Missing the Ocean for the Stream: What We Can and Cannot Learn from IBM’s New Breakthrough

As part of its perpetual quest to reinvent and perfect its business model, IBM has made an aggressive push into the analytics market in the last half-dozen or so years. The company’s slick, though occasionally confusing ad campaigns (remember those ads with the mysterious red box being unveiled?) often announce its new initiatives, though it is not always clear that a new announcement is indeed a major one. In the analytics space, however, Big Blue does mean business. The announcement of its sizable new business analytics and optimization division is clearly intended to prove as much. Shortly after its announcement, IBM also unveiled a new stream computing platform called “System S” to much fanfare. The breathless enthusiasm of business journalists, technology bloggers and investment analysts has been palpable. But what exactly does this technological advancement do, and what does it mean for your business?

To answer this question, let’s begin briefly by dissecting what IBM has introduced. Imagine that you are receiving a continuous stream of data, such as stock prices on the Nasdaq. These figures must be quickly analyzed so that the proper buy and sell orders can be placed. Suppose that you also need to base your decisions not just on the Nasdaq prices but also the numbers figures coming in from dozens of other exchanges. Read the rest of this entry »

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Cheating Your Way into Business Visibility

Several weeks ago, I wrote a post about how the pace at which the world is accumulating information exceeds our ability to critically evaluate it. For companies that make thousands or millions of marketing decisions every day in the form of price offerings, advertising placements and so on, this translates into making decisions that perpetually involve a greater amount of uncertainty relative to the amount of information we have. The root cause of this problem is a technological one: we do not have the computing power to slice and dice massive datasets in order to glean insight in time to support decisions. An even deeper explanation is that the gap between the rate of information accumulation in businesses and the pace of information transfer improvements will continue to widen at an increasing rate. This poses serious challenges to the capabilities offered by Business Intelligence, not to mention our ability to determine optimal prices. Read the rest of this entry »

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Wanted: Intelligence (Information Need Not Apply)

Professionals in the foreign intelligence community take pains to distinguish between information and bona fide intelligence. Any piece of knowledge, no matter how trivial or irrelevant, is information. Intelligence, by contrast, is the subset of information valued for its relevance rather than simply its level of detail. That distinction is often lost in sector of the enterprise technology industry that is somewhat loosely referred to as Business Intelligence, or BI. This has become a bit of a catchall term for many different software applications and platforms that have widely different intended uses. I would argue that many BI tools that aggregate and organize a company’s information, such as transaction history or customer lists, more often provide information than intelligence. The lexicon is what it is, but calling something “intelligence” does not give it any more value. In order to sustainably outperform the competition, a company needs more than a meticulously organized and well-structured view of its history. Decision makers at all levels need a boost when making decisions amidst uncertainty and where many variables are exerting influence. They need what I would call predictive intelligence, or PI – the ability to narrow down the relevant variables for analysis and accurately measure their impact on the probability of a range of outcomes.

What makes the distinction between information and intelligence critical is that information is getting more accessible by the day. This democratization of BI is evidenced nowhere more so than at Microsoft. In 2008, Microsoft unveiled several projects in the late stages of development that it claims will put BI capabilities at the fingertips of more users than ever before. “Project Madison” will massively increase Microsoft’s information storage capabilities, while the “Kilimanjaro” and “Gemini” projects together will provide easy-to-use reporting and analysis tools designed to drastically reduce the complexity of using traditional BI tools – all at very low cost compared to large-scale ERP implementation. The possibilities abound. But I still ask the question: what are all of these newly empowered users going to do with all of this information once they can access it at the push of a button?

I am excited by the idea of so many more information workers being able to ask the questions that end up driving businesses to continuously reinvent and perfect themselves, but I worry about relevance. Will these capabilities actually increase the amount of intelligence available to decision makers? Any business decision can be thought of as a bet that some desired future state will materialize as a result of a present course of action. Business intelligence tools as we know them more often than not do not help us make more intelligent bets when it comes to the future. The problem is that we think they do. More data often makes the task of identifying the true predictors of business success and isolating their effects more difficult. In order for a company to get the most out of its data, it needs PI as well as BI capabilities at the fingertips of decision makers. For marketing and pricing to become a more fact-driven corporate discipline, we must recognize the need not for more data, but ways of evaluating the probability of outcomes based on only the factors that matter. This is not child’s play. Information alone, however well-groomed, is simply not sufficient to meet this need.

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