In my blog post “Measuring data governance programs,” I discussed four distinct categories of measurement that apply to data governance programs: level of policy compliance (addressed in “Data Policy Compliance: Beyond Crime and Punishment”), level of data quality, impact on business performance, and performance of data governance processes. By measuring your program across multiple dimensions, [...]
In my last blog post, I discussed four distinct categories of measurement that apply to data governance programs: level of policy compliance, level of data quality, impact on business performance, performance of data governance processes. The premise of that posting was that these four dimensions of measurement each told us something different about data governance [...]
Assessing the impact of any program or initiative requires measurement of outcomes to determine progress toward objectives. While this may be a blinding glimpse of the obvious, we have to ask ourselves “what is the desired outcome that we want to achieve” before we can decide what to measure and how to measure it. This [...]
In his previous blog, Knowledge Integrity’s David Loshin discussed the “Data Governance Gap” and how to avoid it. He highlighted the importance of understanding and using best practices for managing data policies and operationalizing data governance processes to help close that gap. In this blog, he’ll discuss the life cycle of data policy management as a means of operationalizing data governance processes.
We at Kalido frequently describe data governance as the business process of managing data assets. Data governance processes are day-to-day activities no different than other end-to-end business processes like order-to-cash and procure-to-pay. Data policies are the instruments of data governance; therefore, activities of data governance should be based on the lifecycle of data policies. We [...]
