An important component of data governance (also known as data intelligence) is data quality. But there are many parts to data quality including sources of data quality, issues, their impact, people roles, content and processes. In this blog post we will cover the various parts. We’ll also mention the resources (blog posts, videos, recorded webinars, etc.) we have developed regarding data quality.
Sources and Impacts – Poor data quality impacts many areas at a higher education institution including productivity, reputation, decision making and communication. Sources of data quality issues include data entry errors, mismatches between systems, lack of data validation and data not conforming to business rules. If you can’t get traction to establish a formal data quality program, you might use impact information as reasons at your higher education institution.
Additional Resources:
Blog Post: Sources and Impact of Data Quality Problems
Blog Post: Reframing Data Quality Problems
Video: Sources of Data Quality Problems
Video: Is There Really a Data Quality Issue
Video: Types of Data Quality Issues
Video: 5 Elements of a Data Quality Initiative
People – Data quality is the job of everyone at an institution especially anyone who touches or reviews data. Each role must work independently and in collaboration to improve data quality. Communication is important to the success of a data quality program. To build trust in data, internal communication regarding data quality should be as transparent as possible. If there are problems in a certain area, the staff should be informed. The communication should keep data quality as top of mind for the staff.
Additional Resources
Blog Post: Internal Communication Regarding Data Quality
Blog Post: Data Quality Responsibilities at All Levels
Processes (Assessment, Rules, Issue & Resolution) – The three key processes for data quality are: data quality rules, data quality assessment using the rules, and data quality issue resolution. A data quality rule is a tool for codifying the accuracy and completeness of a data attribute. And you must have a catalog to organize the rules. The most pro-active component of a data quality program is to measure or assess data against quality rules. Routine assessment can uncover hidden gaps or inaccuracies before a user discovers them. Most assessment tasks can be automated and scheduled. A data quality issue is a real or perceived inaccuracy discovered anywhere in the data environment. Critical in the managing of quality issues is the ability to funnel issues from multiple communication channels into one issue tracking system.
Additional Resources
Blog Post: Data Quality Programs
Blog Post: Data Quality Rules
Blog Post: Assess Data Quality Within Data Systems
Blog Post: Data Quality Issues
Blog Post: The Steps of a Data Quality Issue Resolution
Video: Types of Data Quality Rules
Video: Data Quality Process
Video: Turning Data Quality Issues into Rules and Assessments
Recorded Webinar: Documenting and Monitoring Your Data Quality Rules
Content – Data quality has a variety of content that is beneficial to the organization including quality issue resolutions, quality assessments and quality rules. This content should be accessible to those that need them.
Additional Resources
Blog Post: Data Quality Content
Action Items and Thoughts – There are many actions or tasks that can be done to improve data quality. Select a few items and consistently practice them. Slowly add more items to your list. Soon your small data quality program will grow into an impressively large one! Actions should include ones on getting started, determining sources of data errors, people responsibilities, internal communication, data quality rules, data assessments and issue resolution. While working with schools we have come up with several thoughts that we would like to share on improving data quality. Start any project such as improving data quality with planning and in most cases that should include a brainstorming session where you get stakeholders together. The goal of these sessions is to create a plan and action items on how to improve data quality.
Additional Resources
Blog Post: Data Quality Action Items - Part 1
Blog Post: Data Quality Action Items - Part 2
Blog Post: Thoughts on Data Quality
Blog Post: Brainstorming about Data Quality
Blog Post: Some Thoughts on Data Literacy and Data Quality
Blog Post: Preceptions of Data Quality Issues
Recorded Webinar: Pragmatic Management of Data Quality
Recorded Webinar: Why Having a Data Quality Issue Reporting Process is So Valuable
Recorded Webinar: Data Quality Best Practices (30 minutes)
Recorded Webinar: Demonstration of the Data Cookbook Data Quality Features
Recorded Webinar: Managing a Pragmatic Data Quality Program
Data quality is a key part of data governance and important to any organization. This post provided resources and covered the processes and roles that constitute a data quality program for organizations of any type, size and structure. Three critical key processes are necessary for enhanced data quality: rules, assessments, and submission/resolution of quality issues. And we covered the importance of data quality internal communication and data quality actions that will aid in improving data quality. Your data quality efforts will benefit from the information in this blog post.
Link to Data Governance Resources page for additional resources. As of September, 2024, we will be adding new content regarding data quality to our Spotlight on Data Quality page.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
(image credit DQcover-image-2_EB #1101)
