Published April 27, 2023

The term “Data Culture” has been around for the past decade or so, and many executives would agree that it is imperative for their organization to have a strong Data Culture. But if you also asked them about their Data Culture strategy, many would struggle to give you a clear answer. So let’s get specific and define Data Culture. 

Similar to company culture, a Data Culture is the infrastructure, processes, and practices that dictate how day to day activities occur within an organization, with a specific focus on data and how it is used to communicate and make decisions. Data Culture is the tool implemented to help organizations become more data driven in a practical way.

Why Make Data Driven Decisions?

We use data everyday to optimize the decisions we make in our personal lives. We look up traffic when planning a trip, refer to the expiration date on a gallon of milk, check our phone battery to decide if it’s time to look for a charger. So when it comes to work, it’s no surprise that data should play a part in making decisions, but we often fail to look outside the data gained through our immediate experiences. 

And while a personal decision often only impacts a small group of people, when it comes to making on the job decisions, it often has ripple impacts for the organization, customers, and stakeholders. Accordingly, it is important to get more data points beyond going with the information gained from your singular experiences. Data provides a path to understanding a multiplicity of experiences. 

Measuring Data Culture

If you’re a data person reading this, you’re probably asking, “But where is the data?” 

In my time implementing Data Culture: 

• 90% of employees attended at least one optional data training.

• Departmental report utilization increased 4x, driven both by an increase in users and views per user.

• Overall users reported a 20% increase in the value of data shared at the company level.

Notice that the metrics reported above align with the Data Culture Framework, one for each level: individual, department, and organization. When you measure your own Data Culture’s success, I recommend focusing on the framework to select the right metrics for you.

Who Owns the Data Culture? 

The Data Team (also known as the Business Intelligence team or Analytics team) should be responsible for the organization’s Data Culture. These are the subject matter experts who are responsible for data governance and own the data software within an organization. They run the operations that facilitate a successful Data Culture and are thus integral in the Data Culture’s success. And, if a Data Culture is implemented effectively, they will not scale with the company, but instead, empower their fellow coworkers with the skills that offset the need to hire analysts to keep up with growth. 

But you may be asking yourself, what if an organization doesn’t have a Data Team? Some organizations may find that data responsibilities are distributed across the organization or the organization is too small and too early for a dedicated data resource. In these situations, I advise that the Data Culture belongs to (1) those that make data available or (2) personnel development. The former because often if data is not reliable, trustworthy, and accessible, it is not used, and the latter because individuals ultimately drive a Data Culture through their data skill sets. Again, more on this in future blog entries. 

An Ethical Data Culture

As you start on your Data Culture journey, remember to keep in mind the power of data. It’s no surprise that AI tools are dominating business conversations right now because we have figured out how to tap into immense datasets that feed these new tools. We have seen the power of these algorithms to change individual’s lives as they are increasingly relied upon to make our decisions. 

We have to ensure that, while historic data may show a link between blackness with criminality, femaleness with tech ineptitude, and immigration status with credit unworthiness, we know that in context, it is the societal barriers and marginalizations that these communities face that have allowed this reality to exist, rather than the community’s inherent identities. We should endeavor to use Data for Good, actively highlighting the disparities and working to offset the harms in our society. For more on this, check out LA Tech4Good’s Leading Equitable Data Practices Workshop.

By Cindy Lin

Edited by Hilda Kong and Gabriel McCormick