It’s been almost two years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. This complex but critical practice still has most enterprises grappling to master it for a myriad of reasons. In this webinar, we’ll examine how Data Governance attitudes and practices continue to evolve and discuss what new research reveals as the most predominant challenges. We’ll delve into technology trends, including how adding certain capabilities will benefit your organization in terms of data asset availability, quality, and usability, including data consumer literacy and confidence. When you attend this webinar, you will learn about: • The requirements for a successful and sustainable Data Governance program • Increasing confidence in data analytics for faster speed to insights • How to automate data preparation and intelligence and where to start

1. Data Governance Reality Check
February 2020

2. Our Agenda
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Exploring the State
of Data Governance
& Automation
Getting on the
Right Path
Building a Solid
Foundation
Using Technology
to Hit the Mark

3. An explosion of data across disparate silos leads to …
The Enterprise Data Dilemma
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Sources: Network World IDC “The Digitization of the World From Edge to Core,” November 2018; Octopai.com, Gartner, September 2016
“Collective sum of the world’s data
will grow from 33 zettabytes this
year to 175 zettabytes by 2025.”
1,000s of undocumented applications + databases
“Big data presents large volumes of multi-
sourced data in varied formats and types.
Metadata management is mandatory.”
1,000s of business terms across different business units

4. 2020 State of Data Governance & Automation Report
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• Surveyed North American organizations
• Respondents in data or data architecture roles,
information/data governance professionals,
business intelligence/analysts, data scientists
and executive management
• Organizations in technology, government,
consulting, insurance, finance, healthcare and
manufacturing among others
• All sizes but majority 101 to 5,000 employees
(40%) then 10,001 to 50,000 (17%)
In partnership with

5. Why This Survey?
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Follow-up to our first study
and the subsequent report
released in 2018
We wanted to see if or how data
governance attitudes and practices have
evolved since GDPR went into effect.
We wanted to understand current
priorities, challenges and technology
usage, including the role of automation.

6. Key Drivers
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What are the top three drivers of your data governance/data
intelligence initiative? [Please select three choices]
In 2018, regulatory compliance was the
primary driver. Today, the priorities are:
Better decision-making (62%)
Analytics (51%)
Regulatory compliance (48%)
Digital transformation (37%)
Data standards/uniformity (36%)

7. Program Maturity
How mature is your
organization’s data
governance/data
intelligence program, or
what stage are you in?
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Work in progress (38%)
Just getting started (31%)
Planning stage (19%)
Fully implemented (12%)
These numbers are
lower than in 2018.
Important steps have been taken, but we suspect
finding the right approach is still a challenge.
Plus re-evaluation of what’s been done or partially
implemented in light of new needs and challenges.

8. Data Value Chain Bottlenecks
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What are the most serious bottlenecks in your
organization’s data value chain? [Select all that apply]
Documenting complete data lineage (62%)
Understanding the quality of source data (58%)
Finding, identifying and harvesting data (55%)
Curating data assets with business context (52%)

9. Data Prep, Governance & Intelligence Challenges
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What is the most significant challenge to your organization’s data
preparation/data governance/data intelligence efforts?
Length of project/delivery time (25%)
Data quality/accuracy (24%)
Time to value (16%)
Reliance on developers and other technical
resources (13%)

10. How many hours per week on average do you
spend on data-related activities?
Time Spent on Data Activities (Hours Per Week)
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70% of survey respondents
spend 10+ hours per week on
data-related activities.
Most of that time is spent on
data analysis.
That is, after searching for and
preparing data so it can be
analyzed.

11. Data Prep, Governance & Technology Deployments
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Have you deployed any data preparation/data governance/data
intelligence solutions?
If yes, what type? [Select all that apply]
Data Analytics (65%)
Metadata Management (59.48%)
Data Quality (58.82%)
Data Catalog (49%)
Business Glossary (49%)

12. Level of Automation
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To what level are
your data operations
automated?
Mildly Automated (41%)
Somewhat Automated (34%)
Totally Manual (20%)
Completely Automated (2.5%)

13. What’s Been Automated
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What specific data operations have you
automated? [Select all that apply]
Data Mapping (39%)
Data Cataloging (26%)
Data Lineage (25%)
Code Generation (21%)
Impact Analysis (12%)
Data Harvesting (48%)

14. Desired Automation
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What specific data operations would prove the
most valuable to automate, if they aren’t
already? [Select all that apply]
Impact Analysis (48%)
Data Mapping (53%)
Data Cataloging (61%)
Data Lineage (65%)
Data Harvesting (38%)
Code Generation (21%)

15. erwin’s Take on the State of DG & Automation
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Organizations are still
iterating on data governance,
trying to get it right.
Priorities are re-balancing
risk avoidance and
opportunity enablement.
Data discovery,
preparation, quality and
traceability are challenges.
Accurate, high-quality,
real-time data pipeline is
key for decision-making.
Automating data operations will create
sustainable and repeatable practices
to reduce errors, improve analytics and
increase speed to insights.

16. Recommendations
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Constantly re-evaluate and tune
your DG strategy and
deployment.
Put data quality first, and it
starts with metadata quality.
Data lineage is complex and
an absolute requirement; deal
with it.
Automated code generation is a
huge opportunity for efficiency,
agility and consistency in data
preparation.

17. Recommendations, continued
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Impact analysis is more than
“where used;” it drives data
valuation efforts.
Catalog your data assets:
you can’t govern what
you can’t catalog.
Promote data literacy: “An
educated consumer is your
best customer.”
Automation is always a good
option, but with some tasks, it is
the only option.

18. Data Intelligence: Solving the Enterprise Data Dilemma
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Harvest Data Organize Data Curate Data Administrate Data Socialize Data
Data
Consumers
Data
Management
Data
Governance

19. The erwin EDGE Platform
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The software that connects all
aspects of your business,
technology and data
architectures for the intelligence
to fuel results

20. D AT A L I T E R A C Y
Combine erwin Data Catalog with erwin
Data Literacy to fuel an automated, real-
time, high-quality data pipeline.
Give all stakeholders access to data
relevant to their roles and within a
business context.
Power decision-making based on a full
inventory of reliable information.
erwin Data Intelligence Suite
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Improve IT and business data literacy and knowledge, supporting enterprise
data governance and business enablement.
erwin Data Literacy Suite
Business
User Portal
Business Glossary
Manager
erwin Data Catalog Suite
Metadata Manager Mapping Manager
Reference Data
Manager
Lifecycle Manager
Data Intelligence Suite
Standard Data Connectors Smart Data Connectors
D AT A C AT A L O G I N G

21. Fuel Success Across the Board
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Improved
Decision-Making
Increased
Customer
Satisfaction
Compliance,
Privacy & Security
Operational
Efficiency
Revenue
Growth

22. Recognized Industry Leadership
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Industry Awards and Accoladeserwin Is a Leader in Metadata Management
(Gartner 2019)
CHALLENGERS LEADERS
NICHE PLAYERS VISIONARIES
AbilitytoExecute
Completeness of Vision As of October 2019
Informatica
Collibra
Smartlogic
ASG
Infogix Alation
Alex Solutions
IBM
Oracle
Adaptive
SAP
Syniti
Semantic Web Company
data.world
Global IDs
Data Advantage Group

23. The erwin EDGE platform is providing data intelligence in context by combining
enterprise architecture modelling with business process and data entity
modeling capabilities connected to a glossary that can support classification of
data using business ontology knowledge graphs, integrated with a data
dictionary and data catalog containing location, relationship, lineage, and motion
intelligence of enterprise data.
Stewart Bond, Principal Analyst IDC 2019
Why erwin?
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Most trusted name in
data, powering mission-
critical applications for
world’s largest
organizations
Significant R&D with
customer input to create
tools for digital
transformation
Only vendor providing
an EDGE to deliver an
aligned and
sustainable operating
framework
On-prem and hosted
delivery of federated,
integrated and adaptable
solutions
Superior support and
services with industry-
leading customer
satisfaction
TRUST INNOVATION EDGE FLEXIBILITY SATISFACTION
23

24. Next Steps: Request a Demo
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25. Questions?