Posts

Data Quality Measurement: Looking Beyond the Obvious

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, you can better ensure that your organization reaps the greatest benefit from your investment in data governance. Read more

Data Policy Compliance: Beyond Crime and Punishment

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 programs and the policies that are a product of our data governance processes. Each of these dimensions tells us a part of the story, but all are necessary to bring the whole picture into focus. Read more

Measuring data governance programs

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 is especially true in regard to data governance programs.  When we think about measuring the effectiveness of data governance programs, we can think about it across multiple dimensions. Each of these dimensions provides insight into a different aspect of the enterprise that the program touches. Read more

Managing the Data Policy Life Cycle

Since 1999, David Loshin, president of Knowledge Integrity, has developed technical and management methodologies for instituting data quality, master data management, data standards, and data governance in many different industries, including financial services, banking, insurance, health care, manufacturing, pharmaceuticals, and government agencies. Read more

Data Governance Should be Formalized as a Business Process

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 can divide these activities into three sequential steps: policy definition, policy implementation and policy enforcement. Read more

Data Policies are the Instruments of Data Governance

In my last blog, I compared the digital world of data to the physical environment we live in. I made a case for thinking about data governance in terms of data policies aimed at keeping bad data, analogous to trash in the physical environment, out of the data environment in the first place. Read more