Over the past several quarters, I’ve had the privilege of speaking with a number of companies involved in data governance. The interesting thing I found: firms who identified both drivers as critical, but only invest in one and not the other.
Case in point: a leading financial services firm implemented a data governance program to improve the comprehension and accuracy of the company’s existing board reports. I learned that one of their goals was to define their business terms and definitions (i.e. business metadata) to help non-technical users improve their understanding of the data used to run the business. What I found fascinating was that this was being done prior to addressing their data quality issues. In fact, when asked, “Do you have data quality challenges?” most business users said “yes”. Unfortunately, no one at this company knew to what extent. Instead, their focus was on defining their business metadata. This leads me to ask, “Can you trust your metadata without addressing your data quality issues as part of a data governance practice?”
If metadata is information about your data which your business users are relying on to drive decisions, but the source data is not clean, how will that affect your business? The answers seem self-explanatory. Of course you can’t trust your metadata if you have poor quality data. For example, business metadata is defined from an approved list of valid values. Unfortunately, if the data used to define those values are incorrect, the downstream impact is you end up with inaccurate metadata.
Organizations implementing data governance programs need to consider the lifecycle of how data is captured, processed, and delivered to downstream systems— whether that is your data warehouse, master data management application, data hub or CRM system. Creating, defining, and publishing business metadata without addressing your data quality issues may not help companies looking to benefit from data governance.
2 Comments
Very valid point Peter, although it would appear obvious that there is a clear relationship between DQ, DG and metadata, it often gets ignored.
I think managing data quality at a metadata level is also important.
I’ve seen companies create metadata in a one-off fashion but surely it should be monitored, measured and improved continuously as systems, processes and our business adapts.
As ever, it depends on the type of metadata.
Basic metadata such as structural or relationship information across entities can be very forgiving of defective data as even if the data is 20% incomplete or a relationship is invalid, the original metadata rules are still accurate.
So I think the key is to combine our metadata rules with a traditional data quality management process. Metadata are simply another form of data quality rules after all.
That way we can have a complete hierarchy from governance to metadata to current DQ. It’s a simple process to then validate precisely what hit our data is impacting on our metadata.
Dylan,
You are absolutely correct. Accurate and consistent metadata definitions depend on having a mature data quality planning, design, and implementation process. The consequences of not having one include incorrect interpretations of the information used by business users which can result in increased regulatory fines, higher marketing spend, and customer dissatisfaction. Therefore, metadata provides the context for data quality and data quality processes support the ability to define accurate metadata. This symbiotic relationship is absolutely essential for successful data governance. Companies who ignore this position will continue to be exposed to incomplete and inaccurate information and will fail to meet their data governance objectives.
Peter