, I have coined one!). 

How are retailers using social intelligence? Retailers arguably have the largest presence on social media and have successfully used it to build brand recognition by driving traffic to their sites. Yet, many big retailers had negative return on their investments on social media sites since ‘likes’ often did not result in purchase.  Definitely this is not the end of retailers’ social media adventure.  I do expect sharper social analytics will help craft a renewed and financially sustainable strategy in the near future. Why? The traffic is simply too finger licking good to ignore.  So do expect more here. 

How are banks using social intelligence? Definitely banks have made some headway in extracting actionable risk intelligence from social data. For example, to detect fraudulent loan applicants, some banks now seek to cross-match applicants’ personal details like date of birth, location, photos etc. with social data that is readily available from Facebook, Twitter etc. Mismatches are subject to greater /manual scrutiny. 

Risk managers and collection agencies can identify social network behavior of customers whose payment behavior is already known. We can use the same analytical paradigm by taking a set of existing customers (known behavior) and matching it with social networking data – travels (location), payment transactions etc  to glean insights. For example we can track delinquent customers’ travels and see if their delayed payments arise from travel and travel related splurges.  Frequent holiday travel / vacation data emanating from social network intelligence can alert lenders to proactively minimize / reduce / freeze credit lines. The legal and regulatory ramifications of such actions are unclear and will be tested in courts or when regulators take a stand.  But it is well known that Banks have used, are using and will not hesitate to use customer intelligence in creative ways to manage their profitability.

While social analytics is gaining ground, my personal view is that overall impact of social intelligence today has been a mixed bag of success. One key reason is that the extant social analytics tool kit is evolving. Often we find we are not able to garner cool, actionable, disruptive intelligence because we don’t know what to do with this ocean of social data. But that is changing and better tools are coming into the market.

A new paper published by McKinsey points out that social intelligence is now playing a powerful role in helping companies gain strategic insights and develop competitive strategy. New tools are helping social analytics teams to look into user groups, user forums etc. and catch up on what customers are complaining about. 

A talented social analytics team can piece together information on competitor’s new product strategies!  How?  If you listen closely to Twitter chatters, technical help forums etc. , there is wealth of ‘confidential’ information.  For example, techies while seeking help on complex problems they are trying to solve, inadvertently let the cat out of the bag.  That gives you the ‘what’ in the puzzle. Cross matching this user forum posts information / twitter handles with face book or Linkedin can lead to ‘who’ and ‘where’ puzzle pieces. Now thinking social analysts can collate the ‘what’, ‘who’ and ‘where’ and come up with missing pieces. The paper says it is often surprisingly easy to get at confidential information on competitors by deploying appropriate social analytics tools.  

Well, this is scary for companies and do expect more restrictions on employees after the CEOs have taken the time to read this paper.  Behind this emerging scenario is the hidden hand of intelligent social analytics.

Succeeding in Banking Analytics – Choosing the right business model

Also available here

What is an appropriate business engagement model for succeeding in the analytics business?  This is a great question, but has no simple answer. This question seems to be on the minds of leadership in analytics service providers. What engagement model do you choose to build long term trusted relationships with your clients? What is unique in banking analytics space?  In fact, this question also came up recently, rather unexpectedly, when I was having dinner with two old friends in beautiful Los Angeles.

Professional services provided by the vendors range from staff augmentation on one end of the spectrum to high-end solutions consulting that seeks to solve complex business problems.  All this emanates from the thought construct insiders refer to as the engagement maturity model.  This model tracks the morphing of provider-consumer relationship as the two embark on their journey over time. High-end consulting offers higher and obviously desirable ROI. Hence vendors strive to attain this utopia in each of their relationships. In an ideal world, if you can design solutions / offerings that will move clients up on the value chain - from staff augmentation to high end consulting, you have a winning recipe that will make you the top vendor with an enviable revenue stream.  All this is an ideal world. But, how do we work this magic in banking and financial services space in the real world?

Businesses that serve banking and financial services clients face hidden challenges.  It is well known that analytics is what differentiates successful banks. Large, well-run institutions have star-studded analytical teams that have the depth and skills to crunch through well-organized data and come up with insights to make the right decisions.  That is the upside. The down side is that, these teams may sometimes engage in dueling analytics in an effort to be one up on the other, an avoidable waste of resources and talent horsepower. Smaller institutions on the other hand, have smaller talent pool, less organized data and limited capability to undertake complex analytical projects on their own.  

Another key dynamic is that the outcomes of analytical projects impact the banks’ core decisions.  Hence banks often prefer to work with the crème-de-la crème in the business that they can trust. This provides the vendors a great window of opportunity to showcase their excellence in domain expertise, execution and delivery to win the trust of banks. Winning the trust of banks is a prerequisite for deeper, long-term engagements.  

In other words, the analytical ecosystems in these institutions are very different – ranging from the highly competitive and sometimes counterproductive to those with less sophisticated analytical infrastructure. Understanding the extant analytical ecosystem is critical in choosing the right business model for banking analytics providers.

But in the real world what engagement mix should we choose?  Seriously it depends on the prevailing analytical ecosystem of the banking customer. My personal view is that emerging and growing businesses tend to generate a significant proportion of their revenue via an on/off site staff augmentation model. Smaller boutique vendors have successfully demonstrated this as a key entry strategy in a very competitive business.  On the other hand, there seem to be fewer examples of providers choosing high end solutions as the dominant component of their mix. But I think an understanding of the nuances of this industry and the interplay of analytical ecosystem is fundamental to succeeding in banking analytics. This understanding helps discover the right mix.