By Dr. Ben Gilad, ACI Faculty
Google (or, just to be fair, Bing) ‘Big Data’ and you get 749 million+ results. The top results are Oracle, IBM, SAS (also IBM), McKinsey, Informatica. Big Data analytics are Big Business. But, is that expense justified?
Definitely, and… Not at all – depending on what you expect of it.
Big data (and analytics to process it) means better, faster, more accurate forecasts and therefor better, faster, more accurate management. The business media (and big data analytics solutions providers) can provide piles of stories, anecdotes, and case studies to show how big data allowed companies to:
- More precisely target advertising and promotion
- Segment the customer base
- Plan production and inventory
- Support a horde of other internal operations from human resources to marketing to R&D.
It comes down to common sense: Which management will be more effective – one using guesses and intuition or one using real data?
But, Everyone Using Big Data Analytics is Performing Better, Right?
The answer should be simple, but it is not. In a “The King has no clothes” type scenario, there is actually little reliable data on the true value of Big Data.
In an HBR piece by Andrew McAfee of MIT, titled no less than “Big Data- The Management Revolution,” the author admits to scarcity of good measurements about the revolution of better management. In the piece, he reports the results of a survey done by his team and McKinsey that shows those companies who reported using Big Data analytics performed better. Yet, in the same piece, he confessed that too many executives are “pretending to be more data-driven than they actually are.” This supports Phil Rosenzweig’s conclusion about similar studies suffering from the Halo Effect – the effect of the dependent variable (performance) on the independent variable (use of big data), making the results quite suspect.
Big Data Analytics Can’t Hurt, Though, Can They?
Even though there is no data on the value of big data, we can declare that using more data and better statistical techniques can only improve management. But that is actually the problem as much as the solution. The common use of Big Data analytics has been almost completely restricted to internal data or social media’s tactical information. Both belong to what Michael Porter called “Operational Effectiveness,” but they have little or no effect on strategies.
Managing better should be (and is) on every executive’s mind. But this is a race with no winners and no sustainable competitive advantage. As one Fortune company uses Big Data and Analytics, another will rush to do the same. The race will continue until every one of the Fortune 500 makes IBM, SAS, Informatica, and McKinsey even bigger and richer. At that point, only those platform providers/consultants will be enriched, not the companies themselves. The best analogy is investment in IT.
The difference between using Big Data and Analytics for improving current execution of existing strategy versus improving the strategy itself can be made crystal clear using an example brought by the aforementioned HBR article to demonstrate the value of Big Data. The article tells about how a large company improved its marketing promotion execution for its various brands by using Big Data. The speed of the promotion cycle went down from 8 weeks to one! The company?
Sears. Need I say more?
In fairness, the author acknowledges that Big Data are not a substitute for the right vision and strategy, but his caveats are naturally a kind of an afterthought, since he sells Big Data consulting. It shouldn’t be.
Success Relies on Big Data Analytics Looking Forward Instead of Backward
Replacing good tactical decisions with excellent tactical decisions doesn’t change the core of companies’ problems one iota: identifying early enough strategic risks and strategic opportunities and acting on them proactively before performance suffers.
To address this core problem, Big Data (and the analytics to convert it to intelligence) need to migrate to the competitive environment with its multitude of high impact players (HIP) affecting the performance of the company and its future.
Picasso once said, “Computers are useless. They can only give you answers.” In the field of strategy, this quote is priceless. The issue of applying Big Data analytics to competition (in the broader, HIP sense), is simple:
Given the exabytes, or zettabytes or whocaresbytes of data available, what questions should you ask?
And that’s where the competitive intelligence analysts we train become true data scientists. And that is where the future of early warning and companies’ survival lies.
If you’d like to build a competitive intelligence perspective into your management team, these courses at the Academy are a great place to start: