5 Challenges That Can be Solved with Data Governance
In an era of rapidly advancing technology, big data and advanced analytics, data governance serves a vital function within every organization, regardless of size or industry. From defining guidelines for metadata management, to driving processes for data issue resolution, to actively measuring data quality improvement over time, the importance of data governance cannot be understated. Today and in the future, organizations that lack a data governance process will not just fail to thrive, they may struggle to survive.
An effective data governance process enables business users to make decisions based on transparent and trustworthy data. It helps businesses understand not only what their data assets are and how to access them, but also how to use that data most effectively. It can also maximize and quantify the value of data, by preserving its quality, measuring and monetizing its worth, and maintaining consistency of usage across the entire enterprise.
Data governance serves a range of organizational needs, but it also solves a wide variety of organizational challenges. Let’s look at the issues where data governance can provide the biggest value.
The top five organizational obstacles overcome with data governance
For organizations struggling with the following challenges, a comprehensive data governance process will serve as a huge benefit.
1. Complying with regulatory mandates
Regulatory concerns such as GDPR BCBS 239, CCAR, Dodd-Frank, and MiFID all place an emphasis on how organizations use, report, and manage data. They all also require a sophisticated level of monitoring and policing of data. Organizations need to understand what they must report, who owns the responsibility for the reports, and where they can find the information. A thorough data governance model can help ensure regulatory compliance.
2. Ineffective data integrity assurance
Business users should be spending their time analyzing data to reveal meaningful business insights, not searching for the right information, or questioning whether data can be trusted. If data consumers are constantly researching questions such as, “Where does this field come from,” “Is this data accurate,” or “Who owns this data and can answer my questions?” then they have little time to achieve their actual business goals. A data governance framework can document the policies, common definitions, data lineage, catalog data and shared glossary to provide answers for data users while reducing the number of data integrity problems. Data governance provides business users with both data accessibility and understanding, which means they are more likely to utilize those data assets. From a data integrity perspective, data governance can also increase users’ trust in data, as a solid scoring, ongoing data quality monitoring, and to continuously improve data integrity over time.
Read our Whitepaper
A Roadmap for Data Governance
Learn more about how a comprehensive enterprise data governance process can help you solve organizational data quality challenges.
3. Business users misunderstanding data assets
As a consultant, I’ve heard the same story across many industries: a group of VPs are all presenting at a board meeting, and each one has different numbers. Soon a discussion (argument) ensues about which numbers are correct. If an organization is wasting valuable time and resources because the IT team must constantly explain the meaning and usage of data to the business team, data governance can help. Data dictionaries, business glossaries simple self-service tools, and the culture of collaboration nurtured by data governance can all improve understanding and appropriate usage across the enterprise. It can increase productivity and promote effective communication among and across teams, creating educated data consumers who know how to use data and know who to ask beyond just IT when they do have questions.
4. Centralizing data
If an organization is trying to centralize all their data by building an enterprise data warehouse, data lake, data hub, enterprise service bus, data transformation layer, or a data mart, then data governance is a must. During an enterprise data warehouse initiative, organizations spend tremendous amounts of time defining what the data means, where it comes from, and what kind of transformation it needs to go through before mapping it to the warehouse. If that data is not governed simultaneously, all the metadata involved will quickly grow stale. With data governance, that metadata can be captured and curated while the enterprise data warehouse is being built.
5. Individual employees are a single point of failure
When a project or business process stalls or stops because individual members of the team are on vacation, or even leave the company, then data governance is needed. If an organization can’t afford to lose a specific employee because of the institutional knowledge he/she possesses, then that person is a single point of failure. Documenting that critical knowledge through a data governance program can provide enterprise access to technical and institutional knowledge to keep the enterprise running smoothly and prevent the loss of vital, valuable intellectual property.
Of course, the potential use cases for data governance are innumerable, while its value is undeniable. Data challenges will arise in every organization. With a comprehensive data governance framework in place, organizations can proactively manage and mitigate data issues, and solve any problems before they impact the business.
To learn more about how a comprehensive enterprise data governance solution can help you solve the organizational data quality challenges, read our whitepaper A Roadmap for Data Governance.