3 most common data governance drivers
From my interactions with organizations that have mature data governance programs and those still in the planning or early adoption stage, I found there are some common drivers for starting a data governance program and here they are.
Which one is yours? Feel free to fill out the poll below.
1. Regulatory compliance
This is affecting all organizations. At the lowest denominator, all organizations need to comply with their own country’s financial regulations. Then there are region specific data privacy regulations, some stricter than others, but noncompliance to those can also end up costing large sums of money as well as bad publicity. For context, according to Deutsche Bank, GDPR fines alone could wipe out 2% from Google’s revenue.
Here are practical takeaways on data classification and how it can help you with GDPR compliance.
Then there are industry specific regulations, such as HIPPA, Basel I and II, GLBA, HACCP, which can also be country specific. Therefore if you are in that industry and have to conduct business in that country, you need to abide by those regulations. Then there are those which might not necessarily relate to your own industry, but with the interaction between you and your customers. Similar to GDPR, you might have the CAN-SPAM, FIPPA, FCRA, etc., or PCI DSS
The reason why this tends to score high in the list of data governance drivers is because of the high risks and costs associated with noncompliance.
2. Data driven decision making
This is an umbrella for a few drivers, so sometimes you might see this stated simply as “implementing a Business Intelligence (BI) program”. Some other times you hear about “starting data analytics”, or “big data adoption”. Even improving overall efficiency and customer satisfaction. The reason why I count all of these under one driver is because they all go under to the idea of knowing what are the best decisions to make based on data.
As a side note, when asked, everyone says this is an important goal to them, but a lot still don’t invest in data governance as a necessary foundation to secure success in such programs. So why is it only important in theory?
3. The quality of your data
It all boils down to data quality and it’s why a lot of organizations are pointing towards this as the main driver. Even those which want to start a BI program, ensure regulatory compliance, become more efficient, increase customer satisfaction, and so on, they need to ensure the data is clean and accurate and in agreement with the data quality dimensions that matter to the business. If you don’t have good data quality, then you won’t accurately know that the right customer unsubscribed from your newsletters and you’re still continuing to send them. You might overcharge someone, send inaccurate financials to the IRS, mislabel ingredients on a product, wrongly categorize those medical lab tests, or draw wrong conclusions from revenue projections. The state of quality of your data can make or break everything and for this you need a good data governance program.
You might think, “Well what about rolling out an Enterprise Resource Planning solution, breaking down departmental data silos, have a single enterprise view of master data and so on?”. Well, arguably these goals still fall under the above 3. What do you think?