data governance assessment: essential for success...© 1997 to present, enterprise warehousing...

© 1997 to present, Enterprise Warehousing Solutions, Inc. (EWSolutions) – 1

Over 155 Successful Client Partner Implementations

Contact us at [email protected] to become our next success

Data Governance Assessment:

Essential for Success By Dr. David P. Marco, Fellow IIM, CDMP (Master), CDP, CBIP

President

EWSolutions

mailto:[email protected]




EWSolutions’ BackgroundEWSolutions is a Chicago-headquartered strategic partner and full life-cycle systems integrator

providing both award winning strategic consulting and full-service implementation services. This

combination affords our client partners a full range of services for any size enterprise information

management, metadata management, data governance and data warehouse/business intelligence

initiative. Our notable client partner projects have been featured in the Chicago Tribune, Federal

Computer Weekly, Journal of the American Medical Informatics Association (JAMIA), Crain’s Chicago

Business, FBI, The Doings and won the 2004 Intelligent Enterprise’s RealWare award, 2007

Excellence in Information Integrity Award nomination, DM Review’s 2005 World Class Solutions

award, 2016 CIO Review 20 Most Promising Enterprise Architecture providers and 2018 Innovation

award.

For more information on our Strategic Consulting Services, Implementation Services, or World-

Class Training, email us at [email protected]

Best Business Intelligence Application

Information Integration

Client: Department of Defense

World Class

Solutions Award

Data Management

2007 Excellence in Information

Integrity Award Nomination

FBI

Meritorious

Service Award

2018: Most Innovative in

Data Management Solutions Excellence and

Thought Leadership


http://www.ewsolutions.com/news-events/news-press-releases/press_release.2005-10-03.9140171518




Over 155 Client Partners Since 1997

For more information on our Strategic Consulting Services, Implementation Services,

or World-Class Training email us at [email protected]

AFLACAgVantis

AON (Hewitt Associates)Arizona Supreme Court

Bank of America (Countrywide)Bank of Montreal

BankUnited

Basic American FoodsBecton, Dickinson and Company

Blue Cross Blue Shield AssociationBlue Cross Blue Shield companies

Booz Allen Hamilton

Branch Banking & Trust (BB&T)British Petroleum (BP)

California DMVCalifornia State Fund

Canadian Institute for Health Information (CIHI)Canadian National Railway

Capella University

CignaCollege Board

ComcastCorning Cable Systems

Contech Systems, Inc.

CostCoD.A. Davidson

Defense Logistics Agency (DLA)Delta Dental

Department of Defense (DoD)Department of the Interior

DiscoverDriehaus Capital Management

Eli Lilly and CompanyEnvironment Protection Agency (EPA)

ErwinFannie Mae

Farmers Insurance Group

Federal Aviation AdministrationFederal Bureau of Investigation (FBI)

FedExFEMA

Fidelity Information Services

FICO (Fair Issac)Ford Motor Company

GlaxoSmithKlineHarbor Funds

Harris Bank (BMO)The Hartford

Harvard Pilgrim HealthCare

Health Care Services CorporationHP (Hewlett-Packard)

IDMA (Insurance Data Mgt. Institute)Information Resources Inc.

International Paper

IQVIA (Quintiles)Janus Mutual Funds

Johnson ControlsKey Bank

LiquidNet

SurescriptsTarget Corporation

Texas CPAThe Regence Group

Thomson Multimedia (RCA)Thrivent Financial

United Healthcare (UHC)

United Health GroupUnited Nations (ICAO)

United States Air ForceUnited States Federal Courts

United States Department of State

United States Fish and Wildlife ServiceUnited States Navy

United States Navy (BUPERS)United States Transportation Command

University of MichiganUniversity of Wisconsin Health

USAA

US CellularVera Bradley

Waste ManagementWells Fargo

Wisconsin Department of Transportation

Workers’ Compensation Insurance Rating Bureau of California (WCIRB)Zurich Cantonal Bank

Loyola Medical CenterManulife Financial (John Hancock Fin.)

Mayo ClinicMcDonalds

MicronMicrosoft

MoneyGram

NASANational City Bank (PNC Financial)

NationwideNeighborhood Health Plan

NORC

Physicians Mutual InsurancePillsbury

Quartz BenefitsRowan College of South Jersey (RCSJ)

Sallie MaeSaudi Arabia Minister of the Interior

Saudi Telecom Company (STC)

Schneider NationalSecretary of Defense/Logistics

Singapore Defense Science & Technology Agency (DSTA)Social Security Administration

South Orange County Community College

SpherionStandard Bank of South Africa

SunTrust Bank





Dr. David P. Marco – Professional Profile

Best known as the world’s foremost authority on metadata management and the father of the Managed Metadata Environment, he is an internationally recognized expert in the fields of data governance, digital transformation, data literacy, big data, data warehousing, master data management and data management. In 2004 Dr. David P. Marco was named the “Melvil Dewey of Metadata” by Crain’s Chicago Business as he was selected to their very prestigious “Top 40 Under 40” list. Dr. Marco has authored several books including the widely acclaimed “Universal Metadata Models” (Wiley, 2004) and the classic “Building and Managing the Metadata Repository: A Full Life-Cycle Guide” (Wiley, 2000).

❑ 2020 received a lifetime professional Fellowship from the Institute of Information Management (their highest honor)

❑ President of Data Management University (DataManagementU.com)

❑ Author of several best-selling information technology books, including the top 2 sellers in metadata management history

❑ 2016 Data Management Channel Expert for Business Analytics Collaborative

❑ 2008 DAMA Data Management Hall of Fame (Professional Achievement Award)

❑ 2007 DePaul University named him one of their “Top 14 Alumni Under 40”

❑ Selected to the prestigious 2004 Crain’s Chicago Business “Top 40 Under 40”

❑ Worked with over 90 client partners to successfully build their data management & governance programs

❑ Presented hundreds of keynotes/seminars across four continents

❑ Published hundreds of IT articles some of which were translated into Mandarin, Russian, Italian, Portuguese and others

❑ Taught at the University of Chicago and DePaul University

❑ Holds PhD, Fellow IIM, CDMP (Master), CDP, CCP and CBIP certifications

Email: [email protected]


http://lnkd.in/bky55wQ




Data Governance

Assessment





Assessment Basics





Data Governance Assessment

❑ It’s an industry best practice to conduct a data governance

assessment before launching a data governance program

❑ A Data Governance Assessment evaluates an organizations

readiness to embark on a data governance program. The

assessment provides an accurate current state (AS-IS) view of

data governance as well as a future / recommended state (TO-

BE) of data governance maturity that the organization can strive

for






❑A data governance assessment needs to be fair, avoid bias,

accurate, and tailored to the organization

❑A data governance assessment has 3 major sections:

➢Current State Analysis (AS-IS)

➢Future State Recommendation (TO-BE)

➢Recommendation Roadmap





Current State Analysis (AS-IS)






❑ The Current State Analysis contains Maturity Levels and Environmental Elements

❑ Environmental Elements are the things that will be evaluated during the assessment

❑ For a data governance assessment, Environmental Elements include:

➢ Activities & Deliverables: that support the data governance program or are involved in data governance practices being conducted

➢ Roles & Organization: that have been defined and implemented by the entity to support data governance

➢ Practices & Metrics: that have been implemented to measure the performance of the data governance program

➢ Technology: that has been implemented or purchased that support the activities of the data governance program

➢ Skills & Training: that the data governance professional staff and data stewards have received

➢ Culture: of the organization and how the value of data is viewed and the concern the entity has for managing their data correctly





Data Governance

Maturity Levels






❑Maturity Levels provide an approach for measuring / ranking

various Environmental Elements that will be assessed

❑The maturity levels presented here are used by EWSolutions

and are roughly based on the Capability and Maturity Model

(CMM) created by Carnegie Mellon University

Level 1Informal Processes

Level 2Emerging Processes

Level 3Engineered Processes

Level 4Controlled Processes

Level 5Optimizing Processes

Since 2010 © EWSolutions

All Rights Reserved





Level 1 – Informal Processes❑ Executive Management Awareness of Data Governance: Some, but no enterprise buy-in on the need and value of data

governance

❑ Enterprise Data Management: No formal program or professionals

❑ Data Responsibilities: are assigned across separate IT groups

❑ Business Involvement in Data Governance: Little or none

❑ Enterprise Data Governance Processes: None or minimal

❑ Data Governance Structures: ones that exist are redundant without an enterprise approach

❑ Data Governance Team: None or it is assigned to 1 person

❑ Data Governance Skills: No professional data governance practitioners leading the team

❑ Data Stewards: Formal data stewards are not defined or identified

❑ Data Governance Tools: None. General purpose tools used as point solutions

❑ Metadata Management: No formal and enterprise approach for managing metadata

❑ Metadata Management Team: None or 1 person without the needed skills

❑ How Data Quality Problems are Solved: Heroic / reactive action is typical

❑ Data Cleansing: Minimal with no enterprise approach

❑ Enterprise Data Dictionary: does not exist

❑ Data: Redundant and undocumented, with differing definitions

❑ Business Metadata: Little or none

❑ Semantics: Diverging with rhyme or reason

❑ Data integration: performed by individuals with no enterprise approach

Level 1Informal Processes





Level 2 – Emerging Processes❑ Executive Management Awareness of Data Governance: Some executives understand and are willing to invest, but no enterprise buy-in

❑ Enterprise Data Management: Maybe a program exists but it is not enterprise wide and most likely underfunded

❑ Data Responsibilities: are assigned to an enterprise IT team; however, it lacks the proper business involvement

❑ Business Involvement in Data Governance: Some departments work with the data governance team; however, not all major departments do

❑ Enterprise Data Governance Processes: Some are starting to appear; however, they may only be built for 1 or 2 departments

❑ Data Governance Structures: there maybe some structures emerging; however, they are departmental or are under funded

❑ Data Governance Team: Maybe 1 or 2 people

❑ Data Governance Skills: Probably 1 professional data governance practitioner without a supporting team of qualified professionals

❑ Data Stewards: Formal data stewards are defined and identified; however, they may only cover 1 or 2 departments with no enterprise structure

above them

❑ Data Governance Tools: Possibly an enterprise data governance tool has been purchased; however, the organization does not have developers

able to properly use the tool or the tool has not been implemented at an enterprise level

❑ Metadata Management: Metadata is starting to be actively managed; however, the program is under funded and not at an enterprise level. No fully

functional managed metadata environment

❑ Metadata Management Team: 1 or 2 people, with only 1 having the proper background

❑ How Data Quality Problems are Solved: Some departments have implemented sound approaches and DQM frameworks

❑ Data Cleansing: Some but it lacks an enterprise approach

❑ Enterprise Data Dictionary: Several may exist rather than an enterprise agreed to, single data dictionary

❑ Data: Redundancies are starting to be reduced. Some documentation with some enterprise definitions

❑ Business Metadata: Some is being defined and managed

❑ Semantics: Some departmental semantics are emerging

❑ Data integration: performed by a centralized group that lacks an enterprise approach

Level 2Emerging Processes





Level 3 – Engineered Processes❑ Executive Management Awareness of Data Governance: The majority of executives understand and are willing to invest with enterprise buy-in

❑ Enterprise Data Management: There is a formal, enterprise data management program that is fully funded

❑ Data Responsibilities: are assigned to an enterprise IT team that directly works with the business in partnership

❑ Business Involvement in Data Governance: All major departments are actively working with the data governance team

❑ Enterprise Data Governance Processes: Some significant, enterprise processes have been implemented

❑ Data Governance Structures: A fully funded, enterprise data governance structure and framework exist

❑ Data Governance Team: 3 or more data governance professionals are on the team

❑ Data Governance Skills: At least 3 professional data governance practitioners with strong skill sets are assigned to the data governance team

❑ Data Stewards: Formal, enterprise data stewards are defined and identified across the organization, including geographic boundaries

❑ Data Governance Tools: An enterprise data governance tool has been purchased and has a proper development team with the right skills

implementing and maintaining it at an enterprise level

❑ Metadata Management: Metadata is actively managed with a fully functional, enterprise managed metadata environment. Capabilities like

enterprise impact analysis and global definitional lookups have been implemented

❑ Metadata Management Team: 2 or more metadata management professionals have been hired / trained

❑ How Data Quality Problems are Solved: There is an enterprise approach and an enterprise data quality management team in place, using sound

DQM frameworks

❑ Data Cleansing: Enterprise approach

❑ Enterprise Data Dictionary: There is a single, enterprise data dictionary that exists in the managed metadata environment

❑ Data: Redundancies have been dramatically reduced as has the entire IT footprint. Global and well understood enterprise definitions

❑ Business Metadata: Enterprise business metadata has been defined and is being managed

❑ Semantics: Enterprise semantics have emerged and are being used

❑ Data integration: performed by a centralized group with an enterprise approachLevel 3

Engineered Processes





Level 3 – Engineered Processes

❑ Active executive Involvement across the enterprise

❑ Ongoing, clearly defined business data stewardship

❑ Central Data Management organization

❑ Standard processes, metrics, and tools used enterprise wide

❑ Enterprise data architecture guides implementations

❑ Centralized metadata management

❑ Quality SLA’s are defined and monitored regularly

❑ Commitment to continual skills development

❑ Periodic audits and proactive monitoring

Level 3Engineered Processes





Level 4 – Controlled Processes

❑ Levels 4 and 5 assumes all of the capabilities provided in Level 3

❑ At this level we are analyzing the structures, processes, performance, functions, and

capabilities built in the earlier levels

❑ Measurable process goals are established for each defined process

❑ Measurements are collected and analyzed

❑ Quantitative (measurement) analysis of each process occurs

❑ Beginning to predict future performance

➢ Historical metadata to perform trend analysis is maintained

❑ Defects are proactively identified and corrected

❑ Quality SLA’s (service level agreement) are defined and monitored regularly

❑ Performance audits are regularly conducted

❑ Ongoing skills development is occurring Level 4Controlled Processes





Level 5 – Optimizing Processes

❑ Level 5 builds on the work performed and the achievements made in Levels 3 and 4

❑ Quantitative and qualitative understanding used to continually improve each

process

❑ Organization is continually optimizing its data governance structures, processes,

functions and capabilities

❑ Understanding of how each process contributes to the business strategies and goals

of the enterprise

❑ Ongoing, standardized metrics and key performance indicators (KPIs) are stored

and managed over time in the managed metadata environment

Level 5Optimizing Processes





Define Future Data Governance State (TO-BE)

❑Identify reasonable, achievable and valuable maturity

targets

❑Determine roadmap for achieving maturity targets






❑ The assessment process

includes interviews and expert

analysis to rank/grade an

organization’s maturity level and

environmental elements.





Identify Reasonable Data Governance Maturity Targets

❑It’s not reasonable to go from Level 1 to Level 3 in one program

iteration

❑Several step process

➢Corporate Objectives as defined by the CEO

➢Pain points

➢Cost / benefit analysis driven by requirements gathering

➢Common data governance targets

➢Use EWSolutions’ Data Governance Maturity Levels as a guide





Roadmap





Assessment Roadmap

❑Based on the maturity targets, a set of recommendations needs

to be created

❑These recommendations needs to focus on leading the

organization to a sound data governance foundation

❑A Roadmap presents a set of recommendations that, if

achieved, will allow the organization to reach its maturity targets





Assessment Roadmap

❑A Data Governance Roadmap may include some or all of the following recommendations:➢ Educate Key Executives

➢ Attain Executive Sponsors

➢ Define an Enterprise Data Strategy

➢ Provide Formal Data Governance Training to Key Staff Members

➢ Build a Domain Model

➢ Hire a Data Governance Program Manager

➢ Form a Data Governance Council

➢ Develop Foundational Metadata Management Capability

➢ Stand Up 2 Data Steward Domain Groups





Questions & Answers

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