Data Governance Tooling — Data Catalogue Value Case(Ep1)

lawrence giordano
4 min readSep 6, 2023

I’m a firm believer in the fact that a good tool is only as good as its use cases, blindly thinking that a tool will fix all of your problems will only end in tears, in this series I am going to explore the tools for data governance, to be open and transparent, I have worked with a lot of tools but by no means all of them, where I can I will provide some hands on experiences of what I liked and what I don't, but in the vein of transparency in some cases I will be using research to conclude my findings.

This is a broad topic, one that if we cover in one article would take way to long and lets be honest its not the easiest thing to sit and read for hours on end, so lets start at the beginning, you want a tool for your data governance program right? but how can you convince the purse strings its a good idea.

The most common tool amongst data governance programs is a data catalogue, as a tool they have matured over the recent years to cover many use case from a glossary of common business terms, metadata capture, data lineage, data quality and policy libraries to name just a few. Given the rise of new architectural paradigms such as data mesh and the move to modern data stacks, the humble data catalogue is having a bit of a boom. For a data governance program to embark on a data catalogue journey there is a requirement for business case or clear value statements, this not only ensures you secure the funding for the program but can act as a anchor for your plans or to a cliché :

What's the low hanging fruit that will add value the quickest and speed up adoption.

Here are some example use cases for the business case, we will then explore how you can associate business value to each:

  1. Data Definitions: The ability for a user to search for an: Data Attribute, System, Report, KPI or Service and view the definition and owner
  2. Automated Discovery: The ability to ingest a data set and provide a view of sensitivity classification, lineage, and structure.
  3. Workflows: Ability for user interaction through either flag against attributes, ingestion alerts i.e. DQ or classification
  4. Data Definitions — Review: Flags against an artefact to signify confidence level i.e…

--

--