In this data-crazed era, businesspeople are prone to assume that data and strategy are closely related — or in some extreme interpretations, even synonymous. Vendors, technologists and executives all seem equally convinced that data is key to realizing strategic advantage, and that by immersing themselves in data, companies will improve across the board in efficiency, productivity and competitiveness. The existence of young, flourishing, data-driven companies like Google and Facebook helps strengthen this impression.
In practice, however, data and strategy are not easy and intuitive to reconcile. The discipline of business data analysis has developed in IT, finance, and other internal departments where it is most often a form of cyclical reporting. Business strategy has grown up separately as an academic and somewhat abstract branch of knowledge. Generally speaking the business executives and consultants most likely to drive strategy work have been situated far from the data. A true reconciliation of these disciplines has never really taken place.
Business strategy as we understand it today came to fruition in the 60s, 70s, and 80s, a history richly chronicled by Walter Kiechel in his book The Lords of Strategy. The original groundbreaking strategy work of people like Bruce Henderson and Michael Porter was fairly empirical and grounded in primary research. But these strategy pioneers were also marketers and salespeople for their own firms and consulting practices. As their theories and methods spread through the business world, they drifted further and further from their empirical roots. Business strategy became more like a folkloric practice based on high-level models, such as the BCG Matrix or Porter’s Five Forces. These models, while intellectually interesting and often useful, were not rigorously tested or supported with data as they were applied to the situations of individual companies.
The gap between these disciplines no longer makes practical or economic sense. In an age of proliferating data and cheap processing power, it should be possible to bring data analysis and business strategy into much closer alignment. We have the data, we have the strategic literature. What we don’t have is practitioners committed to working across these disciplines in a careful and thoughtful way.
I don’t believe that big data — or even traditional enterprise data — will yield up its potential value until it is wedded to strategy. Data most certainly does not speak for itself. Without context, it is meaningless; and without an interpretive framework, it cannot drive decision-making. As the history of departmental BI shows, without strategy data analysis tends to devolve into a bland form of non-financial accounting, generating lots of reports but not much insight or action. Data analysis performed without strategy is the reason that so many organizations complain, even today, that “We have all this data, but we don’t know what to do with it.”
A rapprochement of data and strategy, in contrast, would raise the bar for both disciplines. The use of a strategic frame would force us to think about how we acquire, interpret, and apply data, and free us from the bureaucratic disease of report proliferation. Conversely, the use of data would make strategy more empirical and impose greater rigor on a field that often hovers at the level of folk practice.
In my next article, I’ll explore the practical application of data to an older framework (Porter’s Five Forces) and to a newer one (the Business Model Canvas).
Rob Lawrence 