The ‘how’ of data governance: implementing data quality initiatives

Data governance is as broad a topic as data itself, and as such, there are far too many of these ‘how’s’ to cover in one article. This article is the second part in a series to go over many of the desk level procedures that make up data governance. For more information on this topic, read part one, Properly documenting data assets and part three, Data security, privacy and compliance.

For organizations striving to make data-informed decisions, data quality is key. When the accuracy or completeness of data is poor, leaders quickly lose trust and lack confidence to make informed business decisions. An organization’s data assets can only provide value if they are being utilized properly.

The topic of data governance continues to be inaccessible and difficult to define, despite how much airtime it gets in the industry. Given the wide breadth of domains that data governance covers, breaking down data governance into digestible sections is helpful; this article does that by being part two in a three part series. The first article in the series detailed how documenting data assets can provide concrete benefits for a data program. Part two of this series will continue that trend of outlining the ‘how’ of data governance by focusing on how to implement data quality initiatives in a governance program and explore tangible steps an organization can take to increase accuracy, efficiency, and trust in their data assets.

Upcycle data assets

Data warehouses are systems that combine data together from multiple different sources within an organization. The data from these warehouses are utilized in analytics and reporting purposes for business leaders to make more informed business decisions. Data warehouses can be very fluid, as data is constantly being added, transformed and merged to meet business needs.   

Similar to Bob Ross’s painting lessons, “happy little data quality issues” are part of life. There is often a desire to scrap data assets that have data quality issues and start over. If an organization does not have data governance practices in place to address data discrepancies, any new data asset is going to end up with the same issues as the original. To avoid this, set in motion a regular cadence for information management to clean-up their data assets. This “spring cleaning” can include: