Data is all around us. It is in the form of numbers, spreadsheets, pictures, videos and many other things. Companies are now using data and leveraging it to derive impact and grow. Today, data is important. And for organisations to survive and expand, a data-driven culture is very critical.
But what is data-driven culture? Simply put, it is when data is used to make decisions at every level of the organization. A data-driven culture is about replacing the gut feeling to make decisions with facts and assumptions.
However, it is important to note that a large amount of data does not mean a company is data-driven or has a data-driven culture. Organisations need to leverage data to derive insights and make decisions based on it.
What goes into building a data-driven culture?
A company is said to have a data-driven culture when people are clear about the driver metrics they are responsible for and how those metrics move the Key Performance Indicators, or KPIs. There needs to be data democratization, i.e. the information is accessible to the average user. The company needs its employees to understand and use data to make decisions based on their roles. It needs citizen analysts, who can do simpler analytics, and are not dependent on the data team for it. The company also needs a Single Source of Truth—when the employees are making decisions based on the same set of data. It needs to have data governance and Master Data Management in place to maintain uniformity, accuracy, usability, and security of data.
The better these indicators are, the more data-driven a company is. While these may seem complicated, they are actually not. Once you start your data culture journey and properly do it, these factors will slip right into place.
However, it is important to note that data culture is not a one-off project that can be shelved once completed.
Data culture is a journey, where you need to constantly keep working on it, and it will keep on improving.
There are four component of data-driven culture—Data Maturity, Data-Driven Leadership, Data Literacy, and Decision-making Process. These 4Ds are very essential when building a data-driven culture.
A CDO’s Guide to Building a Culture of Data
Solid data maturity is foundational to data culture. It deals with the raw material, i.e. data, and its management. An organization with have good data maturity has high standard data quality and there are checks in place to maintain it.
For a good level of data maturity, it is important to have metadata management in place and ensure that it is aligned with the KPIs. Similarly, it is necessary to record Data Lineage, which helps in understanding what happened to it since its origin. Further, a solid data governance structure should be in place and employees have the right level of access to data based on their decision-making needs.
Other factors that affect data maturity are usability, ease of access, and scalable and agile infrastructure. For example, if a company has an archaic infrastructure in place, it would take too long to access data. In such scenarios, the organization will not use data that is not easily accessible. Further, companies would spend most of their time validating and building alignment rather than on the impact if there is no alignment of the KPIs.
Leaders define the culture of any organization. To establish a data culture, leaders have to step up and lead by example. A data-driven leader asks the right questions and holds his/her teams responsible to ensure that data is being used and a structured process is followed. A data-driven leader sees data as a strategic asset and makes “think and act data” a key strategic priority.
For example, an organization is planning to change the default pricing for an app from annual to monthly subscription. Here, the leader should ensure that the teams are making the decisions based on data. The teams will make the decision based on an experiment — that with correct planning, the sample size is met. In addition, the experiment should show if the uptick in the difference by changing the subscription plan is statistically significant.
Data literacy is the ability to read, use, digest, and interpret data toward meaningful discussion and conclusion. For an organization, data literacy does not mean that employees have an excellent understanding of using and interpreting data. It calls for everyone to have a certain level of data literacy depending upon their job role and the decisions they need to make. However, it also calls for ensuring that there is no data skeptic.
Companies with have a higher data literacy tend to use data to understand their customers better as well how they use the product.
For instance, Susan, a product manager, is a citizen analyst and wants to use data to make changes but her manager is a data skeptic. Would Susan able to make data-driven decisions?
Data needs to be an integral part of that decision-making process to get the most value out of it. Is there a planning mechanism in place to choose between projects to work upon or if there is a lookback mechanism to review the decisions? For example, if the marketing budget is allotted based on the expected return of investment, it can be said that data is being used to make decisions. Most organizations do not have a systematic, data-driven decision-making process.