What is a culture of data?
Data culture is a never-ending journey, you can keep working on it, and it will keep improving, but it has to be constantly worked upon. A company is said to have a data culture when it functions like a well-oiled machine when people are clear of the driver metrics they are responsible for and how their metric moves the KPIs. There is data democratization, and there are citizen analysts who are doing simpler analytics themselves and not dependent on the data team to do simpler analytics. There is a Single Source of truth, and everyone is proper data governance and Master Data Management in place. Data Scientists are working on the highest value problems.
Components of Data Culture
There are 4 components to the above-defined euphoria –
- Data Maturity – Data maturity is one of the most important pillars of data culture. It deals with the raw material i.e. data in this case, for a culture of data. An organization is said to have good data maturity if they have good data quality, and there are checks to ensure the quality. If there is metadata management in place where there is an alignment on definitions of KPIs. Data Lineage is recorded. A solid data governance structure is in place, with people having the right level of access according to their decision-making needs. Other factors that affect data maturity are usability, ease of access, and scalable, agile infrastructure. For example, suppose you have an archaic infrastructure in place. It would just take too long for people to access the data, and anything which is not easy will not get used. If we don’t have alignment in definitions of KPIs, then most of the time would go validating and building alignment of whether the analysis is correct or not than the impact it will have.
- Data-Driven Leadership – Of all the 4-D’s of Data Culture, data-driven leadership is paramount. Leaders have to be the champions and lead with example to create a culture of data. They have to ask the right questions and hold the teams accountable for using data and following a structured process. For example, if we are changing from Annual to Monthly subscription in the default pricing for an app, the leader should hold the team responsible for making the decision based on data. The decision is based on an experiment that with correct planning, the sample size is met or not, and if the uptick in the difference is statistically significant or not.
- Data Literacy – Data literacy is the ability to read, use, digest, and interpret data towards meaningful discussion and conclusion. Everyone in the organization should have some level of data literacy depending upon the decisions they are going to make. Companies with higher data literacy tend to use data to understand their product usage and customers better. Everyone needs to be at a certain level of data literacy depending upon their job role and decision-making support but no one should be a data skeptic. For eg. Susan a product manager is a citizen analyst and wants to use data to make changes based on data but her manager is a data skeptic, would she be able to make data-driven decisions? More can be read on data literacy here.
- Decision-making Process – After having the leadership and people set up, the data needs to be inserted into the decision-making process. Is there a planning mechanism for the projects to be chosen, is there a lookback mechanism? For example, if the marketing budget is allotted based on the expected ROI then it can be said that data is inserted into the decision-making process.
These are the 4D’s of data culture when all of them combine together they form happier customers and product usage.