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Data Governance A Worthwhile Organizational Change. Fabrice Forsans Senior Director - Enterprise Data Management Digital River, Inc. January 21, 2010. Agenda. Introductions Digital River, Inc. Data Governance Binary versus Ternary Organization

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Data Governance

A Worthwhile Organizational Change

Fabrice Forsans

Senior Director - Enterprise Data Management

Digital River, Inc.

January 21, 2010

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  • Introductions

  • Digital River, Inc.

  • Data Governance

  • Binary versus Ternary Organization

  • Example: Data Governance Matrix Organization

  • The “Chief Data Officer”

  • Key Data Management Programs

  • Implementing DG:

    • Digital River’s SAP implementation

    • Digital River’s Meta Data Registry

  • Wrap Up

  • Q & A

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  • Which area do you see yourself in?

    Business / IT / Data

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  • Since 1/1/2008, responsible for Enterprise Data Management at Digital River, Inc., Eden Prairie, MN

  • Education:

    • Engineering diploma, Aerodynamics & Thermodynamics; ENSMA, Poitiers, France, 1989.

    • MBA, Finance & Economics, NYU’s Stern School of Business, New York, 1995

  • U.S. Professional background:

    • Financial Management: Utility, Insurance (IT and Procurement), Professional Services

    • Data Management consulting (PwC Advisory, New York and Minneapolis)

  • Personal background:

    • In US since 1993, MN since 2004

    • Three children (11, 7 and 5)

    • Dual French/American citizenship

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Meet Digital River

2007 Highlights

Revenues $349 million

Net Income $71 million

Revenues $394 million

Net Income $64 million


Founded in 1994

2008 Highlights

Global e-commerce expertise

People + process + technology

Managing over $3 billion in annual online sales

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Unmatched global experience and reach

23 languages supported

  • Minneapolis

  • Aliso Viejo

  • Chicago

  • Lincoln

  • Pittsburgh

  • Portland

  • Provo

  • San Diego

  • Cologne

  • London

  • Luxembourg

  • São Paulo

  • Shanghai

  • Shannon

  • Stockholm

  • Taipei

  • Tokyo

Over 100 localized payment methods

20 transaction currencies

185 display currencies (ISO)

Global footprint

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The on-demand technology advantage

The Daily Stats

  • 20 million pages

  • 30 million emails

  • 175,000 orders

  • 5 terabytes of digital content

  • 20,000 physical shipments

  • 2 second page loads

99.997% uptime

Managed to < 40% utilization

30 APIs + 300 integrations

PCI level 1

8 global data centers


Stockholm (2)

Eden Prairie

Cologne (2)


Aliso Viejo

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Helping companies succeed in e-commerce


Consumer Electronics


Retail Distribution

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Plenty of Definitions out there…

  • Wikipedia (Categories: Information technology governance | Data management):

    • Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data across the business enterprise […]. These goals are realized by the implementation of Data governance programs, or initiatives.


    • Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures.

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Plenty of Definitions out there…

  • Jill Dyché(“Data Governance in 3-D”; 8/28/2008):

    • Data governance is the decision-making process that prioritizes investments, allocates resources, and measures results to ensure that data is managed and deployed to support business needs.

  • Trillium Software (Home >> Products >> Data Profiling >> Data Governance):

    • Data governance is about creating an overall business strategy to manage data assets—a sustained effort to monitor and improve data throughout your enterprise

  • Robert S. Seiner (“What does it mean to ‘govern data’?”, The Data Administration Newsletter; 7/1/2009) provides eight (!) definitions for “govern”, and three principles for each…for a total of 24 statements, all starting with “Governing data means…” (

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Putting it all together:

Data Governance has all the characteristics of any strategic governance process









Governing body

Business needs support


Data Governance attributes


Data Governance means treating data as a strategic area within the enterprise (e.g. Sales, Finance, HR, Sourcing, etc…)

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Governance analogies

  • Data Governance need not be invented from scratch:

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Why so hard to deploy…?

  • Cultural barriers and a lack of senior-level sponsorship:

    • In 2006, Gartner predicted that less than 10% of organizations will succeed at their first attempts at data governance

  • Lack of Data Management ownership:

    • Concept of enterprise data governance is new to many organizations and key components of data management are not well established

  • Lack of Data Management knowledge:

    • Data quality (including profiling analysis), master data management and meta data management skills are still hard to find (not systems knowledge, but underlying programs)

  • Fear of required organizational structure changes:

    • Assigning Data Governance responsibility to an independent senior “data governor” (not IT-dependent) requires significant changes to existing work flows and policies

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The Data Management wheel

  • Embracing DM means fundamentally changing the organizational structure of a company:




DM deployment



Binary model:

No Data Mgmt

IT and Business frictions

Ternary model:

Data Mgmt

No IT and Business frictions

  • The DM “wheel” is owned by the Data Stewards

  • Data Stewards interface with Business and ITStewards to carry out Data Management activities

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Example of an Enterprise Data Management Organization


DM Council/

Steering Committee

Senior DM Executive

* 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.

Program Managers





. . .

DMA** 1

DMA** 2

Data Stewards ***

DMA** 3

DMA** 4

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The CDO must be able to act Strategically

  • Data cannot be governed independently

  • Continuous conflict of interests between Technology and Data Management

  • Data not managed as a strategic asset

  • Difficulty to enforce quality rules across all enterprise

  • High cost, low returns

  • Data becomes silo-driven (like IT…)

  • Responsibility without authority

Data Mgmt.


Data Governance


IT Governance

CIO / VP Technology

CDO / VP Data Mgmt.

Process Mgmt

Data Mgmt

Data Mgmt.

Manager / Director

  • Data governed as an independent asset

  • Centralized authority over data programs

  • Cost reductions from uniform DM processes

  • Improved control over compliance and financial risks

  • Clear accountability for all aspects of data

  • Data scalable across the enterprise, and over time (growth, acquisitions…)

  • Data Management no longer dependent on IT strategy

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Data Quality Program (DQP)

  • Objective:

    • Centralize the management of quality rules for all enterprise data elements

  • Roles & responsibilities:

    • DQP Manager: responsible for the deployment of the DQP, and ongoing management of rules. Must be involved in all POC (Point of Capture) data flows.

    • Business Stewards: own the determination of rules. Engage their Data Stewards when an update/new rule is required.

    • IT Stewards: build and maintain the interfaces between data consuming systems and DQP application (i.e. Trillium)

    • Data Stewards: handle the implementation and regular review of their assigned rules (monthly data quality meetings, rules sign off, Data Quality policy enforcement, etc…)

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Master Data Management Program (MDP)

  • Objective:

    • Centralize all aspects of enterprise master data management, from quality, storage to syndication (requires DQP be deployed)

  • Roles & responsibilities:

    • MDP Manager: responsible for ensuring all enterprise master data is available, accurate, and unique.

    • Business Stewards: own the quality determination of master data, including the de-duplication matching logic.

    • IT Stewards: build and maintain syndication process between the MDM application and the consuming systems. Note: IT Stewards DO NOT modify/update master data.

    • Data Stewards: ensure master data is accurate for their assigned DMA(s); enforce MDM policy, and are the only resources allowed to modify master data content.

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Meta Data Management Program (MTDP)

  • Objective:

    • Centralize all aspects of enterprise meta data management, through the creation and ownership of the corporate MDR (Meta Data Registry)

  • Roles & responsibilities:

    • MTDP Manager: ensures all meta data is properly defined and available to both human and machines.

    • Business Stewards: provide and is accountable for the content of the MDR.

    • IT Stewards: provide support for the MDR, and help establish required interfaces between the MDR and consuming applications.

    • Data Stewards: enforce the MTDP policy; support the MTDP Manager in maintaining the MDR.

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DR’s initial DQP deployment:

  • DQP supported by Trillium Software System® applications:

    • TS Quality supports the DQP for the SAP implementation

    • All data from e-commerce systems extracted and sent to Trillium before SAP load

    • TS Discovery output provides core framework for the Business Rules Determination workshops

  • All quality rules, including data transformation, are managed by the SAP Data Steward (separation of data vs. process)

  • SAP-specific Business rules can be updated/changed easily, without any ETL modification.




Impact assessment




Clarification & remediation

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DR’s first DQP deployment using Trillium - Continued

Ancillary systems














ETL drop zone





  • Structure

  • Extract

  • Transform

  • Load

  • Content

  • Quality Rules

  • Governance

  • Certification

  • Process

  • Integration

  • Productivity

  • Controls

  • Reporting

  • Accuracy

  • Flexibility

  • Scalability

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DR Cleansing Data Process






Locate Data Set

Profile Data Set

Conduct DQ Workshops

DQ Business Rules Signoff

Apply Business Rules (“Cleanse”)

DQ Report

Business Rules

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The Business Rule Book

  • All Business Rules in Trillium are recorded in the Business Rule Book.

  • Each rule is approved and signed off by a Business Steward:

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Sample Data Quality Report

  • Measures the level of data quality = rate of compliance with business rules (Trillium output)

  • Data Quality is measured monthly, after updates in Business Rules from previous report

  • Data Stewards responsible for acting on DQ Dashboard metrics

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Meta Data Management Program (MTDP)

  • Meta Data Management is a key Data Governance process

  • Several frameworks available. DR uses ISO 11179.

  • The Meta Data Registry (MDR) is main tool supporting the MTDP.

  • To my knowledge, only one company offers ISO 11179-compliant MDR application, Data Foundations’ OneData MDR (

  • MDR stores and provides company information about:

    • Data semantics

    • Data description

    • Data accountability

    • Data location

    • Data mapping and relationship

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Wrap Up

  • Data Governance is a strategic and permanent investment

  • Data Governance dramatically reduces operational costs, mainly for the information-delivery processes

  • Data Governance helps reduce various risk exposures (financial, regulatory, market and strategic)

  • Data Governance requires an organizational change, from the Business/IT model, to the Business/Data/IT model

  • Data Governance requires a top “Data Governor”, and a dedicated Data Governance team

  • Data Governance exists through corporate Data Management programs

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Thank you!

Any questions?