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Agile Data Governance Month – 5 Questions to Find Your First Use Case

The biggest obstacle to establishing a Data Governance posture can be scale. Metaphors about boiling saltwater aside, most organizations on a Data Governance journey will find the most success by starting with smaller use cases and scaling out. We call this Agile Data Governance

Can Agile Data Governance work for you? Ask yourself these challenger questions to identify Agile Data Governance opportunities in your organization.  

Are there issues or bottlenecks in your current data management process that could be alleviated through improved Data Governance? 

As Part One of this series outlines, one of the primary functions of Data Governance is accessibility – or making data available to the right user at the right time.   

Identifying common issues and bottlenecks can pinpoint immediate opportunities for accessibility improvements and produce quick wins. These issues could include inconsistent data definitions, data quality problems, lack of data ownership or accountability, or problems with data access and security problems.  

To get started, you should audit your existing infrastructure to determine where data is stored. Taking data out of siloes allows you to create domain-specific data marts – promoting usability and supporting domain-specific data products with a governed data federation. Data accessibility initiatives can provide immediate ROI – the cost-savings associated with replacing brittle ETL systems often pay for modernization tools. Eliminating multiple data warehouses also improves security and control functions.   

Is there a common terminology or data dictionary across the organization? 

A lack of common understanding of data can lead to inconsistency and misinterpretation. Establishing a business glossary that provides a common language and interpretation of data across the organization is an easy first use case and will introduce Data Governance to stakeholders throughout your organization. While tools exist to automate and manage data dictionaries, the first iterations of your data dictionary can take place in Excel. Most importantly, you will want to document the following:  

  • Identify your Data: Understand your data and identify all the data elements within your organization, spanning across different databases and systems. 
  • Classify your Data: Group related data elements into categories. This might include grouping by data type (e.g., numeric, text), by business function (e.g., sales, marketing), by sensitivity (e.g., public, confidential), or by any other characteristic that is meaningful for your organization. 
  • Define your Data: Provide a clear, concise definition for each data element. This should be in plain language that is easy for anyone in the organization to understand. Include any business rules or constraints that apply to the data. 
  • Identify Owners and Stewards: Every data element should have a designated owner and steward responsible for the data’s quality and use. Who are the organization’s data stewards, and how effectively are they managing data? Establishing clear data stewardship roles can be a use case in itself.  
  • Develop a Standard Format: Your data dictionary should have a consistent format that is used for all data elements. This might include fields for data name, definition, data type, owner, steward, related data elements, etc. 

Is there any department that has started on its Data Governance journey? 

Maybe you don’t have to reinvent the wheel! The concept of Data Governance has been a hot topic since the early 2010s, thanks to innovations in big data management, increased regulatory pressures, and exponentially growing data volumes.  Especially in larger organizations, there might be business lines that have started Data Governance initiatives you can build from. If you have a successful Data Governance initiative underway, even if it is one department, document a use case to expand on their work, learn from their experiences, and apply it to other areas. For example, if your finance department has created its own data catalog, the rest of the organization could immediately benefit. 

Are compliance requirements challenging to meet due to data quality or management issues? 

Regulatory compliance can be one of the biggest drivers of Data Governance initiatives. If you are in a regulatory-intensive industry, such as banking or healthcare, you might want to identify a use case around improving Data Governance to meet compliance requirements. For example, if your organization needs banking regulatory compliance, improving Data Governance could help ensure that all personal data is appropriately identified, secured, and managed. First steps in a Data Governance use case for compliance include: 

  • Data Classification: Classify data based on sensitivity to ensure the right security controls are applied to the right data. 
  • Data Quality Management: Poor data quality can lead to non-compliance. Good Data Governance helps ensure data accuracy and integrity. 
  • Establish Data Policies: Create clear, organization-wide policies for how data is handled, stored, and deleted to support compliance with data privacy regulations. 
  • Access Controls and Auditing: Implement strong access controls and keep audit trails to ensure only authorized individuals can access sensitive data, which many regulations require. 
  • Eliminate Manual Processes: Manual data processes in regulatory reporting can be inefficient and error-prone. Automated reporting will decrease the burden of regulatory reporting and let your data team focus on activities that add value to your organization.  

Are you maximizing the use of your metadata? 

A Data Governance use case could involve implementing a metadata management solution if current management is poor or non-existent. This will help users understand where data comes from, how it changes over time, and how it is connected to other data. 

With metadata management you can: 

  • Understand Your Data: Metadata, or “data about data,” provides essential context for understanding what data represents, its purpose, source, format, and relationship to other data. 
  • Improve Data Quality: Metadata can track when data was last updated, by whom, and if any validation rules were applied, which can help assess its accuracy and reliability. 
  • Track Data Lineage: Metadata helps trace the journey of data from source to current form. Observable data lineage is crucial for troubleshooting, impact analysis, and ensuring the integrity of data over time. 
  • Maintain Security and Compliance: Metadata can provide information about data sensitivity, helping to enforce appropriate security measures and ensuring regulatory compliance. For instance, metadata supports privacy regulations by identifying personally identifiable information (PII). 
  • Foster Accessibility: Metadata makes data more easily searchable and accessible, ensuring the right data can be found and used when needed. 
  • Promote Integration: By providing context and standardizing descriptions, metadata helps integrate data from different sources, making it easier to share, collaborate, and gain insights across various data sets. 

With a first use case in mind, you will be ready to launch your Agile Data Governance initiative. In our final installment of the four-part series, we will examine tools that promote collaboration and automation throughout the data lifecycle. Or, download your complete Agile Data Governance Guide here.  

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