A chef knows about all types of peppers and mushrooms, the difference between salted, unsalted, and clarified butter, and what spices work best together. In the same way, you should understand the data you use. If you’re looking at a metric labelled “customer loyalty,” what does that actually mean? The use of a loyalty card? The frequency and volume of purchases? It’s crucial to be clear about what exactly you’re measuring, and that this be standardized across your organization. Without a data dictionary of commonly accepted data definitions, different teams can be literally using different languages, and never realize they’re speaking past each other.

Free range, organic, locally sourced? Part of being a good chef is knowing the origin of your ingredients. Is the data raw, or has it been manipulated in some way? Was it entered by people or automatically generated? The insights you reach could be different depending on the answers.

An example: I was recently talking to a retail executive who was examining data on what was drawing traffic into their stores. This information was collected by sales associates who asked customers during check out why they’d come in. This question was the very last thing before the completion of a sale, a point where the sales associate was likely to be pressed for time and focused on the next customer in line. Yet this data traveled from the stores to corporate headquarters and treated as if it were gospel truth. It's not that the data was useless. But without an awareness of the manual, fallible nature of how it was collected, it was possible that it could lead to misleading conclusions.

For a chef, it’s not just how something tastes. Presentation is crucial. Good chefs care about how their creations appear on the plate, with an appeal to all of the senses. If you want to use data effectively—to produce clear and meaningful insights—you should also look at the whole picture. This means being aware of, among other things, statistical analysis, probability, and how to use data visualization tools. If insights are to flow freely throughout the organization, people need ways to share them that are clear and convincing. And when conversations happen about an insight you want to share, you should be able to explain your conclusions—something that can only happen with an understanding of data basics.

An organization can't simply give people slick-looking dashboards and expect insights to magically flow. There has to be at least a basic level of data literacy. That starts with old-fashioned training. There may be some resistance to this. Some members of your workforce may not have had any kind of numbers training since college. When is the last time your average marketing or HR person had to do statistical analysis? But we find that enthusiasm grows as people see how far even a basic understanding of data goes toward generating and sharing insights.

There are a lot of ways to design a training program, but at Slalom we've found it's good to start with basic statistical concepts such as mean vs. median, R-squared, standard deviation, and what commonly tossed around terms like “statistically significant” actually mean. People don’t have to be statisticians, but should know enough to ask questions and understand the response from a statistician or data scientist.

Another important thing your organization can do: hold leaders to accountable for how they make their decisions. Are they still relying on their gut, or are they making choices that can be justified by numbers? This may be uncomfortable for some leaders because it means some decentralization of authority: As more people have more data, that they should have a corresponding power to make decisions. But the rewards for embracing this data-driven decentralization can be tremendous.

When everyone has access to data, everyone can drive innovation. For an organization to benefit from data, it must be prepared to be guided by its insights, no matter where they lead. That means having the confidence to question the way things have always been done, and, above all, a willingness to try new things. Just like a good chef.