Exploding quantities of data have the potential to fuel a new era of fact-based innovation in corporations, backing up new ideas with solid evidence. Buoyed by hopes of better satisfying customers, streamlining operations, and clarifying strategy, firms have for the past decade amassed data, invested in technologies, and paid handsomely for analytical talent. Yet for many companies a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making.

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.

[Here are the first three “data commandments.”]

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.

3. Don’t pigeonhole your data scientists. Data scientists are often sequestered within a company, with the result that they and business leaders know too little about each another.  Analytics can’t survive or provide value if it operates separately from the rest of a business. Those who have addressed this challenge successfully have generally done so in two ways.

The first tactic is to make any boundaries between the business and the data scientists highly porous. One leading global insurer rotates staff out of centers of excellence and into line roles, where they scale up a proof of concept. Then they may return to the center. A global commodities trading firm has designed new roles in various functional areas and lines of business to augment the analytical sophistication; these roles have dotted-line relationships to centers of excellence.  Ultimately, the particulars matter less than the principle, which is to find ways to fuse domain knowledge and technical knowhow.

Companies at the leading edge use another tactic.  In addition to dragging data science closer to the business, they pull the business toward data science, chiefly by insisting that employees are code-literate and conceptually fluent in quantitative topics. Senior leaders don’t need to be reborn as machine-learning engineers.  But leaders of data-centric organizations cannot remain ignorant of the language of data.