As an educator, speaker, and data governance professional I sometimes get asked, “How can you measure the effectiveness of your data governance program?” From my experience, there are two main ways to do so: Through a data governance scorecard By evaluating it against a data governance maturity model This series of articles is focusing on

Data governance scorecard is another tool for measuring and monitoring the progress of your data governance program. For a quick reminder, the first tool I covered was the maturity model. What is a data governance scorecard? A data governance scorecard is a collection of agreed-upon baseline and metrics reported on a regular basis, usually monthly,

One way of measuring the effectiveness of your data governance program is to assess it against an existing maturity model. This can help by indicating: where you are with the data governance program how you are progressing or not; and what are some of the steps you need to take in order to evolve your

In  “The trifecta of the best data quality management” article, I’ve addressed why a data quality program is needed and what the recurring steps you should always go through are in order to carry on continuous and sustainable efforts to improve and/ or maintain the quality of your data. When it comes to working with the data itself,

Managing reference data poorly can have a profound impact on your operations as well as business intelligence and analytical outcomes. Here are my 5 best practices for managing reference data: 1. Formalize reference data management (RDM) Most often than not, reference data is not maintained if there is no accountability and ownership determined. Usually the

I get these questions a lot from organizations wanting to start a data governance program or who are in the early stages of planning. A data governance council is a governing body responsible for the strategic guidance of the data governance program, prioritization for the data governance projects and initiatives, approval of organization-wide data policies and standards,

A data sources inventory is meant to record basic information about the different systems across and/or outside your ecosystem from which you are capturing, producing, acquiring or procuring data. The data sources tend to be well known to their end users and administrators, but not so much to the organization as a whole. I believe

Data is only meaningful if it serves a purpose. This purpose is to have it transformed into meaningful insight, hindsight, and foresight for an organization to take advantage of. Similarly to how raw materials go through a complex conveyor belt system in a factory before the end product is created, so does the data before