Booz Allen Hamilton Submission for Data Governance Best Practice Award February 28, 2013 Shyla Kennedy Manager, ERA Data Governance
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
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
Data Governance Status
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 Governance Status
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
Business Intelligence
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
Assists in determining data governance priorities
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
Assists in determining data governance priorities
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
Business Intelligence
Interface to key business
units
ERA - EIM
Business Intelligence
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
“DATA GOVERNANCE AT BOOZ ALLEN”
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
“DATA GOVERNANCE AT BOOZ ALLEN”
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
“DATA GOVERNANCE AT BOOZ ALLEN”
30
ERA Data Governance has developed the data governance methodology and
implementation resources.
These resources are available in the Data Governance Library.
EXCERPTED FROM
“DATA GOVERNANCE AT BOOZ ALLEN”