Betting Big on Big Data?

Are you in a hurry to catch up with all the Big Data news and how it’s going to affect your organization? Are you worried that you’ve missed the social media bus? Or, is someone telling you to move to the cloud? Hadoop? Terabytes and Petabytes of information that you need to process? Real time systems and dashboards? Enterprise mobility solutions? iPads? Micro-segmentation? Platform enabled solutions? 1-to-1 solutions?

If you’re close to a coffee shop, I’d recommend you walk in, get yourself a cup of coffee and sit by the window and relax. Some of us want you to over-react and buy technology, services, analytics, cloud, or something else because that’s what we always do. And that’s what you always do with your customers. Once you’ve identified a buzz word, you want everyone to catch on to it. We want you to geel that you’re missing out on something groundbreaking.

Even though the fundamentals of what is being consumed hasn’t shifted significantly. What do I mean by that?

  • Are you selling a different product?
  • Are you selling it to a new customer?
  • Have the underlying economics changed? Of creating/delivering the product experience?
  • Has the channel changed? Are all your customers shifting online?


And several such basic questions. What exactly are you expecting this investment to deliver for you? And while you’re still noodling over big data and analytics, think about this –

  • Big Data is a very contextual thing. For my mother, big data would mean that all three of us siblings start talking at once. For the HR department of a small organization, it would be sifting through the paperwork required to get everyone a work-permit in the various countries where our team members might be required to work. For the marketing team of another organization it could be the buzz that each of their campaigns is generating across channels, and whether its effectively being tracked
  • Data has always been big, in relative context terms. What has been a challenge is your ability to process this data. Microsoft excel moved from 65k rows to a million plus and continues integrating it with other Microsoft database tools to add more functionality. Likewise, programming interfaces started developing intuitive UIs for tech-incapacitated analysts carry on with their analysis. The tools will evolve to support the needs of the hour. Your need is to evaluate your game and what lies ahead, and not get caught up in what the critics are saying all the time (not to say that you should never listen to them). Don’t always look in the rear view mirror. And don’t always listen to the back-seat driver. Sometime’s you have to deal with the cockpits.
  • The basic rules of engagement have not changed. Analytics should focus on the business. Business should always start with the basics. One of the best managers I worked with had this habit of never recommending anything analytically complex to start with, but rather focusing on a few questions. Consulting firms take a lot of pride in their hypotheses driven approach to problem solving. The same applies here. The analysis/analytics/modeling etc. is a tool to answer the needs of the business. It is not the answer itself. I think it was Einstein who said that if I have finite time to solve a really difficult question, then I’d spend 95% of the time thinking about the right questions to ask, because asking the right question invariably gets you to the right answer.
  • Don’t let it go the IT way. Remember the large scale technology investments in your data warehouses, organization systems, POS implementations, etc. Remember how you realized every three months that something was not being captured by that system? Or, not accurately enough. Almost every client that we have worked with, and this includes the ace financial services firms, insurance firms, retail giants, etc. using analytics heavily, the quality of data has been suspect. For three reasons – the difference between legacy systems and incremental value added infrastructure for specific needs. B) No clear owner. C) Constant back and forth between business and technology on what is required vs what is possible. Analytics is at the same cross road, and combined with the mistakes made on the data quality front, you will soon find yourself repeating your mistakes in a more real time manner.
  • Differentiate between analytical capabilities, technological capabilities and business capabilities. Technology will help you process big data, but you need analysis capabilities to question the changing dimensions of your business. And if any analysis that is not tied to the business it’s impacting, it might as well stay in the analyst’s laptop.

It’s not to say that you should not invest. It’s time you start running a fact based business, if nothing else. Or, as consultants are blamed for – stop pulling insights out of your backside. It’s time you developed the capabilities to do backward and forward looking analysis backed by strong business cases and communicated through effective visualization and quality dashboards. At the same time, don’t get swayed with this large wave of discussions. Its time you pulled out the rocking chair in the attic, sat back, and thought about how to do more with less, and how to get the basic fence in place. Because, it’s also the time when confusing information will hit you at ever increasing speeds.


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