10/16/2013
DATA GOVERNANCE &
DATA QUALITY PROGRAMS
BETTER OUTCOMES, WORTHWHILE CHANGE, FOR ANY
ORGANIZATION
by Deepak Bhaskar
+
AGENDA
AGENDA
 Introduction
 Speaker Bio
 Company introduction
 Data issues for our Business:
 Challenge 1
 Batch mode Data cleansing: Centralizing commerce data in an ERP
 DQP in ERP Implementation (Data Discover Profiling & DQ Tool)
 Challenge 2
 Real Time Data cleansing: Cloud Commerce Billing/Shipping Address Errors
 DQP in Real Time Address Validation & Cleansing (DQ Tool & Postal dir.)
 Further Recommendations
 Conclusion: Digital River Data Governance best practices
3
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
SPEAKER BIO:
DEEPAK BHASKAR
Sr. Manager, Data Governance, Trillium Product.
Governance and Compliance.
 At Digital River – 10+ years
 Other roles held:



Manager, Enterprise Data Quality, (2008-12)
Sr. Strategic Database Analyst, Strategic Marketing (2005-08)
Sr. Software Test Engineer, Quality Assurance (2003-05)
 Roles held in prior to Digital River include:



Lead Test Consultant, (Gelco Info. Network, now Concur Technologies)
DBA, (Eschelon Telecom, now Integra Telecom)
DBA, Software Developer , Sr. Test Engineer (techies.com)
 Education & Training:




ACE Leadership Series; Minnesota High Tech Association
Business Strategy: Competitive Advantage; Johnson School of Management, Cornell University
MBA, International Business; Keller School of Management, DeVry University
BSEE, Electrical Engineering: Microelectronics & Telecoms; Minnesota State University
4
DIGITAL RIVER
COMPANY OVERVIEW
Who We Are
Our Focus
Our Passion
Experience
Innovation
DIGITAL RIVER
Managing Over $22 Billion in Annual
Online Transactions
38 Patents Issued in Commerce,
Marketing and Payments
Generating Revenue in Virtually Every
Country on the Planet
Invest 3 Million Hours Per Year Focused
on Growing Our Clients Revenue
Technology Pioneer, Founded in 1994
2012 FINANCIAL HIGHLIGHTS
Revenue
$386 MILLION
R&D Investment
$64 MILLION
Strong Financial Balance Sheet
NASDAQ: DRIV
6
Who We Are
Our Focus
Our Passion
Experience
Innovation
SIMPILFY THE COMPLEX
We manage the complexity
and risk on a global scale to
enable a great user
experience
Store Front
Shopping Cart
Tax & Fraud
Management
Compliance
(PCI, SOX, SAS, Export)
API’s &
Integrations
Global Capabilities
Payments, Multi-lingual
Export
Compliance
Marketing and
Demand Gen
Advanced Business Models
Subs, Rentals, Points, etc.
7
Who We Are
Our Focus
Our Passion
Experience
Innovation
UNMATCHED GLOBAL EXPERIENCE AND REACH
40
localized payment methods
40
transaction currencies
30
site display languages
31
offices across the globe
15
languages in customer service
Minneapolis • Aliso Viejo • Pittsburgh • Portland • Provo • San
Diego • Seattle • Cologne • London • Luxembourg • São Paulo •
Shanghai • Shannon • Stockholm • Taipei • Tokyo • Vienna
8
Who We Are
Our Focus
Our Passion
Experience
Innovation
DIGITAL RIVER PROMISE
Why world class companies put their trust in Digital River
19 years of experience
3 million hours a year
invested in our client
success
1,400+ e-commerce
experts worldwide
Unmatched speed to
market
Over 100 third party
relationships
Deep understanding of
consumer psychology
and online behaviors
Global Demand
marketing experts
Manage more than
$22 billion in online
transactions
Most complete fraud
detection tools in the
industry
“Digital River has been with us step-by-step as we’ve launched online stores. Their technology
supports our online commerce capabilities in North America, Europe and Asia, and their
marketing solutions help us acquire and retain new customers every day.”
- Lance Binley, Logitech Vice President of Digital and E-Commerce
9
Who We Are
Our Focus
Our Passion
Experience
Innovation
SERVICES
YOUR CUSTOM ECOSYSTEM
WORLDWIDE
PAYMENTS
WORLDWIDE
COMMERCE
WORLDWIDE
MARKETING
Currency
Pricing
Local
Fulfillment
Site
Optimization
Global
Processing
Store
Architecture
Locale
Merchandising
Transaction
Routing
Store
Content
Email
Marketing
Fraud
Screening
Subscriptions
Search
Optimization
Local/VAT
Tax Support
Reporting
& Analytics
Affiliate
Marketing
Merchant
Services
Customer
Service
Brand
Development
A flexible, expandable e-commerce ecosystem
perfectly suited to the needs of your business.
10
Who We Are
Our Focus
Our Passion
Experience
Innovation
PERFORMANCE MARKETING
Marketing expertise to acquire and retain
customers.
• Search Engine Marketing services to help create
a strategy that maximizes your pay-per-click ad
spend
• Display Advertising to drive “eyeballs” to your
sites and create the brand awareness needed to
compete for market share
• Affiliate Programs and Networks to drive
revenue through a community of pay-forperformance publishers
• Site Optimization to make sure customers find
their way to your site
• Email Programs that match messages to your
customers digital body language
• Advanced Analytics to provide the data points
needed to manage key performance indicators
11
Who We Are
Our Focus
Our Passion
Experience
Innovation
WORLD-CLASS CUSTOMERS
Consumer
Electronics
TRAVEL
GAMES AND
ENTERTAINMENT
E-TAIL
SOFTWARE &
SERVICES
EDUCATION
12
Who We Are
Our Focus
Our Passion
Experience
Innovation
OPEN. MODULAR. ECOSYSTEM
13
BUSINESS CHALLENGE 1
BATCH MODE DATA CLEANSING: CENTRALIZING
COMMERCE DATA
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
EARLY YEARS (MID-90’S): SINGLE E-COMMERCE PLATFORM
 At the heart of the web hosting business:
 The order checkout workflow, which consists of:









Store homepage
Product detail Page
Shopping cart page
Bill to page
Ship to page
Payment processing page
Order confirmation page
Thank you page
Invoice page
15
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
TODAY: MANY CLOUD COMMERCE PLATFORMS (A RESULT OF ACQUISITIONS)
E-Com1
E-Com4
E-Com2
E-Com5
E-Com6
E-Com3
E-Com8
E-Com7
16
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
BATCH MODE DATA CLEANSING: CENTRALIZING COMMERCE DATA
 In 2008 Digital River was dealing with Multiple commerce platforms

Challenges:
 Different source data capture points and multiple workflows
 Different payments methods and fraud rates
 Similar technology processes performed by different systems
 Similar business concepts that used many terminologies

Cons:





Inefficient use of Developers and Functional teams
Confusion around definition of common terms
Inaccurate data being propagated across the systems
Longer times to close our books at the end of the month
Many manual work efforts
 Digital River Solution:
 Align all of the platform transaction data, as a Business
Imperative with the aid of a Data Governance Program, to
support creating a single source of truth (ERP)
17
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA MANAGEMENT ASSOCIATION (DAMA)
DATA MANAGEMENT BODY OF
KNOWLEDGE (DMBOK) GOVERNANCE
FRAMEWORK
- Data Architecture: as an integral part of the enterprise
architecture
- Data Modeling & Design: analysis, design, build, test,
deployment and maintain
- Data Storage: structured physical data assets storage
management
- Data Security– support ensuring privacy, confidentiality
and appropriate access
- Data Integration & Interoperability – support data
acquisition, transformation and movement (ETL),
federation, or virtualization
- Documents and Content – store, protect, index, and
enable access to data found in unstructured sources
(electronic files and physical records), and make data
available for integration and interoperability with
structured (database) data.
- Reference & Master Data – manage gold versions and
replicas
- Data Warehousing and Business Intelligence –
support managing analytical data processing and enable
access to decision support data for reporting and analysis
© DAMA-DMBOK2 (Apr 2012)
- Meta-data: integrate, control and deliver meta-data
-
18
Data Quality: define, monitor and improve data quality
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA MANAGEMENT ASSOCIATION (DAMA)
DATA MANAGEMENT BODY OF
KNOWLEDGE (DMBOK) GOVERNANCE
FRAMEWORK
Data Governance:
Involves planning, oversight, and
control over data management
and use of data
© DAMA-DMBOK2 (Apr 2012)
19
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA MANAGEMENT ASSOCIATION (DAMA)
Data Management Functions
Environmental Elements
© DAMA-DMBOK2 (Apr 2012)
20
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
WHAT IS DATA GOVERNANCE?
People
Process
Technology
Programs
Governing
body
Strategy
Assets
Management
Business
needs
support
Plan
Data Governance has all the
characteristics of any Strategic
governance process
Decision
-making
Procedures
Digital River’s definition of Data Governance:A set of processes that treats Data as a Strategic Area within the enterprise
(just like Sales, Finance, HR, Sourcing, etc…)
21
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
BUSINESS IMPACT/BENEFITS AND RETURN ON OBJECTIVE
 A mechanism to convert raw Order/Transaction, Customer, Client, Vendor,
Product and Other data collected from the shopper websites that we host
for our clients, to 2 categories.
 Clean Data (passed on to the ERP)
 Dirty Data (requiring some clarification and remediation)
 Digital River’s definition of Data Governance:-
 A set of processes that treats Data as a Strategic Area within
the enterprise
22
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
THE DATA MANAGEMENT WHEEL: BINARY VS. TERNARY
 In 2008 embraced DM which meant fundamentally
changing the organizational structure of Digital
River:
DM
Bus
IT
Binary model:
No Data Mgmt
IT and Business frictions
DM deployment
Bus
IT
Ternary model:
Data Mgmt
No IT and Business frictions
 The DM is a process “wheel” owned by the Data Stewards
 Data Stewards interface with Business and IT Stewards to carry
out Data Management activities around remediating the Dirty Data
23
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
ENTERPRISE DATA MANAGEMENT MATRIX ORGANIZATION & ACTIVITIES
24
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
SIMPLIFYING PLATFORMS DOING SIMILAR THINGS
 Decentralized structure
 Business functions spread across each platform
-
Accounting
Reporting
Billing
Client Management
Tax
Compliance
E-Com2
-
Accounting
Reporting
Billing
Client Management
Tax
Compliance
.
.
.
E-Com1
E-Com8
-
Accounting
Reporting
Billing
Client Management
Tax
Compliance
 Challenge:
 How can we centralize all of our platforms, creating one
true source for all Accounting, Reporting, Billing, etc?
25
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
SOLUTION: ERP

Implement an ERP system, sourced from each of the separate e-commerce
platforms

Commerce would continue to happen on platforms, and transmit to the ERP
system in batches of data
E-Com1
E-Com2
SAP - ERP
.
.
.
E-Com8
26
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
SOLUTION: ERP SYSTEM FED BY COMMERCE PLATFORM DATA
Ancillary systems
ETL
Stage
ERP
Integration
DATA QUALITY
E-Com2
ERP
ERP
DW
ETL drop
zone
E-Com3
TSS ®
Structure (ETL)
• Extract
• Transform
• Load
REPORTING
Content (Data Quality Tool)
• Quality Rules
• Governance
• Certification
BI
E-Com1
ERP
ERP
MDM
Process (ERP)
• Integration
• Productivity
• Controls
Reporting
• Accuracy
• Flexibility
• Scalability
>
Commerce occurs on platforms, batches of data transmitted to ERP
>
DQP RFP: DQP Tool became an integral Technology component of
the ERP Implementation
27
.
.
.
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA GOVERNANCE HAS A FOCUS ON POLICIES AND PROCESSES
28
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA QUALITY HAS A FOCUS ON DATA PROFILING
29
DATA QUALITY MEASURES THE LEVEL OF QUALITY
DQ COMPONENTS:
COMPLETENESS
Is all the requisite information available? Are data values missing, or in an unusable state?
Example: Product ID code not present; missing fee amount; etc.
CONFORMITY
Are there expectations that data values conform to specified formats? If so, do all the values
conform to those formats?
Examples: Phone numbers in different formats; numbers with different decimal precision; etc.
CONSISTENTCY
Do distinct data instances provide conflicting information about the same underlying data
object? Are values consistent across data sets? Do interdependent attributes always
appropriately reflect their expected consistency?
Examples: different meanings for Authorization Date or Contract End Date; etc.
ACCURACY
DUPLICATION
INTEGRITY
Do data objects accurately represent the “real-world” values they are expected to model?
Examples: misspelled names, addresses; wrong product id codes; etc.
Are there multiple, unnecessary representations of the same data objects within your data
set?
Examples: duplicate customer name, site id; address; etc.
What data is missing important relationship linkages?
Examples: A sale event cannot be linked to a marketing campaign; etc.
30
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
THE DATA QUALITY PROGRAM (DQP): PROCESS COMPONENT
>
Objective:
>
Improving the Quality of your Data through a strategic framework and a tactical
methodology
1.
Identification
2.
Monitoring
IT
Bus.
Impact
assessment
3.
Clarification &
remediation
4.
Identification:
>
Top Data Areas of importance
>
Top 5 issues/concerns in Data Areas
>
Provide unfiltered dataset to EDM
Impact assessment:
>
EDM loads dataset to TSS for Profiling
>
EDM writes up potential Business Rule
>
EDM sets up a workshop
Clarification & remediation
>
Data Steward attends Business Rules workshop
>
Data Steward clarifies and sign-off Business Rules
>
EDM Implement Business Rules
Monitoring
>
EDM builds the Data Quality dashboard
>
EDM conducts regular Data Quality compliance
monitoring
31
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA QUALITY PROGRAM (DQP FOR ERP): PEOPLE COMPONENT
>
Objective:
>
Centralize the management of quality rules for all enterprise data elements
>Roles & responsibilities:
Identification
>Data Management (DQP Manager,
Data Stewards)
Monitoring
IT
Bus.
Impact
assessment
>Handle the implementation and regular
review of their assigned rules (monthly
data quality meetings, rules sign off, Data
Quality policy enforcement, etc…)
>Business Owners:
>Own the determination of Business rules.
Engage their Data Stewards when an
update/new rule is required.
Clarification &
remediation
>IT SMEs:
>Build and maintain the interfaces
between data consuming systems and the
DQP application
32
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DQP ROLES
33
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DQP: ERP IMPACT ASSESSMENT
>
DQ Workshop Document
Unique
Values
Min
Max
Null Dist
%
Platform Id
1
GAT
GAT
0
Customer Id
37216
742328
2789613
0
Permissible values are GAT, TLA, or GNT. Nulls are not allowed. When the
value is TLA, it must be recoded to TA.
Nulls are not allowed. When a value is present, this field is a pass through.
Bill To Address Id
39044
4293408 5749721
0
Nulls are not allowed. When a value is present, this field is a pass through.
Ship To Address Id
39044
4293408 5749721
0
Nulls are not allowed. When a value is present, this field is a pass through.
No Nulls Allowed. Permissible Value set are determined within ERP
(location of master list to be determined)
No Nulls Allowed. Permissible Value set are determined within ERP
(location of master list to be determined)
Attribute
Site Id
216
bhaute
zitvee
0
Site Owner Id
151
bhaute
zitvee
0
Business Rules
DQP: ERP Clarification & Remediation
>
DQ Tool Business Rules were recorded in a Business Rule Book
>
Each rule was approved and signed off by a Business Steward
34
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
Identification
DQP: ERP CLARIFICATION & REMEDIATION
 Where do we implement the Business rules?
Monitoring
Staging
ETL drop zone
ERP
E-Com2
payment_id
number (2)
1
E-Com3
pay_method
char (2 byte)
VS
Impact
assessment
Bus.
Clarification &
remediation
E-Com1
payment_type
varchar2 (32 byte)
Visa
IT
payment_method
Visa
1
VS
payment_method
varchar2 (32 byte)
VISA
payment_method
VISA
DATA QUALITY
.
.
.
TSS ®
Each Business Rule is against a column:
>
If the Payment method column value is: ‘Visa’ , ‘1’ , ‘VS’
>
Then recode the Payment Method column value to ‘VISA’
35
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DQP: ERP MONITORING
 Measures the level of
data quality = rate of
compliance with business
rules (DQ Tool output)
 Data Quality is measured
monthly, after updates in
Business Rules from
previous report
 Data Stewards
responsible for acting on
DQ Dashboard metrics
 Over 400+ attributes
have business rules fired.
 Consistently achieving
15-20% increase in the
quality of data as a result
of data cleansing
36
BUSINESS CHALLENGE 2
REAL TIME ADDRESS VALIDATION FOR COMMERCE STORES
Who We Are
Our Focus
Our Passion
Experience
Innovation
THE ON-DEMAND TECHNOLOGY ADVANTAGE
Industry Leading 99.997% Uptime
Managed to < 40% Utilization
7 Triple Redundant Servers Worldwide
An Average Day, We Support:
• 1.5+ billion API calls
• Serve 60 million pages
• Send 3+ million emails
• Process 300,000 orders
• Create 5 authorizations/sec
• Host 6+ terabytes of digital content
38
Who We Are
Our Focus
Our Passion
Experience
Innovation
E-COMMERCE TAILORED TO YOUR NEEDS
Our partners complement existing systems, address specific technology requirements, and evolve
with the market and your growing business over time.
39
Who We Are
Our Focus
Our Passion
Experience
Innovation
API FIRST METHODOLOGY
APIs
40
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
CLOUD COMMERCE BILLING & SHIPPING ADDRESS ORDER ERRORS
 Incorrect Cloud Commerce Billing and Shipping Address Order Errors
 Challenges:
 Increased Lost / Returned Package costs
 Incorrect taxation on orders
 Cons:
 Increased customer service costs
 Unsatisfied customers
 Loss of products and sales
 Potential for undetected fraud
 Many manual work efforts to go around the challenge
 Digital River Solution:
 Digital River implemented Real-Time Address validation
(RTAV). A Data Quality Traffic Monitor/Router and a Data
Quality Tool were selected for the RTAV.
 Enterprise Software licenses were acquired and Country Postal
Templates and Country Postal Subscriptions were subscribed to.
 Data Management team was made responsible for the and Data
Governance and Data Quality efforts pertain Addresses.
 And DQ efforts moved upstream from ERP batch to real-time.
41
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
BUSINESS IMPACT/ BENEFITS AND RETURN ON OBJECTIVE FOR RTAV
42
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DUE DILIGENCE: ADDRESS DATA QUALITY VENDOR REVIEW
43
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
LENGTH OF TIME RTAV HAS BEEN IN PLACE/PROGRAM EVALUATION
DQP: HOW RTAV WORKS
44
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
SCALE OF THE RTAV RELEASE PROCESS SOLUTION (ENTERPRISE)
45
Identification
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
Monitoring
IT
Bus.
Impact
assessment
DQP: REAL TIME ADDRESS VALIDATION (RTAV)
Clarification &
remediation
Data Quality &
Traffic Monitoring
Service
• 3 Data Center red.
solution
• Load balanced
• Code Promotion (Dev,
Sys)..
• Platform Release Cycle Traffic Router
Data
Stewards
Bad Addresses
Real Time Cleansing
E-Com
Platform 2
Cleansed Addresses
E-Com
Platform 3
Hourly Batch Cleansing
DQP Tool
ETL
Clean Addresses
Global Postal
Directories
Countries covered
•
N.America (2)
•
W. Europe Bundle (16)
•
LAM Bundle (1)
•
APAC Bundle (2 Multi-byte,
1 single byte)
E-Com
Platform 1
Future Expansion
•
E.Europe
expansion
•
APAC expansion
•
LAM expansion
ERP System
IT Owners,
Code
Owners,
Tech. SME’s
Bad Addresses
Data Quality & Profiling Discovery
Tool
• 1 Data Center solution with backup
• Load balanced
• Code Promotion, Dev, Sys, Int,
Prod
• ERP Release Cycle
Business
Consumers/Owners
46
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
THE TEAM EVOLUTION: DATA MANAGEMENT AT DIGITAL RIVER (2008-13)
Vice President
Operations
2008
Vice President
Strategic
Technologies
Vice President
Enterprise Systems
and Data
Management
Sr. Director
EDM
Data Steward
Vice President
Finance
Vice President
Tax
Vice President
Operations
Vice President
Strategic
Technologies
Vice President
Strategic
Marketing
Data Steward
Data Steward
2010
Vice President
Enterprise Systems
and Data
Management
CFO
Vice President
Strategic
Technologies
Sr. Director
EDM
Data Governance Steering Committee
Enterprise Data Management
Manager
Data Quality
Data Steward
2013
Vice President
Governance &
Compliance
CIO
Vice President
Internal
Systems
Sr. Software
Engineer
Data Steward
Enterprise Data Management
CFO
COO
Data Steward
ERP
Enterprise Data Management
CIO
Vice President
Finance
Vice President
Tax
Sr. Manager
Data Governance,
DQ Tool Product
Manager
Vice President
Internal
Systems
Vice President
Internal
Systems
Sr. Manager
Data Governance, DQ
Tool Product Manager
CFO
Vice President
Product
Vice President
Finance
Vice President
Strategic
Technologies
Vice President
Tax
Vice President
Internal
Systems
Manager
Data Quality
Data Governance Steering Committee
CMO
Vice President
Development
Vice President
Product
Vice President
Governance &
Compliance
Data Governance Steering Committee
47
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
OVERALL BENEFITS OF THE DATA QUALITY PROGRAM
 Data Quality provides - Single, independent environment manages all
business rules that ensures data quality for ERP
 DQ Traffic Routing Tool and DQ Tool provides the ability to conduct Real Time
Address validation for the Commerce platforms and other batch mode cleansing
functionality for the ERP
 DQP Tool Advantage: When new e-commerce platforms are integrated to the
ERP, existing business rules are reused, minimizing redundant development, and
centralized management of Business rules
 DQP: A 4-step process that requires People, Process and Technology to support
our Data Governance efforts
 2010 Pitney Bowes Software survey - 2/3 of organizations (revenues >
$1Billion), have Data Governance activities underway (including MDM projects)
http://www.information-management.com/newsletters/data_governance_MDM_maturity_ROI-10022164-1.html
48
FURTHER
RECOMMENDATIONS
WHAT OTHER CHANGES COULD POTENTIALLY WORK BETTER?
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
PEOPLE, PROCESS, TECHNOLOGY
HR Governance
Financial Governance
Data Governance
People
HR associates
Financial analysts;
accountants
Data Stewards
Process
Human Capital
Management
Finance & Accounting
Data Management
HR systems
Accounting systems
(G/L; Tax; Treasury)
Data Quality; MDM; MDR
systems
Skill set mgmt
Recruiting
Benefits mgmt
Compensation framework
Contractor mgmt
Training
Budget & forecasting
Treasury
Financial reporting
Tax
Investment Mgmt
Data Quality Program
MDM Program
MDR Program
Managed asset
Labor force
Financial assets &
liabilities
Data
Policies & Regulations
HR policies
SOX, SAS 70, SEC, IFRS,
etc…
Privacy laws; HIPAA; SOX; DM
Policies; etc…
Training Mgr
Recruitment Mgr
Benefits Mgr
Comptroller
Tax Mgr
Investment Mgr
DQP Mgr
MDM Mgr
MDR Mgr
VP of HR
VP of Finance / CFO
VP of Data Management / CDO
(Chief Data Officer)
>Data Governance need not be invented from scratch:
Technology
Functional Programs
Functional leaders
Process owner
50
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
NEW ORG. ROLES CHIEF DATA OFFICER/VP OF DATA MGMT.
CIO / VP Technology
Focus: Process Mgmt
Data
Governance
+
IT
Governance
CDO / VP Data Mgmt.
Focus: Data Mgmt
Manager / Director

Cannot be governed Independently

Data Governed as an Independent Asset

Not managed as a Strategic Asset

Centralized authority: CDO / VP Data Mgmt.

Conflict of interests between Technology 
and Data Management

Difficult to enforce Quality rules across
the enterprise

Improved control over compliance and
financial risks

High cost and low returns


Data becomes silo-driven (like IT…)
Data scalable across the enterprise, and
over time (growth, acquisitions…)

Responsibility without authority

Data Management no longer dependent on
IT strategy

Clear accountability for all aspects of data
Cost reductions from uniform DM processes
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Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
EXPANSION OF THE EDM MATRIX ORGANIZATION
Data Stewards ***
Program Managers
DM Council/
Steering Committee
CDO*
Senior DM Executives
DQ
MDM
MDR
LDM
...
DMA** 1
DMA** 2
DMA** 3
DMA** 4
* Chief Data Officer (typically reports to CTO, CIO, CEO, CMO, CSO) http://en.wikipedia.org/wiki/Chief_data_officer
** Data Management Area: typically determined using a Data Consumption Matrix (regularly updated)
*** Data Stewards can either belong to the EDMO, remain in their respective DMA, or both.
52
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA GOVERNANCE SCOPE OF CONTROL
© Copyright Baseline Consulting Group, 2013. Used with permission from SAS Institute.
53
CONCLUSION
WHAT ARE THE LESSONS LEARNED?
Introduction
Business Challenge 1
Business Challenge 2
Recommendations
Conclusion
DATA GOVERNANCE AT DIGITAL RIVER
Identification
 Data Governance and the DQP: Managed process oversight to
ensure that data-related processes and controls are being followed
Monitoring
IT
Impact
assessment
Bus.
 Data Governance at Digital River
 Is a Strategic and Permanent investment to treat Data as a Strategic Asset
 It exists through a functional Enterprise Data Management program
Clarification &
remediation
 Data Quality Program (DQP)
 A 4-step process. Requires People, Process and Technology to support our Data Governance efforts
 Reduces Operational costs for order checkout and info. delivery processes
 Reduces Risk exposures (financial, regulatory, market and strategic)
 Both Require: An organizational change to the Ternary model (Business / Data / IT)
 A “Data Governor Authority” (e.g. VP of Data Mgmt.) and a dedicated EDM team
 Effective use of Data Quality tools (for Profiling, Discovery, Cleansing etc.)
 Contrary to many beliefs the Data Quality Tool is NOT a Database
 It is a repository of business rules; Rules can be managed and reused.
55
DEEPAK BHASKAR
Sr. Manager, Data Governance, Trillium Product
Governance and Compliance
Digital River, Inc.
http://www.linkedin.com/in/dbhaskar1
DB_2008
dbhaskar03
dbhaskar2008
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