Data Quality metrics are a measuring system that allows the “quality of data” to be evaluated. Data Quality metrics can be used to determine how useful and relevant data is, and it helps to separate high-quality data from low-quality data. It is much easier—and safer—to make business decisions based on reliable information. Poor information (based […]
Building a Data Governance Program: Ten Steps to Success
Building a Data Governance program from the ground up can be a huge undertaking, much like a puzzle, but with no picture as a guide. The Chief Data Officer at American Fidelity Insurance, Ryan Doupe, spoke at DATAVERSITY® Enterprise Data World Conference and presented a practical ten-step plan for starting or improving a Data Governance […]
Data Quality, Data Stewardship, Data Governance: Three Keys
Typically, Data Governance programs start with Data Quality, because that is where end users or stakeholders begin to interact with data, especially from the reporting and analytics perspective. “They get a report that doesn’t match another report and they can’t marry it to other data,” said Mary Anne Hopper, Data Management Consultant at SAS Institute. […]
The Value of Metadata Governance
Data, by itself, is just data. But put data in context—that’s when it becomes useful information. Robert Seiner, while speaking at the DATAVERSITY® Enterprise Data Governance Online Conference, said that what provides that context is metadata: “Data plus metadata equals the information that our organizations need to use to be successful.” Seiner is President and […]
Redefining Success with Agile Data Governance
Laura Madsen wants to challenge your outdated ideas about Data Governance. “I’m pretty sure that we wouldn’t use software that we used 20 years ago, but we’re still using Data Governance and Data Governance methodologies the same way we did 20 years ago.” And although she advocates for Agile, she’s not an Agile coach or […]
Defining Principles, Elements, and Roles and Responsibilities in a Data Governance Policy
In previous blog posts, we defined the purpose, scope, and objectives of a Data Governance policy. In this blog post, we will complete the remaining sections needed for an effective Data Governance policy.
The Future of Data Governance: Balancing Data Governance and Data Management
“How do you create a competitive advantage for business partners by delivering fast access to high quality data, instilling confidence and supporting data driven decision making?” asked Ursula Cottone, Chief Data Officer (CDO) of Citizens Bank. As the keynote speaker at the DATAVERSITY® Enterprise Data Governance Online Conference (EDGO), she tackled this question in her […]
Data Governance Operationalization: The Team
Click to learn more about author Jayakumar Rajaretnam. This is the second part of a two-part series on the operationalization of Data Governance. The first part of the series “Data Governance Operationalization: The Gap” discussed how the need arose, some of the main reasons needed for Data Governance operationalization, and what is required. This part takes […]
Data Ownership: Overcoming Challenges and Moving Forward
Click to learn more about author Tejasvi Addagada. This is the second part of a two-part series on Data Ownership. The first part was – Data Ownership: Leadership, Challenges, and Data Governance Data Owners Work Only Part Time Data owners can be anyone from a manager running operations, a system owner, a project manager or a […]
Starting a Data Governance Program: What Does it Take?
Bob Seiner thinks your Data Governance definition needs to have “some teeth behind it.” According to Seiner’s definition, Data Governance is: “The execution and enforcement of authority over the management of data and data-related assets.” He prefers strong words like “execution,” and “enforcement of authority,” to make it clear that the Data Governance program is […]