Nam Tran | December 19, 2017

5 Key Tips to Help Identify a Starting Point for Data Governance

Data is widely accepted as one of the most valuable assets in an organization.  Understanding how data is used, preserving data integrity, and maintaining consistency in usage are crucial to the business.  Data governance serves as a vital function within an organization by defining guidelines for metadata management, driving processes for data issue resolution, and actively measuring data quality improvement over time.  An effective data governance program enables business users to make decisions based on transparent and trustworthy data.

Starting a data governance program in a single line of business isn’t easy, let alone across an enterprise.  Organizations often struggle to initiate a program due to lack of time, lack of sponsorship, or competing budget priorities.  One particularly difficult challenge can be the creation of a business case with quantifiable, “hard dollar” benefits.  Unless faced with regulatory compliance requirements, the major benefits are often confidence in the data because of common definitions, the ability to track data usage, and the ability to trust the quality.

If you find yourself struggling to initiate a program, or build a business case, or determine if a data governance program is right for you, consider the scenarios below:

  1. Centralize and Cleanse Data

If your organization is trying to centralize all their data by building an enterprise data warehouse, a data lake, data hub, enterprise service bus, data transformation layer, or a data mart, then you should start a data governance program in parallel.  During an enterprise data warehouse initiative or the like, the organization spends an exorbitant amount 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.  Typically, the initiative concludes with a rich array of metadata and governance requirements that quickly grow stale because they are not tracked and governed simultaneously. As a result, a query from a business user about the source of a field in the enterprise data warehouse requires IT to manually trace the element through layers of logic.  This same information, the metadata, can be captured and curated while the enterprise data warehouse is under construction.

  1. Address Regulatory Mandates

Many organizations are just beginning to come to grips with personal data capture and use.  New legislation, such as the General Data Protection Regulation (GDPR), requires a sophisticated level of monitoring and policing for data.  With the GDPR deadline fast approaching, organizations need to identify Personally Identifiable Information (PII) and track where it resides, who has access, where it is sent, and so much more.  Similarly, the deadline for the Markets in Financial Instruments Directive (MIFID) II’s transaction reporting obligations is less than a month away.  Organizations need to understand what they must report, who owns the responsibility for the reports, and where they can find the information.  Satisfying regulatory mandates is an opportune time to leverage data governance and the business case is clear.

  1. Inefficient Approach to Fixing Data Quality

If your organization’s data management team spends more time fixing data issues than extracting analytics to improve data quality over time, a data governance program is needed.  The goal of an effective data management team should be to identify trends in data issues and implement permanent solutions.  If constantly researching questions like “Where does this field come from?”, “Why is my data wrong?”, “Who should I contact for this issue?”, or “Does this field exist?”, then the data management team has little time to achieve their goal.  A data governance program can create the policies, common definitions, data lineage, and a shared glossary to provide answers for the users while reducing the number of data issues getting introduced into the environment.  The data governance program is how the data management team gets ahead of the data issue volume and systematically injects organization, consistency, and accountability into the organization.

  1. Explaining Metadata – One Person at a Time

When valuable time and resources are wasted because your IT team must constantly explain what they mean to the business team, or vice versa, it’s time to initiate a data governance program.  Building a business glossary allows an organization to be more productive during cross-team discussions.  By defining common terms across the organization, varying teams can communicate easier and analysis from differing organizations is based on the same understanding of the data.

  1. People as a Single Point of Failure

When your project or business processes slow down significantly because certain members of the team are out of the office, or worse, quit, then it’s a good time to explore how data governance can respond to this challenge.  If you panic at the thought of losing a team member because of the institutional knowledge that s/he possesses then that person is the single point of failure.  Getting that critical knowledge into a data governance tool can help ensure that others have access to information about where data originates, where it resides, and who has access.  Data governance can prevent valuable intellectual property from walking out the door.

So, when is the right time to initiate a data governance program?  It’s always a good time to get control of the data, get the enterprise using common definitions, and get data quality measurement underway.

To learn more about the right time to start a data governance program download this datasheet.

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