How data governance and data quality work together
Data quality is an important pillar in the data governance framework and plays a vital role in an organization’s ability to meet established governance standards.
While both exist as individual models, effective implementation of data quality and data governance structures has the potential to produce a symbiotic system that ultimately upholds an organization’s strategic goals and informs decision-making.
It is first important to understand data governance and data quality as distinct concepts. Data governance refers to the oversight of an organization’s information. It is a process that delineates owners who have rights to view and utilize information. It standardizes how this information is collected, stored and ultimately analyzed or disseminated for a specific use. Data governance is somewhat of an umbrella term that encompasses several components. These components are typically formalized in a data management plan or data standard. Michel Girard, Senior Fellow at the Centre for International Governance Innovation, stated in his 2020 research paper Helping Organizations Master Data Governance: An ideal governance standard should 1. include objectives the organization is pursuing, 2. identify the scope of data covered by the data governance standard, 3. designate a position that is accountable for the application of the data governance standard, 4. bring clarity on data ownership rights, 5. articulate how the data collection should be handled, [and] 6. describe how relevant data sets and data streams should be accessed and shared. Girard also highlighted other areas — such as compliance, privacy and security — as pieces of the governance puzzle, demonstrating the extensive nature of a data governance system. In its simplest form, data governance can be surmised as strategic and intentional management of information. To establish data quality, there should be standardized practices to monitor data integrity and bring forth inconsistencies or inaccuracies in the data being processed. A comprehensive data quality system outlines clear pathways for troubleshooting areas of concern, implementing improvements and establishing continuous monitoring. The end user of the information also affects what is considered quality data. If the information being furnished is not useable, regardless of the validity and completeness, the data could be considered poor quality because it does not meet the end user’s needs. The principal difference between data governance and data quality is that data governance provides oversight and management of an organization’s information, whereas data quality is focused on the integrity and value of the information itself. These two forces possess their own intricacies, but organizations have an opportunity to capitalize on their multifaceted nature to build complementary data quality and data governance structures that help meet organizational goals.
Compliance is one of the areas in which data governance and data quality intersect. Take the healthcare or education sectors as examples: Both have regulations that delineate rights to information and how that data can be accessed and shared.
