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Data quality before data governance

Thoughts from DGVision

This year, Dataversity brought its data governance conference, DG Vision, to the nation’s capital. Focused on the needs of government agencies, the conference empowered federal employees with new insights, best practices, and education around starting and optimizing data governance initiatives. We joined business leaders in both private and public sector companies for meaningful conversations about all things data governance.

Many who were in attendance were starting to build out data governance practices within their organization and were seeking advice on planning for their governance programs, finding who in their organization should be responsible for program stewardship, and how to go about building a data governance business case for their senior leader buy-in. What most agencies are seemingly missing is the fact that a successful data governance program requires a strong foundation of data quality. Two main themes emerged through our discussions.

The first theme was the issue surrounding executive buy-in. Many attendees expressed concerns about how to convince the business, and more specifically, senior leaders to invest in a data governance program. This became very apparent during Erin Haselkorn’s, our head of market research, presentation on how to build a foundation of trust and data governance where she talked through our data quality maturity curve. Attendees were interested to know where they currently stand on the curve based on the work that has already been done in their program, and secondly, how to better plan to move up on the curve and incorporate more people, better processes, and improved technology. Consequently, this led to a discussion about what the added business benefits are from moving their organization up the curve and how to communicate these returns to get senior leadership to approve their data governance initiative.

In order to pitch your data governance initiative, you need to first pitch data quality. You need to map out what the benefits of data quality are to the business and how it can be utilized for organizational success in the future. There is a need to take a step back and think about how these initiatives and proper talent can address and benefit specific issues that are ongoing within the business. Highlighting those issues and how they could be resolved with your program is the key to executive buy-in.

The second theme encircled the difficulty many organizations are having implementing the proper data quality tools to get their data governance program on track. A good number of companies we spoke to have begun to implement data quality practices to some capacity, however, they have had significant difficulty making the roll-out effective and scalable relative to their data governance ecosystem.

Basically, this came down to simple data quality tasks performed within Excel, SQL, and other comparable tools. This method was critiqued by many due to the very manual nature of working within these tools, which leads to a limited quantity of meaningful data quality work being completed, in addition to this work being siloed to staff with advanced technical skills. In an attempt to scale these efforts, specialized staff spend a significant amount of time teaching the data quality skills and methodology to stewards in other business areas, which takes much time and degrades the quality of output.

On a more advanced end, some organizations procure full-scale data quality tools to support their data governance efforts. While this approach is trending toward a higher level of data quality maturity, attendees explained their first-hand experiences where enterprise technologies were purchased but poorly implemented. Data stewards were not being trained sufficiently on the vast capabilities of the tools or the methodology for how to use the tool against key business cases. Examples included finding that key data sources are not compatible with these tools. These problems led to their organizations re-baselining their data quality strategy and going back to antiquated methodologies to perform data quality tasks.

It is vital to enable and empower all business users with the power of your data to ensure that the data is being used properly and that the tools you are acquiring are the right choice for your business. In order to have a successful data governance program, ensuring you first have quality data is the first step.

Experian’s data quality solutions can benefit your data governance program both immediately and in the long-term. Experian’s platform and services methodology focuses on ease-of-use for business users, and we can help you start your governance program and see it through with continuous training, mentorship, and partnership.

If you’re looking to begin or improve your data quality strategy for your data governance program, click the link below to see how we can work together to help you achieve organizational success.

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