DataEd Online: Unlock Business Value through Data Governance

Copyright 2013 by Data Blueprint

Unlock Business Value through Data Governance

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• If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.



Date: April 9, 2013Time: 2:00 PM ETPresented by: Peter Aiken, PhD

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If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.


Commonly Asked Questions

1) Will I get copies of the slides after the event?

2) Is this being recorded so I can view it afterwards?

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Meet Your Presenter: Peter Aiken, Ph.D.

• Internationally recognized thought-leader in the data management field - 30 years of experience– Recipient of multiple international awards– Founder, Data Blueprint – 7 books and dozens of articles

• Experienced w/ 500+ data management practices in 20 countries

• Multi-year immersions with organizations as diverse as the US DoD, Deutsche Bank, Nokia, Wells Fargo, and the Commonwealth of Virginia

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• #nowthatcherisdead

• #now thatcher is dead

• #now that cher is dead

• #now t hatcher is dead


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Motivation


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• Context: What is Data Management/DAMA/DM BoK/CDMP?

• What is Data Governance and why is it Important?– Organizational -> IT -> Data– Requirements for Effective Data

Governance• Data Governance

– Frameworks– Checklists – Worst Practices– Building Blocks

• Data Governance in Action:– Securities example– Retail example

• Take Aways/References/Q&A



Tweeting now: #dataed

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• What is Data Governance and why is it Important?– Organiza*onal -‐> IT -‐> Data– Requirements for Effec*ve Data Governance

• Data Governance – Frameworks– Checklists – Worst Prac*ces– Building Blocks

• Data Governance in Action:– Securi*es example– Retail example











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Data Management is an Integrated System of Five Practice Areas

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#dataed


Five Integrated DM Practices

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Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship Data Development

Data Support Operations

#dataed


• 5 Data Management Practices Areas / Data Management Basics

• Are necessary but insufficient prerequisites to organizational data leveraging applications (that is Self Actualizing Data or Advanced Data Practices)

Data Management Practices Hierarchy (after Maslow)

Basic Data Management Practices– Data Program Management– Organizational Data Integration– Data Stewardship– Data Development– Data Support Operations

http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png

Advanced Data Practices• Cloud• MDM• Mining• Analytics• Warehousing• Big

• Published by DAMA International– The professional association for Data

Managers (40 chapters worldwide)– DMBoK organized around – Primary data management functions

focused around data delivery to the organization (more at dama.org)

– Organized around several environmental elements

• CDMP– Certified Data Management Professional– DAMA International and ICCP– Membership in a distinct group made up of

your fellow professionals– Recognition for your specialized knowledge

in a choice of 17 specialty areas– Series of 3 exams– For more information, please visit:

• http://www.dama.org/i4a/pages/index.cfm?pageid=3399 • http://iccp.org/certification/designations/cdmp


DAMA DM BoK & CDMP

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#dataed

Data Management Func-ons










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Data Strategy in Context

Organiza)onal

IT Strategy

Data StrategyOnly 1 is 10 organiza/ons has a board approved data

strategy!


Corporate Governance• "Corporate governance - which can be

defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society….", Financial Times, 1997.

• "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World Bank, President Financial Times, June 1999.

• “Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment”, The Journal of Finance, Shleifer and Vishny, 1997.

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Definition of IT Governance• IT Governance: • "putting structure around how organizations align IT strategy with

business strategy, ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT’s performance.

• It makes sure that all stakeholders’ interests are taken into account and that processes provide measurable results.

• An IT governance framework should answer some key questions, such as how the IT department is functioning overall, what key metrics management needs and what return IT is giving back to the business from the investment it’s making." CIO Magazine (May 2007)

According to the IT Governance Institute, there are five areas of focus: • Strategic Alignment• Value Delivery• Resource Management• Risk Management• Performance Measures

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No clear connection exists between to business priorities and IT initiatives

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Grow expenses slower than

sales

Grow operating income faster

than sales

Pass on savings

Drive efficiency with technology

Leverage scale globally

Leverage expertise

Deploy new formats

Grow productivity of existing assets

Attract new members

Expand into new channels

Enter new markets

Make acquisitions

Produce significant free

cash flow

Drive ROI performance

Deliver greater shareholder

value

Cus

tom

er

Per

spec

tive Open new

stores

Develop new, innovative formats

Appeal to new demographics

Integrate shopping

experience

Develop new, innovative formats

Remain relevant to all

customers

Increase "Green" Image

Inte

rnal

P

ersp

ectiv

e

Create competitive advantages

Improve use of information

Strengthen supply chain

Improve Associate

productivity

Making acquisitions

Increase benefit from our global expertise

Present consistent view and

experience

Integrate channels Match staffing

to store needs Increase sell through

Fina

ncia

l P

ersp

ectiv

e Reduce expenses

Inventory Management

Human and Intell. Capital investment

Manage new facilities

Improve Sales and margin by facilities

Increased member-base

revenues

Revenue growth Cash flow Return on

Capital

Walmart Strategy Map

See more uniform brand and retail experience

Leverage Growth Return

Gross Margin Improvement

CE

O P

ersp

ectiv

e

Attract more customers & have customer purchasing more

Associate Productivity

Customer Insights

Human Capital Corp. Reputation Acquisition Strategic Planning

Real estate CRM CRM

Analytic and reporting processes

Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance

Corporate Processes

Corporate Data

Inventory Mgmt

Tran

sfor

mat

ion

Port

folio

Supply Chain

Multi ChannelMerchant Tools Supply Chain

Strategic Initiatives

AcctingSales

Transactional Processing

Logistics Associate Locations and Codes

Item

Customer Suppliers

Retail Planning

( Alignment Gap )

Adapted from John Ladley


7 Data Governance Definitions• The formal orchestration of people, process, and technology to enable an

organization to leverage data as an enterprise asset. - The MDM Institute• A convergence of data quality, data management, business process management,

and risk management surrounding the handling of data in an organization – Wikipedia

• A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute

• The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting

• A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information – IBM Data Governance Council

• Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions – Sunil Soares

• The exercise of authority and control over the management of data assets – DM BoK

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Organizational Data Governance Purpose Statement

• What does data governance mean to my organization?

– Getting some individuals (whose opinions matter)

– To form a body (needs a formal purpose/authority)

– Who will advocate/evangelize for (not dictate, enforce, rule)

– Increasing scope and rigor of

– Data-centric development practices

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Data Governance from the DMBOK

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Data Governance from the DMBOK

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Decision Making Needs

Data Quality/Inventory Management

Organizational Strategy Formulation/Implementation

Operational Data Delivery Performance

Data Security Planning/Implementation


What is the Difference Between DG and DM?

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• Data Governance– Policy level guidance– Setting general guidelines and direction– Example: All information not marked public

should be considered confidential• Data Management

– The business function of planning for, controlling and delivering data/information assets

– Example: Delivering data to solve business challenges


Why is Data Governance Important?Cost organizations millions each year in

• Productivity

• Redundant and siloed efforts

• Poorly thought out hardware and software purchases

• Reactive instead of proactive initiatives

• Delayed decision making using inadequate information

• 20-40% of IT spending can be reduced through better data governance

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5 Requirements for Effective DGData governance is a set of well-defined policies and practices designed to ensure that data is:1. Accessible

– Can the people who need it access the data they need? – Does the data match the format the user requires?

2. Secure– Are authorized people the only ones who can access the data? – Are non-authorized users prevented from accessing it?

3. Consistent– When two users seek the "same" piece of data, is it actually the same data? – Have multiple versions been rationalized?

4. High Quality– Is the data accurate? – Has it been conformed to meet agreed standards

5. Auditable– Where did the data come from? – Is the lineage clear? – Does IT know who is using it and for what purpose?

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Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160

• Integrity• Accountability• Transparency• Strategic alignment• Standardization• Organizational change

management • Data architecture • Stewardship/Quality• Protection










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Getting Started

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Assess context

Define DG roadmap

Secure executive mandate

Assign Data Stewards

Execute plan

Evaluate results

Revise plan

Apply change management

(Occurs once) (Repeats)

ClassificationNames

ModelNames

*Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative rela-tionships connecting every cell horizontally potentially exist.

AudiencePerspectives

EnterpriseNames

ClassificationNames

AudiencePerspectives

© 1987-2011 John A. Zachman, all rights reserved. Zachman® and Zachman International® are registered trademarks of John A. Zachman

™

C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations

C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations

C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations

Alignment

Transformations

Version 3.0

A l i g n m e n t

A l i g n m e n t

How Where Who WhenWhat Why

ProcessFlows

DistributionNetworks

ResponsibilityAssignments

TimingCycles

InventorySets

MotivationIntentions

Operations

Instances

(Implementations)

TheEnterprise

TheEnterprise

Enterprise

Perspective

(Users)

Executive

Perspective

(Business ContextPlanners)

Business Mgmt

Perspective

(Business Concept Owners)

Architect

Perspective

(Business LogicDesigners)

Engineer

Perspective

(Business Physics Builders)

Technician

Perspective

(Business ComponentImplementers)

Scope

Contexts

(Scope Identification Lists)

Business

Concepts

(Business Definition Models)

System

Logic

(SystemRepresentation Models)

Technology

Physics

(TechnologySpecification Models)

Tool

Components

(Tool Configuration Models)

e.g. e.g. e.g. e.g. e.g. e.g.



e.g. e.g. e.g. e.g. e.g. e.g.e.g.: primitive e.g.: composite model:

model:

Forecast Sales

Plan Production

Sell Products

Take Orders

Train Employees

Assign Territories

Develop Markets

Maintain Facilities

Repair Products

Record Transctns

Material Supply Ntwk

Product Dist. Ntwk

Voice Comm. Ntwk

Data Comm. Ntwk

Manu. Process Ntwk

2I¿FH�:UN�)ORZ�1WZN

Parts Dist. Ntwk

Personnel Dist. Ntwk

etc., etc.

General Mgmt

Product Mgmt

Engineering Design

Manu. Engineering

Accounting

Finance

Transportation

Distribution

Marketing

Sales

Product Cycle

Market Cycle

Planning Cycle

Order Cycle

Employee Cycle

Maint. Cycle

Production Cycle

Sales Cycle

Economic Cycle

Accounting Cycle

Products

Product Types

:DUHKRXVHV

Parts Bins

Customers

Territories

Orders

Employees

Vehicles

Accounts

New Markets

Revenue Growth

Expns Reduction

Cust Convenience

Customer Satis.

Regulatory Comp.

New Capital

Social Contribution

Increased Yield

Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g.

Operations TransformsOperations In/Outputs

Operations LocationsOperations Connections

Operations RolesOperations Work Products

Operations IntervalsOperations Moments

Operations EntitiesOperations Relationships

Operations EndsOperations Means

Process

Instantiations

Distribution

Instantiations

Responsibility

Instantiations

Timing

Instantiations

Inventory

Instantiations

Motivation

Instantiations

List: Timing Types

Business IntervalBusiness Moment

List: Responsibility Types

Business RoleBusiness Work Product

List: Distribution Types

Business LocationBusiness Connection

List: Process Types

Business TransformBusiness Input/Output

System TransformSystem Input /Output

System LocationSystem Connection

System RoleSystem Work Product

System IntervalSystem Moment

Technology TransformTechnology Input /Output

Technology LocationTechnology Connection

Technology RoleTechnology Work Product

Technology IntervalTechnology Moment

Tool TransformTool Input /Output

Tool LocationTool Connection

Tool RoleTool Work Product

Tool IntervalTool Moment

List: Inventory Types

Business EntityBusiness Relationship

System EntitySystem Relationship

Technology EntityTechnology Relationship

Tool EntityTool Relationship

List: Motivation Types

Business EndBusiness Means

System EndSystem Means

Technology EndTechnology Means

Tool EndTool Means

Timing IdentificationResponsibility IdentificationDistribution IdentificationProcess Identification

Timing DefinitionResponsibility DefinitionDistribution DefinitionProcess Definition

Process Representation Distribution Representation Responsibility Representation Timing Representation

Process Specification Distribution Specification Responsibility Specification Timing Specification

Inventory Identification

Inventory Definition

Inventory Representation

Inventory Specification

Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration

Motivation Identification

Motivation Definition

Motivation Representation

Motivation Specification

Motivation Configuration


Data Governance Frameworks

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• A system of ideas for guiding analyses

• A means of organizing project data

• Data integration priorities decision making framework

• A means of assessing progress


Data Governance Institute

-‐ datablueprint.com 1/26/2010 © Copyright this and previous years by Data Blueprint -‐ all rights reserved!8 http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/


KiK Consulting

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http://www.kikconsulting.com/


IBM Data Governance Council

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http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html


Elements of Effective Data Governance

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See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.


American College Personnel Association

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Data Governance from the DM BoK

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Illustration from The DAMA Guide to the Data Management Body of Knowledge p. 37 © 2009 by DAMA International


NASCIO DG Implementation Process

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NASCIO Scorecard

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Data Governance Checklist• The Privacy Technical Assistance

Center has published a new checklist “to assist stakeholder organizations, such as state and local education agencies, with establishing and maintaining a successful data governance program to help ensure the individual privacy and confidentiality of education records.”

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Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198

• The five page paper offers a number of suggestions for implementing a successful data governance program that can be applied to a variety of business models beyond education.

• For more information, please visit the Privacy Technical Assistance Center: http://ed.gov/ptac


Data Governance Checklist• Decision-Making Authority

– Assign appropriate levels of authority to data stewards– Proactively define scope and limitations of that authority

• Standard Policies and Procedures– Adopt and enforce clear policies and procedures in a written data

stewardship plan to ensure that everyone understands the importance of data quality and security

– Helps to motivate and empower staff to implement DG

• Data Inventories– Conduct inventory of all data that require protection– Maintain up-to-date inventory of all sensitive records and data systems– Classify data by sensitivity to identify focus areas for security efforts

• Data Content Management– Closely manage data content to justify the collection of sensitive data,

optimize data management processes and ensure compliance with federal, state, and local regulations

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Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198


Data Governance Checklist, cont’d• Data Records Management

– Specify appropriate managerial and user activities related to handling data to provide data stewards and users with appropriate tools for complying with an organization’s security policies

• Data Quality– Ensure that data are accurate, relevant, timely, and complete for their intended

purposes– Key to maintaining high quality data is a proactive approach to DG that requires

establishing and regularly updating strategies for preventing, detecting, and correcting errors and misuses of data

• Data Access– Define and assign differentiated levels of data access to individuals based on

their roles and responsibilities – This is critical to prevent unauthorized access and minimize risk of data breaches

• Data Security and Risk Management– Ensure the security of sensitive and personally identifiable data and mitigate the

risks of unauthorized disclosure of these data– Top priority for effective data governance plan

42Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198


Largely Ineffective DG Investments

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• Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their DM investments.

• Only 30% of DM investments achieve tangible returns at all.

• Seventy percent of organizations have very small or no tangible return on their DM investments.


Data Governance Goals and Principles• To define, approve, and communicate

data strategies, policies, standards, architecture, procedures, and metrics.

• To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures.

• To sponsor, track, and oversee the delivery of data management projects and services.

• To manage and resolve data related issues.

• To understand and promote the value of data assets.

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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

• Understand Strategic Enterprise Data Needs

• Develop and Maintain the Data Strategy

• Establish Data Professional Roles and Organizations

• Identify and Appoint Data Stewards

• Establish Data Governance and Stewardship Organizations

• Develop and Approve Data Policies, Standards, and Procedures

• Review and Approve Data Architecture

• Plan and Sponsor Data Management Projects and Services

• Estimate Data Asset Value and Associated Costs


Data Governance Activities

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Data Governance Primary Deliverables• Data Policies

• Data Standards

• Resolved Issues

• Data Management Projects and Services

• Quality Data and Information

• Recognized Data Value46



Data Governance Roles and ResponsibilitiesParticipants:• Executive Data Stewards• Coordinating Data Stewards• Business Data Stewards• Data Professionals• DM Executive• CIO

Suppliers:• Business Executives• IT Executives• Data Stewards• Regulatory Bodies

Consumers:• Data Producers• Knowledge Workers• Managers and Executives• Data Professionals• Customers

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Data Governance Technologies• Intranet Website

• E-Mail

• Metadata Tools

• Metadata Repository

• Issue Management Tools

• Data Governance KPI Dashboard48



Data Governance Practices and Techniques• Data Value• Data Management

Cost• Achievement of

Objectives• # of Decisions Made• Steward Representation/Coverage• Data Professional Headcount• Data Management Process Maturity

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Data Governance Examples, cont’d

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Formalizing the Role of U.S. Army IT Governance/Compliance

Suicide Mitigation


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Suicide MitigationData Mapping

12

Mental illness

Deployments

Work History

Soldier Legal Issues

Abuse

Suicide Analysis

FAPDMSS G1 DMDC CID

Data objects complete?

All sources identified?

Best source for each object?

How reconcile differences between sources?

MDR


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Senior Army Official

• A very heavy dose of management support

• Any questions as to future data ownership, "they should make an appointment to speak directly with me!"

• Empower the team– The conversation turned from "can this be

done?" to "how are we going to accomplish this?"

– Mistakes along the way would be tolerated– Implement a workable solution in prototype form

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Communication Patterns

56Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010


Example of Poor Data Governance

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Mizuho Securities Example• Wanted to sell 1 share for

600,000 yen• Sold 600,000 shares for 1

yen• $347 million loss• In-house system did not have

limit checking• Tokyo stock exchange

system did not have limit checking

• And doesn't allow order cancellations

CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000).


Diaper Story

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Old New

Shipping Semi BestTerms 2/10 net 30 ?Turns 5 50Risks same JIT










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Take Aways• Need for DG is increasing• DG is a new discipline

– Must conform to constraints– No one best way

• Comparing DG frameworks can be useful• DG directs data management efforts• DG interacts directly and indirectly with the

organization• Process improvement can improve DG

practices

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10 DG Worst Practices in Detail1. Buy-in but not Committing:

Business vs. IT– Business needs to do more– Data governance tasks need

to recognized as priority– Without a real business-resource commitment, data governance

takes a backseat and will never be implemented effectively

2. Ready, Fire, Aim– Good: Create governance steering committee

(business representatives from across enterprise) and separate governance working group (data stewards)

– Problem: Often get the timing wrong: Panels are formed and people are assigned BEFORE they really understand the scope of the data governance and participants’ roles and responsibilities

– Prematurely organize management framework and realize you need a do-over = Guaranteed way to stall DG initiative

62Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895


10 DG Worst Practices in Detail3. Trying to Solve World Hunger or Boil the Ocean

• Trap 1: Trying to solve all organizational data problems in initial project phase

• Trap 2: Starting with biggest data problems (highly political issues)• Almost impossible to establish a DG program while tacking data

problems that have taken years to build up• Instead: “Think globally and act locally”: break data problems down

into incremental deliverables• “Too big too fast” = Recipe for disaster

4. The Goldilocks Syndrome• Encountering things that are either one

extreme or another• Either the program is too high-level and

substantive issues are never dealt with or it attempts to create definitions and rules for every field and table

• Need to find happy compromise that enables DG initiatives to create real business value

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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895


10 DG Worst Practices in Detail5. Committee Overload

• Good: People of various business units and departments get involved in the governance process

• Bad: more people -> more politics -> more watered down governance responsibilities

• To be successful, limit committee sizes to 6-12 people and ensure that members have decision-making authority

6. Failure to Implement• DG efforts won’t produce any business value if

data definitions, business rules and KPIs are created but not used in any processes

• Governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon, and changed when appropriate

• Also important: Establish ongoing communication about governance to prevent business users going back to old habits

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10 DG Worst Practices in Detail7. Not Dealing with Change Management

• Business and IT processes need to be changed for enterprise DG to be successful

• Need for change management is seldom addressed• Challenges: people/process issues and internal

politics 8. Assuming that Technology Alone is the Answer

• Purchasing MDM, data integration or data quality software to support DG programs is not the solution

• Combination of vendor hype and high price tags set high expectations

• Internal interactions are what make or break data governance efforts

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10 DG Worst Practices in Detail9. Not Building Sustainable and Ongoing

Processes• Initial investment in time, money

and people may be accurate• Many organizations don’t establish a budget, resource

commitments or design DG processes with an eye toward sustaining the governance effort for the long term

10.Ignoring “Data Shadow Systems”• Common mistake: focus on “systems

of record” and BI systems, assuming that all important data can be found there

• Often, key information is located in “data shadow systems” scattered through organization

• Don’t ignore such additional deposits of information

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ReferencesWebsites

• Data Governance Book

Data Governance Book

Compliance Book

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IT Governance Books

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Questions?

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Transcript of DataEd Online: Unlock Business Value through Data Governance