DATA GOVERNANCE &

roughhewnstupidInternet and Web Development

Nov 18, 2013 (3 years and 11 months ago)

94 views

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.
T
est 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
O
ur 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



Who We Are

Our Focus

Our Passion

Experience

Innovation

DIGITAL RIVER PROMISE

9

Unmatched
s
peed to
market

19 years of

experience

Why world class companies put their trust in Digital River

1,400+ e
-
commerce

e
xperts worldwide

3 million hours a year
invested in our client
success

Deep understanding of
consumer
p
sychology
a
nd
o
nline behaviors

Manage more
t
han

$22
b
illion
i
n online
t
ransactions

Global Demand
marketing experts

Over 100 third party
relationships

Most complete fraud
d
etection 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

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

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

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

Who We Are

Our Focus

Our Passion

Experience

Consumer
Electronics

Innovation

OPEN. MODULAR. ECOSYSTEM

13

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

15

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)

18

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)

19

DATA MANAGEMENT ASSOCIATION (DAMA)

Business Challenge 1

Introduction

Business Challenge 2

Recommendations

Conclusion

©
DAMA
-
DMBOK2 (Apr 2012)

Data Management Functions

Environmental Elements

20

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
t
reats
D
ata

as a
S
trategic
A
rea
within the enterprise

(just like
Sales, Finance, HR, Sourcing, etc…)

21

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

22

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

23

ENTERPRISE DATA MANAGEMENT MATRIX ORGANIZATION & ACTIVITIES

Business Challenge 1

Introduction

Business Challenge 2

Recommendations

Conclusion

24

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

25

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

.

.

.

26

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

D
W

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

Business Challenge 1

Introduction

Business Challenge 2

Recommendations

Conclusion

DATA
GOVERNANCE HAS A
FOCUS ON
POLICIES AND PROCESSES

29

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:

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

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

31

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

32

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

34

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:

>
I
f the Payment method column value is: ‘Visa’ , ‘1’ , ‘VS’

>
T
hen 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

36

REAL TIME ADDRESS VALIDATION FOR COMMERCE STORES

BUSINESS CHALLENGE 2

THE ON
-
DEMAND TECHNOLOGY ADVANTAGE

38

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

39

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

40

Who We Are

Our Focus

Our Passion

Experience

Innovation

APIs

CLOUD COMMERCE BILLING & SHIPPING ADDRESS ORDER ERRORS

41

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

Business Challenge
2

Business Challenge
1

Introduction

Recommendations

Conclusion

DUE DILIGENCE: ADDRESS DATA QUALITY VENDOR REVIEW

43

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
)

45

Business Challenge
2

Business Challenge
1

Introduction

Recommendations

Conclusion

DQP
: REAL TIME ADDRESS VALIDATION (RTAV)

46

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 President
Operations
Vice President
Strategic
Technologies
Sr
.
Director

EDM
Data Steward

Data Steward

Data Steward

Enterprise Data Management
Data Governance Steering Committee
Vice President
Operations
Vice President
Finance
Sr
.
Director
EDM
Vice President
Strategic
Technologies
Vice President
Strategic
Marketing
Vice President
Tax
Vice President
Enterprise Systems
and Data
Management
Vice President
Enterprise Systems
and Data
Management
CFO

Vice President
Strategic
Technologies
Data Steward

Manager
Data Quality
Data Steward

Enterprise Data Management
Data Governance Steering Committee
Vice President
Finance
Vice President
Strategic
Technologies
Vice President
Tax
Vice President
Internal
Systems
CFO

Vice President
Internal
Systems
Vice President
Product
Manager
Data Quality
CIO

Vice President
Governance
&
Compliance
Sr
.
Software
Engineer

Sr
.
Manager
Data Governance
,
DQ Tool Product
Manager
Data Steward
ERP
Enterprise Data Management
Data Governance Steering Committee
Vice President
Finance
Vice President
Tax
Vice President
Internal
Systems
CFO

Vice President
Internal
Systems
CIO

Vice President
Governance
&
Compliance
Vice President
Product
Vice President
Development
CMO

Sr
.
Manager
Data Governance
,
DQ
Tool Product Manager
COO

2008

2010

2013

OVERALL BENEFITS OF THE DATA QUALITY PROGRAM

48

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

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

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

52

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 Stewards ***

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

d
bhaskar03

dbhaskar2008