Booz Allen Hamilton Submission for Data Governance Best

Booz Allen Hamilton Submission for Data Governance Best Practice Award

February 28, 2013

Shyla Kennedy

Manager, ERA Data Governance

1

Table Of Contents • Submission Details and Acknowledgements

• Problem Statement

• Solution

• Actual Program Benefits Realized to Date

• Initial Framework: Roles and Policies

• Approach to Integration

• Examining the Possibilities and Making Change

• Deepening Integration

• Taking Stock – Jan 2012 Data Governance Maturity Assessment

• Current Organizational Structure, Branding, and Literature

• Tools and Templates to Assist Other Teams

• Excerpts From Our Firm-Wide Virtual Course

2

Submission Details & Acknowledgements

• Corporate Name: Booz Allen Hamilton, Inc

• Corporate Address: 8283 Greensboro Drive McLean, Virginia 22102

• Submission Contact: Shyla Kennedy, Manager Enterprise Reporting & Analytics Data

Governance Email: [email protected] Office: 703-984-0651

• Booz Allen acknowledges the DGIQ submission conditions as defined on the DGIQ website

• Booz Allen also wishes to thank the organizers of DGIQ for this opportunity and the judges for

their time in reviewing all of the submissions. Many of the materials enclosed herein may seem

familiar. That is because they are a direct result of what we have learned in previous DGIQ

conference sessions.

2

mailto:[email protected]

3 3

The Problem…..

• Before Booz Allen went public in 2010, C-suite support for data governance was tepid.

• Confronted by public-reporting requirements, leadership recognized that robust Business Intelligence

built on strong data governance was imperative due to…..

Multiple BI delivery organizations: some rooted in the business, others in IS

– Disparate agendas, priorities

– Inconsistent interaction model

Lack of centralized leadership / accountability for delivery and supporting BI architecture

– Neither team was completely dedicated to BI delivery – had other responsibilities

– Each team lacked bandwidth for effective delivery – supporting old while developing new

– Lack of certain specialized resources with the capacity (bandwidth and skills) to implement optimal BW/DW

architecture

Core Services

FIRST

IS

Data Services Finance PME Acquisitions

ASC (7)

PCO (3)

Cognos (5)

Hyperion (9)

Transaction Systems (5)

Current “Garage” (6)

New DMT (12)

BUILD RUN

Marts & Reports

BUILD RUN

DW, DMT & Marts

BUILD RUN

Reports

BUILD RUN

Marts & Reports

BUILD RUN

Reports

Training (1)

14 18 # ? # ? # ?

SBS PBS Go Team People Services PME Acquisitions Strategy Mgmt Controlling Security Team

4

The Solution: Introduce Enterprise Reporting & Analytics to Centralize Business Intelligence & Data Governance

From Requirements to Information Delivery….

The ERA program aims to:

• Increase credibility and quality of data through

defined processes, meaningful agreement, and

formal communications

• Reduce misuse/misreporting of data

• Assure correct access and visibility of data

• Informed Management Decisions

• Reduced Complexity

• Mitigation of Risk (compliance, privacy, security)

• Improved Bottom Line

Req.

Req. Req.

Req.

Req. Req. Req.

Business / Functional Owners

EIM Front Door

Fin

ance

FD

People

FD

Acquis

itions

FD

Enterprise

Data Warehouse

Business Intelligence

Applications

Req. Req. Req.

Req. Req.

Req. Req.

Req.

Req. Req. Req.

Account Mgmt

Fin

ance A

M

People

AM

Acquis

itions A

M

Testin

g &

Tra

inin

g

Data

Go

vern

an

ce

Enterprise

Account

Management

Enterprise

Information

Management

(EIM)

Leads to

5

Establishing Data Governance roles, responsibilities, and policies set the foundation for further structure and standardization to support major organizational changes

• A direct benefit is that the first year saw a paradigm shift away from collegial deference toward management

accountability and increased emphasis on fiduciary responsibilities

• Data mapping and profiling standards save the firm time and money as projects are being executed because we

validate assumptions while there is time to adjust project goals and before we begin technical development

• Data quality reporting has become a standard part of project requirements and are operationalized as part of all

new releases. As a result, we have increased operational teams’ ability to minimize issues and prevent poor

data from flowing into other systems and processes

• Penetration exists in every area of the organization, but there are varying degrees of maturity

• The value of data governance has been underscored as leadership reshapes our organization to prepare for

new market dynamics in the coming year

5

6

Roles Responsibilities Inputs Outputs

Data Governance

Sponsor

(Sam Strickland)

Maintains alignment between Data Governance Committee and CIO Council based upon

organizational priority

Advocates broader enterprise understanding of need/benefits of Data Governance

Assists in securing resources as needed for Data Governance working teams

Unresolved DG Issues

Data Governance Status

DG Executive Direction

Data Governance

Lead / Manager

(Chris Soong/Shyla

Kennedy)

Manages Data Governance communication within the organization

Facilitates the Data Governance Committee to establish policies for ensuring responsive

use and access to corporate data

Leads Data Governance working sessions

Ensures decisions are aligned with the organization’s strategy

ERA Priorities


DG Leadership

DG Communication

DG Operating Model

DG Agenda & Status

Data Governance

Committee (TBD)

Works collectively as a cross-data domain/subject area group to assess and audit the

effectiveness and efficiency of the Data Governance program

Serves as focus group to assist with Data Governance evolution and expansion

DG Agenda / Issues DG Priorities

Data , Subject Area,

Application Owners

Provides executive leadership (>L5) over a domain of enterprise data

Approves the data policies, standards and guidelines pertaining to the accuracy, validity,

and security/privacy of that data

Serves as authoritative voice over subject area / Data Steward working teams

Champions for Data Governance within owner’s scope

Corporate Governances

Unresolved

Subject Area Status

Subject Area Priorities

Domain Direction

Domain Priorities


Data , Subject Area

Steward

Serves as SME of a specific subject area Defines the data policies, standards and

guidelines pertaining to the accuracy, validity, and security/privacy of that data (L5/4)

Manages definition and implementation of terminology, definitions, calculations and

common master data used across the enterprise systems

Facilitates data issue identification and resolution

Subject Area

Touchpoints

Subject Area Expertise

Subject Area Definitions

Subject Area Decisions

and Direction

Subject Area Status


Reporting

Data Entry, Data Pulls, Analysis and Requirements with focus on quality and delivery

User Acceptance testing

Data validation

Data Governance

Direction

Questions/Issues

Status

Data Entry

Answers

Recommendations

Status Operations, SME,

Operational Reports

User

Consumer of ERA Information Certified & Trusted Info Improved Business

Decisions

The Initial Framework……

7

Role Responsibilities Inputs Outputs

ERA Governance

Analyst

Serves as liaison between the business and IS

Advocates, facilitates, and audits governance practices for specific initiatives

Ensure right stakeholder team is engaged

Facilitates rationalization of priorities, data processes, data standards, data requirements

Escalation path for data issues

Provides reporting and metrics to demonstrate effectiveness of data quality

Corporate Priorities

DG Priorities

AM Leadership/Priorities

DG Priorities

DG Direction to AMs

ERA Account

Manager

Advocates Data Governance practices for efforts in development and production

Works closely with assigned Business Function teams to rationalize data requirements and

support appropriate usage and understanding of data

Facilitate and drive data owners/stewards to define and maintain data properly

Ensure right stakeholder team is engaged

Ensures data / metrics library is current and effective for business users

Assists in determining data governance priorities

ERA Demand from

Business/Functional

Reporting/Analytics

AM Priorities

DG Priorities

ERA App Deployment

ERA App Expertise

Leveraged Insights

DG Priorities

ERA Testing /

Training

Manages testing to assure accuracy and compliance with Data Governance program

Assists in training and change management activities as information is deployed

ERA Applications UAT Coordination

UAT Coordination

IS EIM Lead Serves as liaison between Data Governance Committee and IS’ EIM Team

Champion of Data Governance practices within IS’ EIM Team


IS Priorities

DG Priorities

DG Priorities

DG Direction to EIM

Team

EIM BA Functional

Lead

Advocates Data Governance practices for efforts in development and production

Works closely with assigned ERA Account Managers to rationalize data requirements and

support appropriate usage and understanding of data

Ensures data / metrics library is current and effective for business users and translated for

EIM technical teams


ERA Demand from

Account Management

Data / Metric Expertise

and Reqs from AM and

Business/Functional

Reporting/Analytics

ERA Technology and

Functional App

Capabilities

EIM BI Apps Team Serves as technical SME and implementer for data practices in the OLAP and UI layers of

the EIM information architecture

ERA Application

Requirements

Reporting & Analysis

Capabilities w/in ERA Apps

EIM Data Warehouse

Team

Serves as technical SME and implementer for data practices in the Data Warehouse and

Relational Data Mart layers of the EIM information architecture

Develops processes needed to cleanse, integrate, transform and load the data

Designs and implements the data model matched to business requirements

Data Demand Single Source of Truth

for ERA Applications

8 Filename/RPS Number

Manages

this

information

= Data

Governance

Committee

Too Much Detail!!!!

9

Summary Data Governance RACI Matrix Function Business

Owner

ERA Account

Management

ERA

Information

Management

Enterprise

Systems

Delivery

Data

Governance

Office

Manage Data

Governance Model I C C C R

Prioritize Data

Governance

Requests

C C I I R

Manage Meta Data C C R R A

Manage Master

Data C R R R A

Manage Data

Quality R C C I A

Implement A I R R I

Audit A I I I R

Communicate R C I I A

Maintain R C I I A

Easier for others to understand

10

Data Governance Policies

"Enterprise" Data Is the Property of the Firm - Data (both structured and unstructured) and the meta-data about that

data are business and technical resources owned by Booz Allen Hamilton, Inc.

Enterprise Data Must Be Managed Efficiently - Every effort must be made by management to eliminate the creation

or maintenance of redundant data without justification. Originating business owner-stewards of data must recognize the

informational needs of downstream processes and business units that may require said data.

Enterprise Data Must Be Modeled - All strategic firm/enterprise data shall be modeled, named, and defined

consistently according to recognized industry standards.

Enterprise Data Must Be Maintained Close to its Source – All firm/enterprise data shall be created and maintained

as close to the source (system or process) as feasible. Data quality standards shall be created to achieve reliability

levels as defined by the business units.

Enterprise Data Must Be Safe and Secured – Firm/enterprise data in all electronic formats shall be in accordance

with existing Firm policy.

Enterprise Data Must Be Accessible as Appropriate – Firm data and information about that data (meta-data) shall

be readily accessible to those in the organization based on their role. When restrictions necessary, business stewards

are accountable for defining specific individuals and levels of access privileges that are to be enabled. Information

Security will be responsible for the implementation of proper security controls, per associated firm policy.

Data Governance Policies have been integrated with other compliance team goals such as Technical Security & Privacy, Regulatory Compliance, Internal Audit, HR Compliance

11 11

Policies Continued…..

Meta-Data Will Be Recorded and Utilized - All Enterprise information system development and integration projects will

utilize a defined meta-data program for data naming, data modeling, and logical and physical database design

purposes.

Data Governance Management is responsible for developing plans to facilitate the capture and recording of specific

data administration-focused meta-data consistent with the defined meta-data program.

Data will be Audited to Provide Assurances of Practice and Reliability – The Data Governance Manager shall

develop a plan to audit and remediate areas where data definitions, use, access, and quality standards have been

implemented to ensure these requirements are consistently met and support the credibility of the firm’s data being

reported to and signed by the firm leadership.

Data Owners & Stewards Will Be Accountable for Enterprise Data – Formal Data Owner and Steward roles will be

assigned to those individuals ultimately responsible for the definition, management, control, integrity or maintenance of

a departmental or firm/enterprise data resource. All Data Stewardship information will be maintained as a form of meta-

data and will be made available to the department through on-line accessibility.

12

Single Version

Of

The Truth

Filename/RPS Number

Developing New Capability

Supporting the dynamic needs of the business

Integrating Data Foundational

Support

Program & Data Governance

Change Management Is Vital & Constant ERA is changing the way the business sources, uses, and understands its data • Working to provide one version of the truth from a centralized source of clean data

• Building brand awareness for Data Governance and its importance

• With consistent definitions of metrics and data elements

• Partnering with the business to prioritize initiatives and improve processes, thereby resulting

consistently reliable data

*Examples later in

presentation

Dashboards

Predictive Analytics

Data Mgt. Tools

Training Course*

Slick Sheet* Reorganization

Due Diligence

Information Access

Resource Mgt.

Executive Metrics

Sales Data

Regulatory Data

Employee Data

Finance Data

Facilities Data

Consistent Reporting

Transactional Analysis

Process Optimization

Upgrades, Phase-Outs &

Stds

Governance Policies

Standards & Practices

Prioritization of Demand

Firm-Wide Data Access Security

13

Ensure existing stakeholders are

documented and published.

Reduce ‘surprise’ impacts to business

Documented pathway to data issue resolution.

Decisions made by the right

people with deep business

knowledge

Evaluate data based on the impact to the

business.

Focus on improvements with greatest

value

Data health reporting

Enterprise Data Dictionary

Security

User Access

Internal Audit

Reg Compliance

Law Dept

Records Mgt

Identify owners/stakeholders of data

Support decision rights & accountabilities

through data stewards, owners and

Governance Committee members

Data process, policy & impacts

Data Quality Management &

Consistent Definitions

Integrate with compliance teams to assure regulations are followed & awareness

is maintained

DATA

GOVERNANCE

• Prioritizing data focus based on impact

• Focusing on logical and secure processes, validations & SLAs for cleaner data

• Providing transparency of data, definitions, and the health of the information

Folding in others to reinforce &

strengthen the program….

Data Governance is working with the business to support its needs for

efficiency and dependability

14

ERA – The Next Business Intelligence Opportunity Maturing the Program to Move up the Pyramid

Showing others the possibilities……….

15

Where do we need to go and grow? Various levels of maturity currently exist across and within business segments

Missing functionality-

Will need to build

Divided ERA into three

lanes – Finance – People Services – Acquisitions

Within ERA, each lane has

different maturity levels

Our goal is to: – Improve and mature our

current reporting – Grow functionality and

tools to move up the pyramid

– Provide certified and consistent management reporting

Develop a game-changing plan of action

16

To support ERA’s maturity goals, we bring together our customers’ needs with program and data governance best

practices.

Enterprise Account Mgt Business Facing

Enterprise Information Mgt Technical Delivery

Program Governance (PMO, & IT)

Council

Approval

ASC, CIO, SIG

Funding

Portfolio Mgt

Socialization

Prioritize

Scope: Parameters

Requirements: What, For Who, When

Design: How

Test: Does it deliver what the customer wants?

Implement: PRODUCTION!

Review: Lessons learned, Unfinished Requirements

Business Need

Demand Capture

Business Case

Data Governance

Data Governance

Matrices

Requirements

Data Profiling

Normalization

Audits

Metrics

KPIs

•Decision Rights

•Accountability/Responsibility

•Expertise

•Business Rules

•Common Definitions

•Data Management Tools & Methods

•Transparency

•Credibility

•Prioritization & Impact

Delivering the firm’s priorities with reliability and transparency

17

Integrating other functions and professional methodologies requires timely engagement and support by the business owner and impacted stakeholders.

Governance – Committees in Action

Data Governance – Making it reliable

Work Products – Support Transparency

& Credibility

•PMO & Demand Management, along with ASC, prioritize larger demand throughout the business

•Departmental Steering Committees help prioritize operational demand within ERA

•Data Governance Committee resolves issues that require executive attention, prioritizes data initiatives, sets standards and guidelines for the firm

• Internal Audit advises team of risks or potential areas of exposure and sensitivity

•Data Governance Sponsor is a strong champion of the benefits to be achieved

•Owners and Stewards responsible for care and feeding of data elements are fully engaged and must sign off on work products

•Data elements are prioritized according to importance and impact to the business

•Business Rules, Processes, Common Definitions are documented, reviewed , and optimized

•Single source of truth/recognized database of record, authoritative lists of subject data,

•Data Quality Reports, SLAs, Correction/feedback loop

•Warehouse Marts & Tables

•Reports and analytics base

•Data Dictionaries

•Process documentation

•Data subject, system & element matrices demonstrating ownership/stewardship/subject matter experts

18

Focusing on the PMO allows us to shape outcomes that are significant to the firm. Specific emphasis has been placed on providing them with tools and templates to be successful and leverage the Data Governance team where it is most needed Data Surety Checklist : The PMO’s ‘bible’ for data changes

18

Key:

Complete

Partially Complete

Incomplete

Yellow highlighted background will designate are of gap (see business process example)

Responsible point of contact reflected in each column in Italics

Project Business Gap or Data Quality

Scope/Definition Process Opportunity Business/Functional Reporting Data Bus Rules Technical Reports Data Dictionary Metrics Dictionary Report Library

PMO &

Stakeholder

BA &

Stakeholder

BA &

Stakeholders BA & Stakeholders

BA, Stakeholders,

Account Mgrs, Technical

SMEs BA & Stakeholders

Tech BA or SA

& Technical

SMEs

Tech BA, SA,

or SMEs BA or Tech BA BA or Tech BA ERA

Boundaries AS-IS

What needs

fixed

What does the

business want and

how should it work Identify data elements What

What should

the system

do? Developed Data Element Data Element/Metric Report Name

Are all

Stakeholders

involved? (Did

you use DG Matrix

on era.bah.com to

validate?) TO-BE

What

question are

we

answering

Gap analysis; recon

process in place

Identify authoritative

source for data When

Gap analysis

work

between

systems Reviewed Definition

Definition/Calculati

on

Content

descriptive

Upsteam

business

process

impacts?

What issue

needs

attention

Any manual

processes

Identify data parameters

- definition of elements

or metrics, expected

calculations, derivations

and associated

dependencies, timing, Why

What will the

technical

team need to

do in order to

meet the

business

requirements

? Tested

Allowable

Values

Owner/Steward/SM

E

Intended

Audience?

How the data

flow will

change.

What data is

of most

impact or

priority

Access

Requirements?

Identify depth/breadth

of Data Warehouse

involvement

How (Under what

circumstance ) Break/Fix

Data Source

(DBOR) Business Signoff

Business

owner/reques

tor/manager?

How the

workflow

will change.

Are we introducing

risks/ jeopardies

with downstream biz

processes or

systems?

Develop report list with

mock-ups for each

report Dependencies

SLA

Established

Data Target

(where will it

reside) SMEs

Owner to

maintain

going fwd

Are we introducing

risks/ jeopardies

with downstream

other projects

planned or in

progress? Is data in warehouse? Scenarios Handoff

Business

Signoff

Is data in single source

system? Validations Owner

Owner

designated

Are there special

analytics and derivations

needed?Are there critical data

point issues that must

be resolved?

DictionaryRequirements

19

Other templates and deliverables to help our PMO succeed are included here, and are a direct adaptation of materials from the Data Governance conferences we have previously attended. We encourage everyone to review our materials and adapt to fit their needs.

Filename/RPS

Number

20

Ris

k

Re

wa

rd

Low

High Low

High Undisciplined Reactive Proactive Governed

Think Locally

Act Locally

Think Globally

Act Locally

Think Locally

Act Collectively

Think Globally

Act Globally

Yr Zero 3-5 yrs 6-10 yrs 10-20yrs

Repeatable

Define

Manage

Optimize

Databases

Datawarehouse

ERP

CRM

CDI

PDM

MDM

BPM

Data Governance Maturity Model: Taking Stock of Where We’ve Come and Where We’re Headed

Initialize

21

Data Governance Organizational Structure Today

ERA Applications

Business Analytics

Seniors

ERA - Account Mgmt


Interface to key business

units

ERA - EIM


Technical Team

Enterprise Systems

Technical Developers

for Source Systems

Business Process

Operational Business

Process Owners

Management

Agreements

Enable Business

Processes for Data

Enable Enterprise

Systems of Data

Enable EIM Architecture for

Data into Information

Enable Interfaces to

Information/analytics

Requests for

Information/analytics

Data Governance Committee

Committee Chair & ERA Director

Data Governance Manager

Finance Director

People Services

Acquisitions Services

Information Services

Data Governance Core Team

Associate & Sr Associate Functional Team

Representatives

Standard Reporting Sub-group CLIENT ANALYTIC SUPPORT LEADS

HR BUSINESS ANALYSTS SENIORS:

CONTRACTS ANALYSTS SENIORS

ERA ACCOUNT MGRS

And so the cycle continues: change, learning, and adaptation are keys to survival Booz Allen continues to evolve and improve efficiencies gained as a result of the program

22

Ensure principles applied consistently across the firm .

**This is a long-term objective requiring constant communication and interaction.

Data Governance Committee Broad perspectives

Subcommittees Multiple perspectives ERA Data Governance Cohesion

Ensure teams are leveraging the governance principles & structures to properly vet issues, ideas, and changes about data.

. Originating business owner-stewards of data must recognize the informational needs of downstream

processes and business units that may require said data. = Solve a problem quickly, with confidence.

Guide internal teams to focus on prioritizing agendas and data integrity based on business need & impact.

Leverage specific people, specific processes & technology to achieve goals.

Include the right stakeholders in decisions around how data is used/input

Create data mapping, rules & definitions … documents necessary to support intelligent actions

ERA Data Governance evolves the framework to ensure

individual teams’ success

23

Maintaining relevance with the PMO: Data Governance Touch Points Throughout the Project Lifecycle

• Leverage Data Governance to ensure:

• Appropriate stakeholder participation throughout project

• Proper vetting of requirements and design documents to ensure data integrity issues are addressed

• Continuous analysis of new metrics/data against existing metrics/data

• Project artifacts should include:

• Data Dictionary

• Metrics Library

• Data/Application Owners and Stewards

• Policies and guidelines for when users need to take data offline for custom reporting / analysis

• Ensure adequate resources/planning for the following data governance related tasks:

• Profile to assess integrity of data

• Testing using data quality scenarios

• Training and Communications plan which indicate significance of using the firm’s direction for consistent information

• Leverage Data Governance to ensure consistent understanding of:

• Data needs

• Authoritative sources

• Functional usage of data

• Frequency of need

• Needs/use beyond requestor

Initiation Planning

Execution Close-out

24

The Slick Sheet

24

ER A : D ata Gov er nance

Data Governance is the formal orchestration of people, process & technology to

enable an organization to leverage data as an enterprise asset & mitigate risk

The ERA Data Governance team drives firm-wide establishment of dependable,

consistent and sustainable information by leveraging resources to execute key tasks

through use of a standard methodology.

Effective Data Governance contributes to:

• Single version of the truth

• Improved bottom line

• Reduced complexity

• Informed management decisions

• Mitigation of risk

• Enable better decision-making through practices that emphasize consistency

• Reduce operational friction by developing dependable and sustainable processes

• Protect the needs of data stakeholders

• Train management and staff to adopt common approaches to data issues

• Build standard, repeatable processes

• Reduce costs and increase effectiveness through coordination of efforts

• Ensure transparency of processes

Booz | Allen | Hamilton

Or ganizat ion: Director

Manager

Analysts

W hat w e do for y ou:

W hy D ata Gov er nance:

Standards

Principles

Resources

Key Tasks

Using multi-channeled branding to underscore change management

25

Continuing to reinforce the brand through Booz Allen’s Corporate University: Data Governance Training

• Incorporating Data Governance concepts as part of the firm’s high-visibility, mandatory compliance training courses

• Presenting a stand-alone virtual Data Governance course (excerpts to follow)

25

“Welcome to

Data Governance at Booz Allen”

26

Data governance is about treating data and information as a critical business asset.

Data governance is the formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset and mitigate risk.

DATA

SYSTEM INFORMATION

critical business asset

EXCERPTED FROM

“DATA GOVERNANCE AT BOOZ ALLEN”

27

Your Role in Data Governance

Your Role

Create Data

Use Data Consume

Data

Business

Process Systems

Reporting &

Analytics

BUSINESS DECISIONS

Identify data owners

Document business processes and systems

information

Establish monitoring measures

EXCERPTED FROM


28

ERA Data Governance

Effective data governance at the firm is supported by ERA Data Governance.

Methodology

• Standards

• Principles

• Key Tasks

• Resources

Consultation

• Provides an overview of

the methodology and

how it applies to your

work

• Consults on less

intensive engagements

• Identifies appropriate

Intellectual Capital and

its application

Support

• Joins project teams for

finite period of time

• Provides subject matter

expertise on data

governance

• Provides Intellectual

Capital that drives

content into the method

for that specific

engagement

EXCERPTED FROM


29

Course Summary

You should now be able to:

Explain how effective data governance benefits the firm

Explain when to leverage the expertise of Enterprise Reporting and Analytics (ERA)

Data Governance to help you implement the data governance methodology

Identify data governance best practices that help to avoid potential risks

Describe the standards, principles, key tasks, and resources that make up the data

governance methodology at Booz Allen

Describe best practices for ensuring dependable,

consistent, and sustainable data

EXCERPTED FROM


30

ERA Data Governance has developed the data governance methodology and

implementation resources.

These resources are available in the Data Governance Library.

EXCERPTED FROM


Documents

Booz Allen Hamilton Submission for Data Governance Best