This document provides information on data governance and discusses several challenges and approaches to data governance. It discusses that 80% of enterprise data is unstructured and spread across many sources like web data, enterprise applications, emails, and social media. Governing such diverse data assets is a complex long-term journey. It also discusses why data governance is needed, challenges of data governance, and different routes and frameworks to conduct data governance assessments and develop solutions. These include using cases studies, lean six sigma methodology, enterprise data architecture approaches, and linking data governance with machine learning. The document concludes by emphasizing structure of data, experimenting with different assessment and solutioning methods, and leveraging machine learning as a new capability.

1. August, 2019

2. Information presented in this session solely represents the
practitioner’s opinions and practices

3. dataismultiplyingat
magnanimousrate
80% of Enterprise
Data Is Unstructured
InformationIsaStrategic
CorporateAsset
Web
Data Marts
Enterprise
Applications
Spreadsheets
Email
Twitter
Data Warehouses
Databases
Events
Facebook
Governing data assets is a long journey and complex

4. Why we need to govern data?

5. We are working harder…
n=300, IDC, 2017

6. DG challenges & roles…
IDC, 2018

8. Routes to
assessments

9. Routes to Assessments…

10. We cannot model in silos…

11. • Define LOCAL practices
at Functional level
• Define STATE practices at
departmental or group
level
• Define FEDERAL
practices at the
enterprise level
Data lifecycle and maturity…
EIM
Lifecycle
Management
Data
Storage
Data
Governance
Data
Access
Data
Enrichment
Data
Creation

12. Solutioning begins with assessing data impacts on X Voices….
Source: industry

13. Solutioning with 6σ methodology for Data Governance
DMAIC for Data Practices
Stop at each step and check Questions and verify Answers

14. Routes… to solving data challenges
▪ Use cases
Enterprise Practice Details Pros Cons
Enterprise data
management
A framework for
information management
Easy to understand Complex – requires
multiple skills set on the
team to achieve success
Lean Six sigma for
enterprise
Lean enterprise practices
aimed at identifying data
defects
Process is backbone for
data stewards
Requires methodical
mindset and discipline in
execution for success
Enterprise data
architecture (ex.
TOGAF)
Governing an enterprise
information technology
architecture
Provides IT blue print for
success
Technical skills is required
for design success
Enterprise Data
Governance (misc.
frameworks)
Multiple industry
frameworks aimed at
cultivating a trusted data
environment
Teaches the A, B, C for
data governance
practices
It is a cultural journey that
requires patience and
perseverance
Data Governance
and Machine
Learning (ML Ops)
MLOps intelligence
applies to the entire
lifecycle
Collaboration between
enterprise ops
professional and data
scientists
Complex—requires expert
training

15. Machine Learning and
artificial intelligence is
applied to the
metadata, data
standards while linking
to policies (business
rules); thereby,
reducing workload for
data stewards
BI-enabled Approach
Data
Warehouse
/Vaults
Data
Lakes
Master
Data &
Data
Quality
ERP
(fin) and
Ops
Data
ME (data
party)
0 0
Data
Science
Data
Stewardship
MetaData,
Taxonomy
Data
Compliance:
GPDR …
Data
Security
Enterprise
Data
GovernanceDAM
Enterprise Ecosystem Enterprise Data
Management & Governance
Data Architecture…
)

16. Use Case: Vision, Mission… $ Multi-Billion High Tech
Manufacturer
Ineffective
customer
campaigns
CoEs teamed
together
EIM&DataGovernanceCoE
EnterprisePMO
EnterprisePracticesCoE
✓ The enterprise marketing roles are defined and
optimized for customer—data stewards are enablers
✓ The data quality of processes are monitored by
stewards and yielding actionable information to
respective users
✓ Marketing dashboards provided transparency on
data health while the executive dashboards are
relevant for timely decisions
✓ The global source of truth is one—the local sources
are is governed and trusted for campaign mgmt.
✓ The Voice of the Customer is heard “Loud and Clear”
across the enterprise and every department knows
its role of how to advance value creation
✓ Process owners and data owners (stewards)
collaborate to optimize output (RACI)
✓ Business rules are governed by policies and enabled
by intelligent software

17. Poor data
impacting
campaigns
Structure data
into 7 resolution
pillars
Easy root-cause
resolution
Use case…Major Financial Institution in excess $1 Trillion in Assets

18. 1. Structure your data into easy to understand disciplines
2. Experiment with assessments and solutioning methods
3. Leverage ML as a new capability in the enterprise toolkit
Key Takeaway:

19. about
Terry Jabali
Terry is BI and Data Governance senior practitioner at Northhighland Worldwide Consulting; he has over 20
years experience as a leader of enterprise data initiatives. As a Six Sigma Master Black Belt, he led programs
in data and operations at Cisco Systems. He led enterprise data governance and master data at Flex
international (Flex), a $6 Billion company and Western Digital Corporation (WD), a $24 Billion company.
Terry holds a B.A. degree from National-Louis University and have completed several post graduate studies
including systems dynamics at MIT.
Terry Jabali
408-644-8565
Terry.jabali@eimiq.com
Linkedin: https://www.linkedin.com/in/terryjabali/

20. Thank you