10/16/2013 data governance & data quality programs · 2017-08-24 · data management...
TRANSCRIPT
DATA GOVERNANCE & DATA QUALITY PROGRAMS
BETTER OUTCOMES, WORTHWHILE CHANGE, FOR ANY
ORGANIZATION
10/16/2013
+
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
SPEAKER BIO:
4
Introduction Business Challenge 1 Business Challenge 2 Recommendations Conclusion
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
DEEPAK BHASKAR
Sr. Manager, Data Governance, Trillium Product.
Governance and Compliance.
COMPANY OVERVIEW
DIGITAL RIVER
DIGITAL RIVER
6
Generating Revenue in Virtually Every Country on the Planet
38 Patents Issued in Commerce, Marketing and Payments
Technology Pioneer, Founded in 1994
2012 FINANCIAL HIGHLIGHTS
Revenue $386 MILLION
R&D Investment $64 MILLION
Strong Financial Balance Sheet
NASDAQ: DRIV
Invest 3 Million Hours Per Year Focused on Growing Our Clients Revenue
Who We Are Our Focus Our Passion Experience
Managing Over $22 Billion in Annual Online Transactions
Innovation
SIMPILFY THE COMPLEX
Shopping Cart
Export Compliance
Global Capabilities Payments, Multi-lingual
Advanced Business Models Subs, Rentals, Points, etc.
Tax & Fraud Management
Compliance (PCI, SOX, SAS, Export)
Marketing and Demand Gen
Store Front
API’s & Integrations
We manage the complexity and risk on a global scale to enable a great user experience
Who We Are Our Focus Our Passion Experience Innovation
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UNMATCHED GLOBAL EXPERIENCE AND REACH
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40
40
30
31
15
localized payment methods
transaction currencies
site display languages
offices across the globe
languages in customer service
Minneapolis • Aliso Viejo • Pittsburgh • Portland • Provo • San Diego • Seattle • Cologne • London • Luxembourg • São Paulo • Shanghai • Shannon • Stockholm • Taipei • Tokyo • Vienna
Who We Are Our Focus Our Passion Experience Innovation
DIGITAL RIVER PROMISE
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Unmatched speed to market
19 years of experience
Why world class companies put their trust in Digital River
1,400+ e-commerce experts worldwide
3 million hours a year invested in our client success
Deep understanding of consumer psychology and online behaviors
Manage more than $22 billion in online transactions
Global Demand marketing experts
Over 100 third party relationships
Most complete fraud detection tools in the industry
Who We Are Our Focus Our Passion Experience
“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
Innovation
SERVICES
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Store Architecture
Store Content
Local Fulfillment
Customer Service
Subscriptions
Reporting & Analytics
Locale Merchandising
Email Marketing
Search Optimization
Affiliate Marketing
Brand Development
Currency Pricing
Local/VAT Tax Support
Global Processing
Transaction Routing
Fraud Screening
Site Optimization
WORLDWIDE PAYMENTS
WORLDWIDE COMMERCE
WORLDWIDE MARKETING
Who We Are Our Focus Our Passion Experience
Merchant Services
A flexible, expandable e-commerce ecosystem perfectly suited to the needs of your business.
YOUR CUSTOM ECOSYSTEM
Innovation
PERFORMANCE MARKETING
Who We Are Our Focus Our Passion Experience
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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-for-
performance 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
Innovation
SOFTWARE & SERVICES
GAMES AND ENTERTAINMENT
WORLD-CLASS CUSTOMERS
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TRAVEL
E-TAIL
EDUCATION
Who We Are Our Focus Our Passion Experience
Consumer Electronics
Innovation
OPEN. MODULAR. ECOSYSTEM
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Who We Are Our Focus Our Passion Experience Innovation
BATCH MODE DATA CLEANSING: CENTRALIZING
COMMERCE DATA
BUSINESS CHALLENGE 1
EARLY YEARS (MID-90’S): SINGLE E-COMMERCE PLATFORM
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Introduction Business Challenge 1 Business Challenge 2 Recommendations Conclusion
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
TODAY: MANY CLOUD COMMERCE PLATFORMS (A RESULT OF ACQUISITIONS)
16
Introduction Business Challenge 1 Business Challenge 2 Recommendations Conclusion
E-Com1
E-Com2
E-Com3
E-Com4
E-Com5 E-Com6
E-Com7
E-Com8
BATCH MODE DATA CLEANSING: CENTRALIZING COMMERCE DATA
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
In 2008 Digital River was dealing with Multiple commerce platforms
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)
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
17
DATA MANAGEMENT ASSOCIATION (DAMA)
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
- 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
- Meta-data: integrate, control and deliver meta-data
- Data Quality: define, monitor and improve data quality
DATA MANAGEMENT BODY OF KNOWLEDGE (DMBOK) GOVERNANCE FRAMEWORK
© DAMA-DMBOK2 (Apr 2012)
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DATA MANAGEMENT ASSOCIATION (DAMA)
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
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)
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DATA MANAGEMENT ASSOCIATION (DAMA)
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
© DAMA-DMBOK2 (Apr 2012)
Data Management Functions Environmental Elements
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WHAT IS DATA GOVERNANCE?
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
Data Governance has all the characteristics of any Strategic
governance process
Process
People
Technology
Programs Management
Governing body
Procedures
Plan
Decision-making
Business needs
support
Strategy
Assets
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…)
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BUSINESS IMPACT/BENEFITS AND RETURN ON OBJECTIVE
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
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
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THE DATA MANAGEMENT WHEEL: BINARY VS. TERNARY
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
In 2008 embraced DM which meant fundamentally changing the organizational structure of Digital River:
IT Bus IT Bus
DM
Binary model:
No Data Mgmt
IT and Business frictions
Ternary model:
Data Mgmt
No IT and Business frictions
DM deployment
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
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ENTERPRISE DATA MANAGEMENT MATRIX ORGANIZATION & ACTIVITIES
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
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SIMPLIFYING PLATFORMS DOING SIMILAR THINGS
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
E-Com1 E-Com2
- Accounting - Reporting - Billing - Client Management - Tax - Compliance
- Accounting - Reporting - Billing - Client Management - Tax - Compliance
- 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?
. . . E-Com8
Business functions spread across each platform
Decentralized structure
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SOLUTION: ERP
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
Commerce would continue to happen on platforms, and transmit to the ERP system in batches of data
Implement an ERP system, sourced from each of the separate e-commerce platforms
E-Com1
E-Com2
E-Com8
SAP - ERP
.
.
.
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SOLUTION: ERP SYSTEM FED BY COMMERCE PLATFORM DATA
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
ERP ETL
E-Com1
E-Com2
E-Com3
DATA QUALITY ERP
ERP Integration
Structure (ETL) • Extract • Transform • Load
Content (Data Quality Tool) • Quality Rules • Governance • Certification
ERP DW
BI
REPORTING
Process (ERP) • Integration • Productivity • Controls
Reporting • Accuracy • Flexibility • Scalability
Ancillary systems
ERP MDM
ETL drop zone
TSS ®
Stage
.
.
.
> Commerce occurs on platforms, batches of data transmitted to ERP
> DQP RFP: DQP Tool became an integral Technology component of the ERP Implementation 27
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Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
DATA GOVERNANCE HAS A FOCUS ON POLICIES AND PROCESSES
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Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
DATA QUALITY HAS A FOCUS ON DATA PROFILING
DATA QUALITY MEASURES THE LEVEL OF QUALITY DQ COMPONENTS:
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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
Do data objects accurately represent the “real-world” values they are expected to model? Examples: misspelled names, addresses; wrong product id codes; etc.
DUPLICATION Are there multiple, unnecessary representations of the same data objects within your data set? Examples: duplicate customer name, site id; address; etc.
INTEGRITY
What data is missing important relationship linkages? Examples: A sale event cannot be linked to a marketing campaign; etc.
THE DATA QUALITY PROGRAM (DQP): PROCESS COMPONENT
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
Identification
Impact
assessment
Clarification &
remediation
Monitoring IT Bus.
1. Identification:
> Top Data Areas of importance
> Top 5 issues/concerns in Data Areas
> Provide unfiltered dataset to EDM
2. Impact assessment:
> EDM loads dataset to TSS for Profiling
> EDM writes up potential Business Rule
> EDM sets up a workshop
3. Clarification & remediation
> Data Steward attends Business Rules workshop
> Data Steward clarifies and sign-off Business Rules
> EDM Implement Business Rules
4. Monitoring
> EDM builds the Data Quality dashboard
> EDM conducts regular Data Quality compliance monitoring
> Objective:
> Improving the Quality of your Data through a strategic framework and a tactical methodology
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DATA QUALITY PROGRAM (DQP FOR ERP): PEOPLE COMPONENT
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
>Roles & responsibilities:
>Data Management (DQP Manager, Data Stewards)
>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.
>IT SMEs:
>Build and maintain the interfaces between data consuming systems and the DQP application
Identification
Impact
assessment
Clarification &
remediation
Monitoring IT Bus.
> Objective:
> Centralize the management of quality rules for all enterprise data elements
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DQP ROLES
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
33
DQP: ERP IMPACT ASSESSMENT
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
Attribute Unique Values
Min Max Null Dist
% Business Rules
Platform Id 1 GAT GAT 0 Permissible values are GAT, TLA, or GNT. Nulls are not allowed. When the value is TLA, it must be recoded to TA.
Customer Id 37216 742328 2789613 0 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.
Site Id 216 bhaute zitvee 0 No Nulls Allowed. Permissible Value set are determined within ERP (location of master list to be determined)
Site Owner Id 151 bhaute zitvee 0 No Nulls Allowed. Permissible Value set are determined within ERP (location of master list to be determined)
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
> DQ Workshop Document
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DQP: ERP CLARIFICATION & REMEDIATION
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
Where do we implement the Business rules?
E-Com1
E-Com2
E-Com3
ERP
DATA QUALITY
ETL drop zone
TSS ®
payment_type varchar2 (32 byte)
Visa
payment_id number (2)
1
pay_method char (2 byte)
VS
payment_method varchar2 (32 byte)
VISA
payment_method Visa
1 VS
payment_method VISA
Impact
assessment
Identification
IT Bus.
Clarification &
remediation
Monitoring
.
.
.
Staging
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
DQP: ERP MONITORING
Business Challenge 1 Introduction Business Challenge 2 Recommendations Conclusion
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
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REAL TIME ADDRESS VALIDATION FOR COMMERCE STORES
BUSINESS CHALLENGE 2
THE ON-DEMAND TECHNOLOGY ADVANTAGE
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Who We Are Our Focus Our Passion Experience Innovation
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
Industry Leading 99.997% Uptime
Managed to < 40% Utilization
7 Triple Redundant Servers Worldwide
E-COMMERCE TAILORED TO YOUR NEEDS
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Our partners complement existing systems, address specific technology requirements, and evolve with the market and your growing business over time.
Who We Are Our Focus Our Passion Experience Innovation
API FIRST METHODOLOGY
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Who We Are Our Focus Our Passion Experience Innovation
APIs
CLOUD COMMERCE BILLING & SHIPPING ADDRESS ORDER ERRORS
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
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.
BUSINESS IMPACT/ BENEFITS AND RETURN ON OBJECTIVE FOR RTAV
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
DUE DILIGENCE: ADDRESS DATA QUALITY VENDOR REVIEW
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
LENGTH OF TIME RTAV HAS BEEN IN PLACE/PROGRAM EVALUATION
44
Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
DQP: HOW RTAV WORKS
SCALE OF THE RTAV RELEASE PROCESS SOLUTION (ENTERPRISE)
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
DQP: REAL TIME ADDRESS VALIDATION (RTAV)
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
E-Com
Platform 3
E-Com Platform 2
E-Com Platform 1
ETL
Global Postal Directories
DQP Tool
ERP System
Traffic Router
Real Time Cleansing
Hourly Batch Cleansing Bad Addresses
Bad Addresses
Cleansed Addresses
Clean Addresses
Impact
assessment
Identification
IT Bus.
Clarification &
remediation
Monitoring
Business Consumers/Owners
IT Owners, Code
Owners, Tech. SME’s
Data Stewards
Countries covered • N.America (2) • W. Europe Bundle (16) • LAM Bundle (1) • APAC Bundle (2 Multi-byte, 1 single byte)
Future Expansion • E.Europe
expansion • APAC expansion • LAM expansion
Data Quality & Traffic Monitoring Service • 3 Data Center red.
solution • Load balanced • Code Promotion (Dev,
Sys).. • Platform Release Cycle
Data Quality & Profiling Discovery Tool • 1 Data Center solution with backup • Load balanced • Code Promotion, Dev, Sys, Int,
Prod • ERP Release Cycle
THE TEAM EVOLUTION: DATA MANAGEMENT AT DIGITAL RIVER (2008-13)
47
Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
Vice PresidentOperations
Vice PresidentStrategic
Technologies
Sr. Director EDM
Data Steward
Data Steward
Data Steward
Enterprise Data Management Data Governance Steering Committee
Vice PresidentOperations
Vice PresidentFinance
Sr. DirectorEDM
Vice PresidentStrategic
Technologies
Vice PresidentStrategic
Marketing
Vice PresidentTax
Vice PresidentEnterprise Systems
and Data Management
Vice PresidentEnterprise Systems
and Data Management
CFO
Vice PresidentStrategic
Technologies
Data Steward
Manager Data Quality
Data Steward
Enterprise Data Management Data Governance Steering Committee
Vice PresidentFinance
Vice PresidentStrategic
Technologies
Vice PresidentTax
Vice PresidentInternal Systems
CFO
Vice PresidentInternal Systems
Vice PresidentProduct
Manager Data Quality
CIO
Vice PresidentGovernance &
Compliance
Sr. Software Engineer
Sr. Manager Data Governance, DQ Tool Product
Manager
Data StewardERP
Enterprise Data Management Data Governance Steering Committee
Vice PresidentFinance
Vice PresidentTax
Vice PresidentInternal Systems
CFO
Vice PresidentInternal Systems
CIO
Vice PresidentGovernance &
Compliance
Vice PresidentProduct
Vice PresidentDevelopment
CMO
Sr. Manager Data Governance, DQ Tool Product Manager
COO
2008
2010
2013
OVERALL BENEFITS OF THE DATA QUALITY PROGRAM
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Business Challenge 2 Business Challenge 1 Introduction Recommendations Conclusion
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
WHAT OTHER CHANGES COULD POTENTIALLY WORK BETTER?
FURTHER RECOMMENDATIONS
Recommendations
PEOPLE, PROCESS, TECHNOLOGY
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Business Challenge 1 Business Challenge 2 Introduction Conclusion
>Data Governance need not be invented from scratch: HR Governance Financial Governance Data Governance
People HR associates Financial analysts;
accountants Data Stewards
Process Human Capital Management
Finance & Accounting Data Management
Technology HR systems Accounting systems (G/L; Tax; Treasury)
Data Quality; MDM; MDR systems
Functional Programs
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…
Functional leaders Training Mgr
Recruitment Mgr Benefits Mgr
Comptroller Tax Mgr
Investment Mgr
DQP Mgr MDM Mgr MDR Mgr
Process owner VP of HR VP of Finance / CFO VP of Data Management / CDO
(Chief Data Officer)
Recommendations
NEW ORG. ROLES CHIEF DATA OFFICER/VP OF DATA MGMT.
51
Business Challenge 1 Business Challenge 2 Introduction Conclusion
CIO / VP Technology
Manager / Director
CDO / VP Data Mgmt. Data
Governance + IT
Governance
Focus: Process Mgmt Focus: Data Mgmt
Data Governed as an Independent Asset
Centralized authority: CDO / VP Data Mgmt.
Improved control over compliance and financial risks
Clear accountability for all aspects of data
Cost reductions from uniform DM processes
Data scalable across the enterprise, and over time (growth, acquisitions…)
Data Management no longer dependent on IT strategy
Cannot be governed Independently
Not managed as a Strategic Asset
Conflict of interests between Technology and Data Management
Difficult to enforce Quality rules across the enterprise
High cost and low returns
Data becomes silo-driven (like IT…)
Responsibility without authority
Recommendations
EXPANSION OF THE EDM MATRIX ORGANIZATION
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Business Challenge 1 Business Challenge 2 Introduction Conclusion
* 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.
CDO*
DQ MDR MDM LDM . . . Program Managers
Senior DM Executives
Data
Ste
ward
s *
**
DMA** 1
DMA** 2
DMA** 4
DMA** 3
DM Council/ Steering Committee
Recommendations
DATA GOVERNANCE SCOPE OF CONTROL
53
Business Challenge 1 Business Challenge 2 Introduction Conclusion
© Copyright Baseline Consulting Group, 2013. Used with permission from SAS Institute.
WHAT ARE THE LESSONS LEARNED?
CONCLUSION
Data Governance and the DQP: Managed process oversight to
ensure that data-related processes and controls are being followed
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
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.
DATA GOVERNANCE AT DIGITAL RIVER
55
Conclusion Business Challenge 1 Business Challenge 2 Recommendations Introduction
Impact
assessment
Identification
IT Bus.
Clarification &
remediation
Monitoring
56
DEEPAK BHASKAR Sr. Manager, Data Governance, Trillium Product Governance and Compliance Digital River, Inc.
http://www.linkedin.com/in/dbhaskar1
DB_2008
dbhaskar03
dbhaskar2008