A data quality program can be any size. Whether large or small, the key thing is to have a plan and to use processes that are predictable and repeatable. A haphazard or unpredictable approach will not inspire trust in your organization’s data. Data users want to know the scope and schedule of the data quality work practiced at their organization. Everyone understands that because resources are limited the level of quality may not be as high as they would prefer, however they find it helpful to rely on the quality work that is performed with the limited resources.
This list of actions or tasks provides starting points for a data quality program at your organization. Select a few items and consistently practice them. Slowly add more items to your list. Soon your small program will grow into an impressively large one! This post covers the following data quality actions around getting started, sources of data errors, people responsibilities and communication:
- Hold brainstorming meetings with staff to identify obvious data quality concerns. Prioritize the list in a subsequent meeting.
- Identify current data quality processes that could be improved and work on a few that can be improved easily.
- Inventory where data is initially created (or sourced) within a domain. Look for points where data is manually entered into a form or data system as well as data that is sourced from other systems. Trace and publish the lineage of data elements across data systems. This is called a Data Creation Inventory.
- Using your Data Creation Inventory, ensure that each data entry uses all available features that support quality. For example, if spell check is available during data entry to a specific field, is the spell check feature enabled? Can drop-down boxes of pre-defined values or codes replace text entry boxes? Is auto-create text turned on?
- Identify staff who can establish quality standards. As appropriate, confirm with each person that they understand their responsibilities. Periodically check in with them to make sure that they can fulfill those responsibilities.
- Record a short (3-5 minute) video that outlines the importance of data quality and highlights what your institution does to manage it. Post the video wherever other data governance materials are found. Use this video in employee training.
- Define data entry standards and incorporate them into training. Reinforce the standards and communicate the value of quality data with each data entry person on a regular basis. Make sure they know how to submit data quality issues.
- Ensure that data entry staff performance reviews include evaluation of the quality of work, not just the speed or quantity. If possible, offer incentives for quality performance.
- Create a central digital library of data quality resources for staff use. The library could include the components of quality standards, roles and resource allocation, definitions of “quality data”, communication deliverables for a quality program, and other topics. Formats may include articles, eBooks, videos, data quality rules, data quality assessments, issue resolutions, data quality program description, and a contact list.
- Create a document outlining a data quality program charter, including its purpose, activities, the parties involved, and benefits.
- Discuss, implement and document data quality communication through various methods. Are the methods a mix of push and pull communication types? Are there gaps in frequency or in the type of user who is targeted as recipient?
- Designate a data quality communications person.
- Create an information sheet on data quality and provide to Human Resources (HR) to distribute to new employees.
- Post and distribute an annual data quality report to all necessary staff.
Feel free to check out our other data quality resources in this blog post. If you need help in implementing data governance or data intelligence including data quality, remember that IData provides data governance services. A data governance solution like the Data Cookbook can help in successful implementation of data governance at a higher education institution and improving data quality. Feel free to 
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