We have done several webinars on best practices related to data governance and data intelligence. We thought we would briefly recap these best practices regarding business glossary, data system inventory, data stewardship, data processing catalog, data requests, data lineage, data quality, reference data, and data policy attributes as well as some on management and oversight of data governance at an organization. A 90-minute recorded webinar recapping the best practices for data governance can be accessed here.
General Best Practices
- Have a clear understanding of the goals of and outcomes desired from data governance
- Do a data governance / data intelligence roadmap that includes high-level summary information and key goals
- Give users access to the information they need in a knowledge base so that data is used appropriately and effectively
- Give users points of entry to request information or report problems
- New knowledge about your data is created with each request. Build-in to your processes ways to capture this data intelligence as it is created
- Continue with on-going training and buy-in activities regarding data governance
- Execute a communication plan regarding data governance so that others in the organization knows the status of the efforts
- Have a framework in place including data stewardship
- Have a data governance solution in place like the Data Cookbook
- Remember that data governance is about helping people and you should think in terms of customer service
Business Glossary
- Create functional data dictionary that creates consistency and manages definitions agnostic to data sources (links to technical definitions)
- Business glossary naming should be as specific as possible and be data system agnostic
- Favor common names where applicable
- Make sure that the business glossary is easy to find
- Use synonyms to help with the searching of the business glossary
- Name must be unique across all business glossary names and include relevant context and/or source
- Manage definition collision with context – might need a new definition instead of making a definition fit for different cases
- Keep the business glossary simple. Remember that sometimes content creation is a priority and sometimes approval is critical
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Data System Inventory and Data Models
- It is OK to simplify things by splitting documentation of a software application in to more than one data system
- Gradually increase information about a data system over time after starting with basic information
- Import data system data models where possible
- It is good to broaden access to metadata. Knowing about the existence of data is not the same as seeing data
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Data Processing Catalog
- Have different templates for different data processing types and have examples to assist in filling out the templates
- Provide a simple request entry and process that involves data stewards and subject matter experts
- Start with curating mission-critical processes or processes that are being created or changed, or when there are questions regarding the specific processes
- Make sure that information from the data processing catalog is available in the actual reports
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Data Stewardship
- Create a description of what a data steward does
- Do a matrix and assign data stewards to specific areas
- If no one assigned to a particular area then put in the data governance oversight team as the data steward
- Support direct and immediate involvement of necessary data stewards, experts, and existing content as part of your data processes
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Data Requests
- Organically build data intelligence content with each data request
- Get the right people involved when needed with a data request process
- Put simple points of entry where people encounter the need
- Have a workflow in place so that the correct data steward is informed of the request
- Document the resolution of the data request and have it available for future reference
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Data Lineage
- Data Processing creates lineage and to fully understand lineage, you need to look at the mappings and the overall process’ functional and technical details
- Realize that managing lineage is hard
- Understand your goals. Track what serves those goals
- Easiest way to document lineage is to capture it as it is created. Have developers document processes as they build them
- Look at your different data processing tools and see what native features (or third-party tools) that extracts lineage information into a readable format
- If you cannot document the lineage then at least point to it, such as where the code for it exists
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Data Quality
- Identify data quality rules and make new ones when new data quality issues are reported, and no rule is in place
- Have a known data quality issue reporting process in place
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Reference Data
- Prioritize and determine what reference data to document as well as where it lives
- Request new codes or changes to a list through data governance channels so that the change can be reviewed for impact
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Data Policy Attributes
- Create a list of data-related policies that your organization needs
- Assign policy attributes to policy content
- Execute and enforce policies based on attributes
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Management and Oversight
- Define data governance roles
- Define getting started and planning responsibilities
- Define implementing and training responsibilities
- Define ongoing support and management responsibilities
- Define data governance expansion responsibilities
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We hope you found these best practices beneficial. If you would like additional information, then please refer to the mentioned recorded webinars.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship 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.
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