edwws: maximizing the value of mdm with data governance

Proprietary & Confidential

The First Step in EIM

Maximizing the Value of MDM with Data Governance

Kelle O’Neal Managing Partner

First San Francisco Partners Inc. @1stsanfrancisco

pg 2 Proprietary and Confidential

Keys to Success

Successful MDM Implementation

Technology

Process People

Failed MDM Implementation!

Technology

Process People


Some Warning Signs

Limited business involvement:

We know what the business wants to do, we can involve them later in testing…

Not recognizing the importance of Data

Stewardship: We can deal with the concept of Data Stewards at a later date…

No clear understanding of what data is needed to support the business

objectives: Bring all the data just in case…

Not enough thought given to metrics and measurement:

How will we know when we have achieved our objective?

Inability to reach a clear decision:

Definitions, Input Sources, Survivorship, KDE’s, lookup values, exception management processes, data quality targets, etc.

Value not understood: I’m not sure that this new piece of technology will actually help me in any way…

How do you know when you’re heading for trouble?


Why We’re Here

Purpose: Drive awareness of how MDM and Data

Governance interact to provide business value

An understanding of:   MDM and Data Governance working together   Data Governance decisions for MDM planning

& implementation   How to create success

Outcome


Agenda

•  FSFP’s Perspective on MDM

•  Data Governance Framework

•  Building the Organization • MDM decisions made by Data Governance

•  Ensuring Success


[ FSFP’S PERSPECTIVE ]


Enterprise Information Management Framework

Provides a holistic view of information in order to manage data as a corporate asset

Enterprise Information Management

Information Strategy

Architecture and Technology Enablement

Content Delivery

Business Intelligence and Performance Management

Data Management Information Asset Management

GOVERNANCE

ORGANIZATIONAL ALIGNMENT

Content Management


Develop and execute architectures, policies and procedures to manage the full data lifecycle

Enterprise Data Management

Enterprise Data Management Ensure data is available, accurate, complete and secure

Traditional & Big Data Governance

Data Quality Management

Data Architecture Data Retention/Archiving

Master Data Management

Big Data Management

Metadata Management

Reference Data

Management

Privacy/Security


What is Master Data Management?

Gartner Master Data Management (MDM) is a discipline in which the business and the IT organization work together to ensure the uniformity, accuracy, semantic persistence, stewardship and accountability of the enterprise’s official, shared master data. Organizations apply MDM to eliminate endless, time-consuming debates about “whose data is right,” which can lead to poor decision making and business performance.

Wikipedia Master data management (MDM) comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization (which may include reference data). MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.


Master Data Management Services Framework


MDM Value Proposition

Business Value

•  Improve reporting and decision making •  Better regulatory reporting compliance • Maximize revenue with integrated

solutions across business units •  Reduce costs through operational

efficiency gains •  Easily share trusted data across various

business functions and channels • Drive costs of bad data out of the

system •  Rapidly respond to new business

opportunities •  Provide “plug and play” capabilities to

consolidate and easily extend IT architecture

Technology Enablement

•  Easily integrate data across siloed IT solutions

•  Ensure the quality of data being delivered enhances the value of data integration investments

•  Provides a single integrated architecture and solution

•  Support for service oriented architecture (SOA) ensures data quality capabilities can easily be consumed as services and provides a flexible, scalable environment for data to move across the enterprise

•  Easily assimilate new data elements into enterprise processes


Challenges of MDM Success

According to a recent TDWI survey, many of the MDM challenges are organizational and collaborative issues—not technical ones.

Half of users surveyed (56%) realize that MDM can be hamstrung without data governance.

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You can’t “do” MDM without Data Governance

An MDM initiative is an important component of a Data

Governance Strategy

Must Have “Tools”…

•  Documented and enforced governance policies and processes

•  Clear accountability, ownership and escalation mechanisms

•  Continuous measurement and monitoring of data quality & adoption

•  Executive support to create a culture of accountability around the quality of the data…it’s everyone’s concern

•  Solid alignment between business & IT

•  Understanding of data before diving into an MDM “Project”

Technology alone will not solve the problem

What else is needed?


[ DATA GOVERNANCE FRAMEWORK ]


Data Governance Definition

Data Governance is the organizing framework for establishing strategy, objectives and policy for effectively managing corporate data.

It consists of the organization, processes, policies, standards and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of data.

Communication

Data Strategy

Data Policies, Processes & standards

Metrics and KPIs

A Data Governance Program consists of the inter-workings of strategy, standards, policies, measurements and communication.


Data Governance Framework


Why is Data Governance Important?

•  Increasing customer demands and new regulations

•  Streamlines and unifies the approach to managing data

•  Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business

•  Balances silo-ed short-term project delivery focus

•  Traditional projects don’t give enough focus to data management

•  Systems are becoming more challenging to manage

•  Data quality issues are persistent

Data is a valuable Corporate Asset


Data Governance & MDM Work Together

Standardized Methods and Data Definitions

Roles and Responsibilities

Decision Rights

Arbiters and Escalation

Statistics / Analysis / Monitoring

Discovery and Profiling

Cleansing

Duplicate Detection

Data Maintenance and Management

Measurement and Monitoring

Data Sharing

Workflow

Provide Guidance

Track Progress

Governance MDM

Create & Enforce Policies

Provide Feedback


[ BUILDING THE ORGANIZATION ]


Operating Model

•  Outlines how Data Governance will operate

•  Forms basis for the Data Governance organizational structure – but isn’t an org chart

•  Ensures proper oversight, escalation and decision making

•  Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business

•  Creates the infrastructure for accountability and ownership

Wikipedia: An Operating Model describes the necessary level of business process integration and data standardization in the business and among trading partners and guides the underlying Business and Technical Architecture to effectively and efficiently realize its Business Model. The process of Operating Model design is also part of business strategy.


Operating Model Design Principles

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Design Principles

Principle Description Be clear on purpose Build governance to guide and oversee the strategic and enterprise

mission

Enterprise thinking Provide consistency and coordination for cross functional initiatives. Maintain an enterprise perspective on data

Be flexible If you make it too difficult, and people will circumvent it. Make it customizable (within guidelines), and people will get a sense of ownership

Simplicity and usability are the keys to acceptance

Adopt a simple governance model people can use. A complicated and inefficient governance structure will result in the business circumventing the process

Be deliberate on participation and process

Select sponsors and participants. Do not apply governance bureaucracy solely to build consensus or to satisfy momentary political interest

Enterprise wide alignment and goal congruence

Maintain alignment with both enterprise and local business needs. Guide prioritization and alignment of initiatives to enterprise goals

Establish policies with proper mandate and ensure compliance

Clearly define and publicize policies, processes and standards. Ensure compliance through tracking and audit

Communicate, Communicate, Communicate!

Frequent, directed communication will provide a mechanism for gauging when to “course correct”, manage stakeholder and effectiveness of the program


Data Governance Leads: Business (Full Time) and IT (Support) Program Manager

Sample: DG Operating Model

Data Governance Steering Committee

Data Quality Team Information Delivery Team

MDM Delivery Team

Reference Data Delivery Team

Data Governance Working Group

Information Architecture Lead

Security –Risk Lead (ISSC)

Existing Technology Workstreams and Delivery Teams

Data Governance Analysts •  Represents cross-functional data analysis and data governance principles for the workstreams (Information, Mgr Acct & Results, Reference data, Acctg & Settlement) •  When MDM is completed, becomes the go to person for data related questions

Data Stewards Lead •  Represents data stewardship within each of the workstreams (Data Mgmt, Mgr Acct & Results, Reference Data, Acctg & Settlement)

Compliance Global Services

International Relationship Management

CFO Marketing IT

Common Services

Information Management supports: •  Data Quality lead •  Metadata Lead •  Data Custodian Lead


Keys to a Successful DG Organization

•  Governance team must contain members from multiple lines of business

•  Team members must represent both business and IT

•  Team needs to meet on a regular basis

•  Agreed upon fundamentals that serve as the Guiding Principles

•  Clear lines of communication

•  Ensure the operating model fits the culture of the company


[ MDM DECISIONS MADE BY DATA GOVERNANCE ]

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Building support for MDM

•  Tie the project to a larger business initiative

•  Link the value of MDM to specific corporate and organizational initiatives

•  Utilize metrics to make MDM real

•  Leverage the approval process

•  Articulate the value to individuals in their terms, per their interests and priorities

•  Identify an Executive Mentor who can help you sell up • Work with those individuals who have the power to

approve and/or can influence the approvers

•  Publish updates and incremental successes


MDM Decisions Made by DG

Category Decision Entity Types •  What type of data will be managed in the MDM Hub

•  What are the agreed upon definitions of each type •  What is the required cardinality between the entity types •  What constitutes a unique instance of an entity

Key Data Elements •  Purpose, definition and usage of each data element

Hierarchies and Relationships

•  Purpose, definition and usage of each hierarchy / relationship structure

Audit Trails and History •  How long do we have to keep track of changes

Data Contributors •  What type of data do they supply •  Why is this needed •  At what frequency should they supply it •  What should be taken for Initial load versus ongoing


MDM Decisions Made by DG (cont.)

Recommendations Meeting – Master Data Management (MDM) Assessment 071411 Category Decision

Data Quality Targets •  How good does the data have to be •  Root cause analysis

Data Consumers •  Who needs the data and for what purpose •  What do they need and at what frequency

Survivorship •  What should happen when…

Lookups •  Which attributes are lookup attributes •  What are the allowable list of values per attribute •  How different are the values across the applications

and how do we deal with inconsistencies

Types of Users and Security •  What types of users have to be catered for •  Can they create, update, delete, search •  Can they merge, unmerge

Delete •  How should deletes be managed

Privacy and Regulatory •  Privacy and regulatory issues


Customer Data Governance Stewardship Activities

Business Request Change Request

Hierarchy Management Match/Merge

Data Quality Customer Search

Data Stewardship


[ ENSURING SUCCESS ]


Seven Reasons Why MDM Needs Data Governance

Source TDWI - Seven Reasons Why MDM Needs DG

1 • MDM needs DG’s collaborative environment

2 • MDM needs DG’s stewardship capabilities

3 • MDM needs DG’s change management process

4 • MDM needs DG’s mandate

5 • MDM needs DG as it grows into enterprise scope

6 • MDM needs DG’s guidance as it matures into new generations

7 • MDM needs DG to support its priorities


Success Measures

People

  # of DG decisions backed up by the Steering Committee (SC)   # of approved projects from DG

  # of issues escalated to SC and resolved   # of data owners and data managers identified

  DG adoption rate by enterprise personnel

Process

  # of data consolidated processes   # of approved and implemented standards, policies, and processes to effectively manage core

business data   # of consistent data definitions (consistency on how core business data is defined and used

across enterprise, independently of a specific initiative or context)   Existence of and adherence to a business request escalation process to manage disputes

regarding data

  Integration into the project lifecycle process to ensure DG oversight of key enterprise initiatives

Technology

  # of consolidated data sources consolidated   # of data targets using mastered data   Presence and usage of a unique identifier(s)   # of address exceptions   Data integrity across systems

  Records/data aged past target   MDM Hub availability   # of hours spent investigating, cleaning

master data   % of matched records   # of new records loaded

Proprietary & Confidential

The First Step in EIM

Contact Info www.firstsanfranciscopartners.com

Kelle O’Neal [email protected]

415-425-9661 @1stsanfrancisco

edwws: maximizing the value of mdm with data governance

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