This document summarizes the key findings of a survey of over 800 data and analytics professionals regarding trends in data governance and data quality. The survey found that improving data quality was the top goal for organizations' data programs and the leading benefit of data governance programs. However, 39% of organizations do not measure data quality enterprise-wide. The survey also found that more mature data governance programs tend to invest more in training and see higher added value from their programs in terms of data quality and other benefits. Common obstacles for effective data governance included cost, lack of understanding of the right approach, and lack of tools and organizational support.

1. Data Governance and
Data Quality Trends to
Inspire Action
Survey of 800+ Data and
Analytics Professionals

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2

3. Today’s speakers
Emily
Washington
Senior Vice President,
Product Management, Precisely
Murugan
Anandarajan, PhD
Professor and Academic Director,
Center for Business Analytics at LeBow
Susan
Pawlak
Senior Product Marketing Manager,
Precisely

4. 4

5. Methodology &
Demographics
5
• Companies over $500M+ revenue
• Titles
• Data driven industries
• Questions about data management
programs
• Subset of questions for companies
with data governance programs

6. 6
75% say Data Quality is the
top goal for their data programs
39% say their organizations
do not measure data quality
enterprise-wide
Data Quality is the Goal
What are your organization’s
goals for data programs?

7. Leading Benefit of Data Governance is Data Quality
KEY FINDING
Improving data quality is the leading benefit organizations
receive from their data governance programs, an added
value that contributes to a range of critical business benefits.
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Improved data quality is the
leading benefit derived from
data governance programs.
How has your data governance
program added value to the
organization?
(n=449)
56%
Higher quality of data analytics and insights
52%
Facilitated collaboration
50%
Faster access to relevant data
49%
Increased regulatory compliance

8. Key Findings
Conclusion
8
Data Governance
• For data to be used to derive value, it
needs to be trusted, which relates
directly to data quality - a key
component of data integrity.
• Data governance enables businesses
to understand and trust their data for
confident decision-making.

9. Data Governance
& Data Quality

10. Understanding Data Governance
10
Which components are included in your organization’s definition of data governance? (n=449)

11. Data Governance Leadership
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12. Data Governance
Structure
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• Dataversity refers to whether or not
a data governance program is
centralized, decentralized, or hybrid
• Organizations with decentralized
DG structures report a lower value
add than those with centralized or
hybrid structures
34%
13%
46%
7%
(n=449)

13. Organizational
Inspiration

14. For the purposes of this survey,
maturity stages for data governance
were defined as:
• Performed as part of
projects, with little consistency across
projects
• Performed consistently
across projects within a business unit
or line of business
• Performed consistently
across the enterprise using
established formalisms and tools
• Metrics and dashboards
are used to communicate and ensure
consistency across the enterprise
• A continuous
improvement program is in place
Data Governance Maturity
Trends in Data Governance and Data Quality
14

15. As Data Governance Maturity Evolves,
So Does Data Quality
How has your data governance program added value to the organization? (n=449)

16. Mature Programs Invest in Training
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17. Obstacles to Address
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46%
Cost
45%
Understanding the right organization approach
41%
Effective management tools
39%
Organizational support
28%
Justification/business case
18%
Executive support
What are the biggest obstacles facing your data governance program? (n=449)
63%
Say cultural
awareness and
adoption is the
biggest obstacle
their data
governance
programs face

18. Conclusions
• Quality data is crucial for business leaders
to make confident and reliable business
decisions.
• Data governance is a key factor in how
organizations achieve the quality of data
that leads to that trust.
•
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19. Questions?

20. Our software, data enrichment products, and strategic
services deliver accuracy, consistency, and context in
your data, powering confident business decisions.
Brands you trust, trust us
20
Engaging a community of industry partners, faculty, and
students, LeBow's Center for Business Analytics convenes
the expertise of researchers, practitioners while preparing
the next generation of data-driven leaders.
Analytics Collaborations
• Student Data Analysis Projects
• Analytics Student Talent
• Industry Thought Leadership
• Data-driven competitions
• Collaborative Publishing

21. Download the full report
https://www.precisely.com/resource-center/analystreports/data-
professionals-speak-trends-in-data-governance-and-data-quality
More Information:
www.lebow.Drexel.edu
www.precisely.com

22. Thank you