10/16/2013 DATA GOVERNANCE & DATA QUALITY PROGRAMS · 2017-08-24 · DATA MANAGEMENT ASSOCIATION...

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

7

UNMATCHED GLOBAL EXPERIENCE AND REACH

8

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


DIGITAL RIVER PROMISE

9

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


“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

10

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


Merchant Services

A flexible, expandable e-commerce ecosystem perfectly suited to the needs of your business.

YOUR CUSTOM ECOSYSTEM

Innovation

PERFORMANCE MARKETING


11

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

12

TRAVEL

E-TAIL

EDUCATION


Consumer Electronics

Innovation

http://www.textually.org/textually/archives/images/set3/NuanceLogo.jpg

http://njecc.org/index.php

//Ntfile/

http://www.google.com/imgres?imgurl=http://www.gamesarefun.com/newsimages/squareenix-logo.gif&imgrefurl=http://www.gamesarefun.com/gamesdb/company.php?companyid=401&h=100&w=180&sz=1&tbnid=S7OvpFdZvg0MQM::&tbnh=56&tbnw=101&prev=/images?q=square+enix++logo&usg=__6HcPTLHndoJa5YN1YeYLZgF5g9M=&ei=geG3SZuoGabAMpiesOkK&sa=X&oi=image_result&resnum=4&ct=image&cd=1

OPEN. MODULAR. ECOSYSTEM

13


BATCH MODE DATA CLEANSING: CENTRALIZING

COMMERCE DATA

BUSINESS CHALLENGE 1

EARLY YEARS (MID-90’S): SINGLE E-COMMERCE PLATFORM

15


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


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)


- 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)

18



DATA MANAGEMENT BODY OF KNOWLEDGE (DMBOK) GOVERNANCE FRAMEWORK

Data Governance: Involves planning, oversight, and control over data management and use of data


19




Data Management Functions Environmental Elements

20

WHAT IS DATA GOVERNANCE?


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…)

21

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

THE DATA MANAGEMENT WHEEL: BINARY VS. TERNARY


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

23

ENTERPRISE DATA MANAGEMENT MATRIX ORGANIZATION & ACTIVITIES


24

SIMPLIFYING PLATFORMS DOING SIMILAR THINGS


E-Com1 E-Com2

- 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

25

SOLUTION: ERP


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

.

.

.

26

SOLUTION: ERP SYSTEM FED BY COMMERCE PLATFORM DATA


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

28


DATA GOVERNANCE HAS A FOCUS ON POLICIES AND PROCESSES

29


DATA QUALITY HAS A FOCUS ON DATA PROFILING

DATA QUALITY MEASURES THE LEVEL OF QUALITY DQ COMPONENTS:

30

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


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

31

DATA QUALITY PROGRAM (DQP FOR ERP): PEOPLE COMPONENT


>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

32

DQP ROLES


33

DQP: ERP IMPACT ASSESSMENT


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

34

DQP: ERP CLARIFICATION & REMEDIATION


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


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

REAL TIME ADDRESS VALIDATION FOR COMMERCE STORES

BUSINESS CHALLENGE 2

THE ON-DEMAND TECHNOLOGY ADVANTAGE

38


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

39

Our partners complement existing systems, address specific technology requirements, and evolve with the market and your growing business over time.


API FIRST METHODOLOGY

40


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

42


DUE DILIGENCE: ADDRESS DATA QUALITY VENDOR REVIEW

43


LENGTH OF TIME RTAV HAS BEEN IN PLACE/PROGRAM EVALUATION

44


DQP: HOW RTAV WORKS

SCALE OF THE RTAV RELEASE PROCESS SOLUTION (ENTERPRISE)

45


DQP: REAL TIME ADDRESS VALIDATION (RTAV)

46


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


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


Technologies


Marketing

Vice PresidentTax

Vice PresidentEnterprise Systems

and Data Management

Vice PresidentEnterprise Systems

and Data Management

CFO


Technologies

Data Steward

Manager Data Quality

Data Steward




Technologies

Vice PresidentTax

Vice PresidentInternal Systems

CFO


Vice PresidentProduct

Manager Data Quality

CIO

Vice PresidentGovernance &

Compliance

Sr. Software Engineer

Sr. Manager Data Governance, DQ Tool Product

Manager

Data StewardERP



Vice PresidentTax


CFO


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

48


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

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

50

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


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

52


* 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

http://en.wikipedia.org/wiki/Chief_data_officer

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

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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

http://www.linkedin.com/in/dbhaskar1

10/16/2013 DATA GOVERNANCE & DATA QUALITY PROGRAMS · 2017-08-24 · DATA MANAGEMENT ASSOCIATION...

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