Data Governance as a Profit Center
Revenue Impact Alongside Cost Savings
Companies often view Data Governance as a method for mitigating risk and decreasing costs. It’s true. Data Governance can help save on data storage costs and ensure compliance with data privacy legislation. However, when applied correctly, it can have a major impact on revenue generating functions in analytics and data science.
What is Data Governance?
Data Governance, or DG, is the practice of making data secure, accurate, discoverable, and accessible. Here is a quick intro to some (not all) of the products that DG teams create.
Common DG Products:
Data Catalogs
Data Policies
Data Quality
Data Lineage
Data Usage
Benefits of DG Products:
Know what data you have
Know what to do with your data
Perform better, faster data processing
Know how your data flows
Recognize valuable data
How Does Data Governance Affect Profit?
Data Governance directly affects the efficient flow of high quality data through your company. It allows teams to quickly add value on top of existing data, which can be used for data driven decision making, data integrations with business partners, and new products for both internal and external usage. Consider the revenue generating benefits of Data Governance in three simple areas.
Data Time-To-Value
Better Reports & Analytics
Efficient Workflows
Data Time-To-Value
“How quickly can you have this report ready by?” For quality reporting, you may not like the answer. The reality of working with data means that data scientists and analysts need to first find the data, gain access to it, explore it, and clean it before they can start adding value with their analysis. Data Governance can make generating meaningful results faster.
Facilitating data discover means that teams can quickly find the data they need by using the data catalog, which also identifies a subject matter expert (SME) for the dataset. Any data user will tell you that having documentation on a dataset will drastically decrease the time it takes to create value.
Further benefiting data time-to-value, the data governance team can certify datasets for quality, meaning that the analysts and data scientists can do basic checks for quality without having to perform arduous data manipulation.
Finally, data governance ensures that data remains protected while getting the right people access to the right data. I have personally witnessed too many products delayed for days or even weeks due to delays in data access requests.
Better Reports and Analytics
High quality data enables the best results from our analytics and data science teams. Quality data allows for the end product to be automated, which adds value by making sure information is in the right place at the right time.
When data is reliable, then reports are accurate and timely, and they will function without breaking down. This builds trust between the business and it’s data, which means leaders are more likely to make data-driven decisions.
Finally, data governance teams can map out data connections. New value can be discovered by merging data sets that haven’t been used together, resulting in net new reporting never before seen by the business. Data users may be able to create new products that can increase sales efficiency, allow for better management of teams, or directly improve the customer experience.
Efficient Procedures
Maintenance is often much more cost effective than periodically doing big data cleanups, allowing for data to stay at high quality. The Data Governance team creates standard operating procedures (SOPs) that enable data maintenance and data development. When the business builds a new product, begins a new partnership, or buys access to a new dataset, an SOP defines what needs to happen for integrating the dataset into the data infrastructure, taking away guess work and allowing for reporting to get up and running faster.
Data governance partners with operational data users to solve the underlying problems of major data issues. In addition to fixing data when it’s wrong, DG will help identify the underlying problem, propose a solution to fix it, and see the project through to implementation.
A culture of integrity can be simply explained as “if you see a problem, fix it”, democratizing the responsibility of identify and fixing issues. These issues, when relating to data, may not only be about data quality, but also helping fix issues with data privacy, and data security as well.
Conclusion
It’s important to consider the revenue generating benefits of data governance alongside the cost savings. This can help motivate your team by understanding how their work is affecting the bottom line. Business leaders around the company may be more likely to buy in to data governance practices when viewing it from a revenue generating perspective. Being able to explain both sides of the benefits will allow you to best position your team for success.
