High-quality data is a fuel that accelerates highly effective decision-making. Under the modern data governance journey, enterprises must revisit their stewardship processes regularly in order to keep data accurate, consistent, and relevant in all decisioning contexts. At the same time, organizations must make sure that data governance practices protect privacy, are free of bias, and enforce stringent controls in response to new legal, regulatory, and competitive imperatives. Join TDWI’s senior research director James Kobielus along with Precisely’s Michael Sisolak and Susan Pawlak on this webinar to explore the challenges and benefits of implementing robust data governance in today's cloud-centric business environments. They discuss key steps in the implementation of governance practices that contribute to high-quality decision-making, including: • Assessing your enterprise’s data governance maturity and integrity level; • Aligning data governance with key business priorities, outcomes, and metrics; • Producing improved business outcomes based on trusted data • Instituting data literacy training with a strong self-service data governance focus; • Implementing data-driven automation, continual monitoring, and self-service augmentation of all data-governance functions • Automating the application of predefined data governance policies to ensure compliance with new regulatory mandates • Must-have capabilities of an operational data governance solution • The integral relationship between data governance and data quality

1. James Kobielus
Senior Research Director, Data Management
TDWI
April 28, 2022
Why Integrated Data Governance and Data
Quality is Critical to Business Success

2. SPONSOR
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3. JAMES KOBIELUS
Senior Research Director,
Data Management
TDWI

4. Keeping data analytics fit for purpose requires governance
Source: TDWI 2021 survey. Based on 113 respondents

5. Detecting poorly governed data analytics practices
Source: TDWI 2021 survey. Based on 129 respondents
These deficiencies are as much due
to a poorly governed organizational
culture surrounding consumption of
data and analytics as in shoddy
governance of these business assets.

6. Encouraging high-quality data-driven decisioning
requires a mature business culture that instills
governance, integrity, and accountability at every level.

7. Pillars of
mature data
governance
• Culture: aligning data analytics practices with business
priorities
• Competency: universal training in data literacy and
governance
• Motivation: employee incentives for data analytics best
practices
• Sponsorship: executive-level buy-in to need for strong data
analytics governance
• Breadth: cross-domain governance of data analytics usage,
development, and management
• Collaboration: team-based governance of data analytics
• Responsibility: practical framework for data analytics ethics,
accountability, and transparency

8. Culture: aligning data analytics with business priorities
Source: TDWI Data & Analytics Survey 2022
Governance thrives
on conducive data-
driven business
culture.
From an organizational perspective, what could be improved in
your BI & analytics efforts to make them more successful?

9. Competency: training everybody in data literacy
What are your company's biggest priorities for analytics in 2022?
Source: TDWI Data & Analytics Survey 2022
Governance is less of a
challenge if all personnel
are trained and
empowered in self-
service data analytics.

10. Motivation: incentivizing data analytics best practices
Is your organization able to measure success related to its analytics efforts?
Source: TDWI Data & Analytics Survey 2022
Tying
incentives to BI
& analytics
success metrics
can be
transformative.

11. Sponsorship: top-down data analytics governance
Based on your organization's experience with analytics, what
do you believe are the top three organizational strategies for
success?
Source: TDWI Data & Analytics Survey 2022
Executive buy-in and top-
down sponsorship of
data analytics
governance will put it on
the fast track.

12. Breadth: cross-LOB data analytics governance
What is the most important area for organizational improvement for data
management?
Source: TDWI Data & Analytics Survey 2022
Standard governance
processes across the
enterprise ensure
consistency in how data
analytics are built,
managed, and
consumed.

13. Collaboration: team-based governance of data analytics
What best practices has your organization found beneficial for
data governance?
Source: TDWI Data & Analytics Survey 2022
Cross-functional
teams with shared
tooling give teeth to
data analytics
governance .

14. Responsibility: safeguarding data ethics accountability
From a technical perspective, what could be improved
in your company's analytics effort to make it more
successful?
Source: TDWI Data & Analytics Survey 2022
Governance requires strong
pipeline controls to ensure that
data analytics comply with ethical
standards, maintain algorithmic
accountability, and are always
compliant with relevant policies,
regulations, and laws.

15. Recommendation
• Assess the maturity of data analytics governance
practices
 Can your organization ensure ready availability of correct,
current, clean, and actionable data for all uses?
 Can you prevent occurrence of rogue data sets, untagged
data assets, and other governance deficiencies?
 Can you respond fully to governance mandates in privacy
protection, anti-bias, and other areas?

16. Recommendation
• Encourage strong data analytics governance culture
• Do all technical and business roles place high priority on maintaining strong
governance over data analytics practices, platforms, and assets?
• Do you have a central data analytics governance coordinating body that
includes representatives from all business units and functions?
• Does your organization have an ongoing campaign that educates, and
incentivizes all employees to observe company-wide data analytics
governance practices?
• Do you provide clear, usable data analytics governance guidance is
distributed regularly to all personnel?

17. Recommendation
• Strengthen data analytics governance processes
• Does your organization provide self-service tooling that enforces
data analytics governance policies?
• Do you proactively monitor and remediate data analytics quality,
bias, privacy, and other deficiencies before they become
showstoppers?
• Do you automate predefined policies that ensure nonstop
compliance with all data analytics governance and compliance
mandates?

18. THANK YOU!
James Kobielus
Senior Research Director, Data Management
TDWI (www.tdwi.org)
jkobielus@tdwi.org
@jameskobielus

19. Sue Pawlak
Senior Product
Marketing Manager
Precisely
Michael Sisolak
Senior Sales Engineer
Precisely

20. QUESTIONS?
tdwi.org

21. CONTACT INFORMATION
If you have further questions or comments:
James Kobielus, TDWI Michael Sisolak
jkobielus@tdwi.org michael.sisolak@precisely.com
Sue Pawlak
sue.pawlak@precisely.com
tdwi.org

22. Thanks to Our Sponsor
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