The Role of Data Governance in Ensuring Data Quality Throughout the Life Cycle

Data governance (DG) is everything we do to ensure data is secure, private, accurate, available, and usable. It is the process that modern organizations – governments, companies, schools – use to manage the availability, usability, integrity, and security of their data and data enterprise systems. This is typically based on internal data standards and policies controlling data usage and external compliance mandates. Effective data governance must ensure data is consistent and correct. But more importantly, it is vital to ensure that data is available to use for business value without being misused.

The Data Governance Institute defines DG as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

The Data Management Association (DAMA) International’s definition is “planning, oversight, and control over management of data and the use of data and data-related sources.”

Why Does DG Matter?

If we don’t govern the integrity of our data, inconsistencies from system to system across an organization might be left unresolved. The names of customers listed in sales might vary from logistics and customer service systems. This is just one example of how data integrity can complicate integration and cause issues that affect the accuracy of business intelligence (BI), corporate reporting, and analytics applications. Do you see how this effect can be magnified to business detriment? Ultimately, data that cannot be readily accessed for use is useless towards business value. DG must be seen as integral to data access and business value.

How to Ensure DG Accuracy?

Data Governance Team: A functional and detailed data governance program should include a governance team. The steering committee or team can comprise of teams of teams that include the data steward roles. They work to create the standards and policies for governing data and implementation and enforcement procedures. Executives have final decision rights through the steering committee routines, and assignees from a business take part, in addition to the IT teams. The extended teams should be made up of participants from various departments, including IT, data management, legal, compliance, and perhaps company stakeholders.

The Framework

Data governance supports an organization’s overarching data management strategy. Such a framework gives your organization a comprehensive way to collect, manage, secure, and store data.

• Data architecture: The structure of how data and resources are arranged as a core part of the company
• Data design: It is best to start this using a value stream perspective. Think of the user experience you want to build for. Look at the systems by which data is analyzed, built, and designed for the business and how these will be maintained.
• Data storage: What will deployment assets for managing physical data look like?

• Data security: It’s vital to ensure privacy and restrict access to a select few.
• Data interpretation: This looks at how data is gathered, transformed, moved, delivered, replicated, extracted, and the operations in place to support DM.

• Documentation: Gathered data must have proper storage, security, and access.
• Reference: This is how data is managed to reduce redundancy. It also produces better-standardized data quality.
• Data business intelligence (BI): Data must be managed in a way that is useful and can enable access for reporting and analysis

Data Lifecycle

A data lifecycle is the stages one data unit goes through, from creation to disposal, when it is no longer helpful.

• Acquire: Data is collected by an organization.
• Store: Data is stored securely but with accessibility for management and analysis.
• Aggregate: Company data in distinct datasets are combined through an aggregate to create a larger dataset.
• Analyze: Data is inspected, and the information is used to generate insights.
• Use: Data insights are used for decision-making to affect change and deliver products or services.
• Share: Those entrusted with the care of the data can provide access to datasets or data insights as appropriate.
• Dispose: Based on set retention schedules, company data is removed from all systems, preventing further use.

Data governance is selecting and using technology and tools to support data quality. Maintaining high data quality becomes a challenge without a strong data governance program. With high-quality data, the insights and decisions derived from that data can improve. Initiatives in developing a data governance plan are crucial for ensuring data quality in your organization.