10 Steps to Creating a Data-Driven Culture

Here is an excerpt from an article written by David Waller for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

Credit:  Steve Bronstein/Getty Images

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Why is it so hard?

Our work in a range of industries indicates that the biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. It is simple enough to describe how to inject data into a decision-making process. It is far harder to make this normal, even automatic, for employees — a shift in mindset that presents a daunting challenge. So we’ve distilled 10 data commandments to help create and sustain a culture with data at its core.

1. Data-driven culture starts at the (very) top. Companies with strong data-driven cultures tend have top managers who set an expectation that decisions must be anchored in data — that this is normal, not novel or exceptional.  They lead through example.  At one retail bank, C-suite leaders together sift through the evidence from controlled market trials to decide on product launches.  At a leading tech firm, senior executives spend 30 minutes at the start of meetings reading detailed summaries of proposals and their supporting facts, so that they can take evidence-based actions. These practices propagate downwards, as employees who want to be taken seriously have to communicate with senior leaders on their terms and in their language. The example set by a few at the top can catalyze substantial shifts in company-wide norms.

2. Choose metrics with care — and cunning. Leaders can exert a powerful effect on behavior by artfully choosing what to measure and what metrics they expect employees to use. Suppose a company can profit by anticipating competitors’ price moves. Well, there’s a metric for that: predictive accuracy through time. So a team should continuously make explicit predictions about the magnitude and direction of such moves. It should also track the quality of those predictions – they will steadily improve!

For example, a leading telco operator wanted to ensure that its network provided key customers with the best possible user experience. But it had only gathered aggregated statistics on network performance, so it knew little about who was receiving what and the service quality they experienced. By creating detailed metrics on customers’ experiences, the operator could make a quantitative analysis of the consumer impact of network upgrades. To do this, the company just needed to have a much tighter grip on the provenance and consumption of its data than is typically the case — and that’s precisely the point.

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Here is a direct link to the complete article.
David Waller is a partner and the head of data Science and analytics for Oliver Wyman Labs.


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