Data Culture Issues and How to Fix Them
If your organization uses data—and even if it doesn’t—you have a data culture. Think about the ways that you and your colleagues interact with and discuss data. Are people afraid of it? Do they trust it? Is it spoken about as a driver of business and competitive edge or just the exhaust of your existing operations?
Data culture is the organizational processes and social norms surrounding the production, use, and consumption of data. A poor data culture can lead to confusing communication, inconsistent decision-making, and non-actionable insights, while a good one promotes robust, actionable, and data-driven insights.
What should you look out for when assessing your data culture? I have found three key indicators of poor data culture: fear of data, inconsistent use of vocabulary and metrics, and mistrust of data. In this article, I discuss the consequences of these issues and some solutions I have found effective in correcting them.
These data culture issues are largely consequences of having democratized analytical data. Democratization means having data available to the people who need it and have the skill sets to derive meaningful insights from it. The debate between centralization and democratization of data is an entire discussion in its own right, but I have found strong benefits of allowing people to access and understand data for the issues they encounter in their daily work. In this article, I will also explore some of the danger zones of democratized data and their possible solutions.
All organizations are moving towards data-driven insights. Data is a powerful tool that allows us to look at past performance and see aggregate trends to indicate future performance and direct decision-making. If people in organizations are afraid of data, however, they will not be inclined to use it.
Many people feel uncomfortable around data. They may not know how to use it, feel overwhelmed by it, or think that it is an unintuitive black box. They may be afraid of potentially breaking something. Even people who do understand data concepts may be afraid to venture unfamiliar datasets. This hesitation means that people will not use the data to its fullest extent; people who want to make decisions with data will avoid doing so because they are intimidated.
To tackle this issue, we must give people the appropriate tools to feel comfortable with the data. Up-front training and discussions are essential. Instead of handing licenses to new analysts and assuming their skills have prepared them to deal with your company’s data, provide short education sessions on not only the tool, but also the company’s data and best practices.
I tell everyone who takes this kind of training that the goal is not for them to remember everything I say, but instead to know when and how to ask questions. Data can be tricky and there are often specific ways it can and (often more importantly) cannot be used. Instead of telling them to memorize each scenario, I give them the tools to identify these situations and to sniff out if something seems amiss, then provide them with a variety of resources to understand and rectify the issue.
Up-front data education is beneficial to provide context and resources before new analysts have a chance to develop bad habits. They then feel empowered with their data and they know who they can approach with various questions. These training sessions make them feel as if they are a part of the analyst community and help them feel comfortable discussing data issues with other analysts across the organization.
Inconsistent use of vocabulary and metrics can easily lead to confusion in meetings. Here is the kind of scenario I have experienced many times:
People gather in a meeting to discuss last month’s sales and ensure that quarterly numbers are on track. Last month’s sales goal was $1.5M. Director of Operations: “Last month’s sales were $1.7M! We’re definitely on track to make our quarterly numbers.
