The Case for the Chief Data Officer - DAMA NY...The Case for the Chief Data Officer Recasting the...

Building the Data-driven Enterprise

Presented by Peter Aiken, Ph.D.10124 W. Broad Street, Suite C

Glen Allen, Virginia 23060804.521.4056

The Case for the Chief Data Officer

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

Copyright 2013 by Data Blueprint

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• 30+ years of experience in data management

• Multiple international awards & recognition

• Founder, Data Blueprint (datablueprint.com)

• Associate Professor of IS, VCU (vcu.edu)

• (Past) President, DAMA Int. (dama.org)

• 9 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, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia

Peter Aiken, Ph.D.




• 88 pages, 16,000 words• Not written for you• It is for your boss' boss• Must make fundamental

changes to the c-suite for data (especially big data) to succeed

• #1,097,268 in books at amazon.com

• #390,300 in books at amazon.com

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• As-Is/Cause for Concern

- Disclaimer/Bad Data Decisions Spiral

- Data Management Practices Hierarchy Structure

- Lack of Architecture/Engineering Capabilities/Costs

- Self Assessment/Root cause analysis

• To-Be/Necessary (but insufficient) CDO Prerequisites

1. Dedicated solely to data asset leveraging

2. Unconstrained by an IT project mindset

3. Reporting directly to the business


The Case for the Chief Data OfficerRecasting the C-Suite to Leverage your most Valuable Asset



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CIOs/Organizational IT

• Have accomplished astounding technological feats• Have developed excellent organizational skill sets• Have delivered phenomenal business value

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IT Project Failure Rates (moving average)

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Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php

0%

15%

30%

45%

60%

1994 1993 1998 2000 2002 2004 2009

16%

27% 26%28%

34%

29%

32%

53%

33%

46%

49%51%

53%

44%

31%

40%

28%

23%

15%

18%

24%

Failed Challenged Succeeded


Bad Data Decisions Spiral

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Bad Data Decisions Spiral

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Bad data decisions

Most CIOs are not data

knowledgable

Poor treatment of organizational data

assets

C-level decision makers are not

data knowledgable

Poorqualitydata

Poor organizational outcomes

• Beth Jacobs abruptly resigned in Marc


These decisions have consequences

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10

Maslow's Hierarchiy of Needs


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You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will:• Take longer• Cost more• Deliver less• Present

greaterrisk


Data Management Practices Hierarchy

Basic Data Management Practices

Advanced Data

Practices• MDM• Mining• Big Data• Analytics• Warehousing• SOA

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Data Program Management

Data Stewardship Data Development

Data Support Operations

Organizational Data Integration

Data Program Coordination

Feedback

DataDevelopment


StandardData

Data Management is an Integrated System of Five Practice AreasOrganizational Strategies

Goals

BusinessData

Business Value

Application Models & Designs

Implementation

Direction

Guidance

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OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

IntegratedModels

Leverage data in organizational activities

Data management processes andinfrastructure

Combining multipleassets to produceextra value

Organizational-entity subject area data

integration

Provide reliable data access

Achieve sharing of data within a business area


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.


DataDevelopment

OrganizationalData Integration

DataStewardship

Data SupportOperations


Weakest Link Results Reporting Results• Understand five organizational data

management practice areas– Rate each area per capability maturity

model

• Understand the "weakest link" nature of the results reporting – Engineered components can only be as

strong as their weakest component– Low scores seem harsh but are realistic

– (and on the upside) easily improvable– A single "1" degrades the entire practice

area – as shown with "stewardship"

• DMPA results are granulized for each practice area providing improvement process guidance

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He who doesn’t lay his foundations before hand, may by great abilities do so afterward ...... although with great trouble to the architect and danger to the building.


Machiavelli, Niccolo. The Prince. 19 Mar. 2004 http://pd.sparknotes.com/philosophy/prince

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Niccolo Machiavelli (1469-1527)

You cannot architect after implementation!


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USS Midway & Pancakes


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What is this?

• It is tall• It has a clutch• It was built in 1942• It is still in regular use!


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Architecture and

Engineering

Architecture

Engineering• Architecture enables

complex "things" to be built

• Engineering ensures a disciplined approach to development


Data Leverage

• Permits organizations to better manage their sole non-depleteable, non-degrading, durable, strategic asset - data– within the organization, and – with organizational data exchange partners

• Leverage – Obtained by implementation of data-centric technologies, processes, and human skill

sets– Increased by elimination of data ROT (redundant, obsolete, or trivial)

• The bigger the organization, the greater potential leverage exists

• Treating data more asset-like simultaneously 1. lowers organizational IT costs and 2. increases organizational knowledge worker productivity

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Less ROT

Technologies

Process

People


Data Data

Data

Information

Fact Meaning

Request

A Model Specifying Relationships Among Important Terms

[Built on definition by Dan Appleton 1983]

Intelligence

Strategic Use

1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one

MEANING.5. INTELLIGENCE is INFORMATION associated with its USES.

Wisdom & knowledge are often used synonymously

Data

Data

Data Data

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• Organizations spend between 20 - 40% of their IT budget evolving their data - including:– Data migration

• Changing the location from one place to another

– Data conversion• Changing data into another form,

state, or product

– Data improving• Inspecting and manipulating, or re-

keying data to prepare it for subsequent use

– Source: John Zachman


Data is a hidden IT Expense

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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’sMost Important Asset.














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Organizations Surveyed

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• Results from more than 500 organizations

• 32% government• Appropriate

public company representation

• Enough data to demonstrate European organization DM practices are generally more mature

Local Government4%

State Government Agencies17%

Federal Government11%

Public Companies 58%

International Organizations10%


Data Management Capability Maturity Model Levels

Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts

Initial(1)

Repeatable(2)

We have DM experience and have the ability to

implement disciplined processes

We have experience that we have standardized so that all in

the organization can follow itDefined

(3)

Managed(4)

We manage our DM processes so that the whole organization can follow our standard

DM guidance

Optimizing(5)We have a process for improving our DM capabilities

One concept for process improvement, others include:

• Norton Stage Theory• TQM• TQdM• TDQM• ISO 9000

and focus on understanding current processes and determining where to make improvements.

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Assessment Components

Data Management Practice AreasData Management Practice Areas

Data program coordination

DM is practiced as a coherent and coordinated set of activities

Organizational data integration

Delivery of data is support of organizational objectives – the currency of DM

Data stewardshipDesignating specific individuals caretakers for certain data

Data development Efficient delivery of data via appropriate channels

Data support Ensuring reliable access to data

Capability Maturity Model Levels Examples of practice maturity

1 – InitialOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 - RepeatableWe have DM experience and have the ability to implement disciplined processes

3 - DocumentedWe have standardized DM practices so that all in the organization can perform it with uniform quality

4 - ManagedWe manage our DM processes so that the whole organization can follow our standard DM guidance

5 - Optimizing We have a process for improving our DM capabilities

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• CMU's Software Engineering Institute (SEI) Collaboration

• Results from hundreds organizations in various industries including:✓ Public Companies ✓ State Government Agencies✓ Federal Government✓ International Organizations

• Defined industry standard• Steps toward defining data management

"state of the practice"

Data Management Practices Measurement (DMPA)

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Data Stewardship

Data Development


Focus: Implementation

and Access

Focus: Guidance and

Facilitation

Optimizing (V)

Managed (IV)

Documented (III)

Repeatable (II)

Initial (I)

Development guidance

Data Adminstration

Support systems

Asset recovery capability

Development training

0 1 2 3 4 5

Client Industry Competition All Respondents


Data Management Practices Assessment

Challenge

Challenge

Challenge



Data Stewardship

Data Development


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30

High Marks for IFC's Audit

Leadership & Guidance

Asset Creation

Metadata Management

Quality Assurance

Change Management

Data Quality

0 1 2 3 4 5

TRE ISG IFC Industry Benchmarks Overall Benchmarks

• 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


Largely Ineffective Investments

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Investment <= Return10%

Investment > Return20%

Return ≈ 070%


Comparison of DM Maturity 2007-2012

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1

2

3

4

5

Data

Prog

ram

Coor

dinati

on

Orga

nizati

onal

Data

Integ

ratio

n

Data

Stew

ards

hip

Data

Deve

lopme

nt

Data

Supp

ort O

pera

tions

2007 Maturity Levels 2012 Maturity Levels


Conclusion must be?1. CIOs are unaware of the

strategic nature of data; or

2. CIOs are not concerned about how data management is accomplished in their organizations; or

3. CIOs think data management is being adequately accomplished in their organizations

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0

0.09

0.18

0.27

0.36

0.45

SuccessfulPartial Success

Don't know/too soon to tellUnsuccessful

Does not exist

• In 25 years:– "Successful" DM organizations fell from 43% to 15%– "Unsuccessful" increased from 5% to 21%.


% of DM organizations labeled "successful"

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19812007

1981 2007

Percentage Reporting Directly

to the CIO74% 43%

Percentage reporting 3 or

more levels below the CIO

26% 57%


CIOs are distancing themselves from DM

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Why Data Projects Fail by Joseph R. Hudicka

• Assessed 1200 migration projects!– Surveyed only

experienced migration specialists who have done at least four migration projects

• The median project costs over 10 times the amount planned!– Biggest Challenges: Bad Data; Missing Data; Duplicate Data

• The survey did not consider projects that were cancelled largely due to data migration difficulties

• "… problems are encountered rather than discovered"

$0 $125,000 $250,000 $375,000

Median Project Expense

Median Project Cost

Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31

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Root Cause Analysis of IT Challenges

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Poor Data

Asset Management

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016


5% Sales Increase Versus Data Volume

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Sales Data Volume

Driver:


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• Increasing demand• Increasing amount• Heightened public

and corporate sensitivity to security, privacy, and compliance

• Data dependent IT initiatives (BI, SOA, Analytics, Big Data ...)

• New data emphasis • Leveraging data for

competitive advantage and profitability


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A likely state of your data

Very

Silo

’ed or

conf

lictin

g data

sour

ces

Multiple Data Sources

Inconsistent data definitions of

common terms

IT are data owners

Lots of Data….Minim

um Inform

ation

Inconsistent Data Quality

Difficult to report and mine against

Redundancy

Multiple changes to source system


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A likely state of your data management efforts

Data becomes a fuel!


Various Maturity Frameworks

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Usage basis Make it Happen

Make it Happen Faster

What Happened

?

Why did it happen?

What will happen?

Make it happen by

itself

What do I want to

happen?

How do we make it

happen better?

What should we do next?

Content basis Events Trans- actions Reporting Analyzing Predictive Operation-

alizeClosed

loopCollabor -

ative Foresight

Capability basis Initial Defined

Organization Basis Innovate Operate: Consolidate OptimizeIntegrate

OptimizedManagedRepeatable

Adapted from John Ladley

Data is a lubricant!

Data is the new oil!Data is the new (s)oil!

Data is the new bacon!


Enron• August 2001 Enron stock falls to $42/share from $90/share

• Dynergy brings several $ billion in an attempted rescue

• Enron spends entire amount in 1 week– Any person can write a check at Enron for

– Any amount of money for

– Any purchase at

– Any time

• Enron goes back to Dynergy for more $

• Dynergy: What happened to the several $ billion I gave you last week?

• Enron:• http://en.wikipedia.org/wiki/Enron

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CFO Necessary Prerequisites/Qualifications• CPA

• CMA

• Masters of Accountancy

• Other recognized degrees/certifications

• These are necessary but insufficient prerequisites/qualifications

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What do we teach knowledge workers about data?

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What percentage of the deal with it daily?


What do we teach IT professionals about data?• 1 course

– How to build a new database

– 80% if IT expenses are used to improve existing IT assets

• What impressions do IT professionals get from this education?– Data is a technical

skill that is used to develop new databases

• This is not the best way to educate IT and business professionals - every organization's– Sole, non-depletable,

non-degrading, durable, strategic asset

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Organizations aren't ...• The ultimate authority

on organizational informational assets

• Able to devote the required time/attention to the management of organizational informational assets

• Possessed of the requisite expertise to manage organizational informational assets

• Situated to achieve success organizationally as long as they have a technology development perspective














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CIO Infrastructure Focus

Help DeskMobileDesktop

User N

eeds

Conne

ctivit

y

Networking Telephony

Back

End

Sys

.

VirtualizationCloud SupportData Centers

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Data


Top Five CIO Concerns 2005-2011

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2005 2006 2007 2008 2009 2010 2011Grant Thornton-State CIO Survey 1 1 1 1 1 1 6Ameritech ITAA Annual CIO survey 1 1 1 1 1 1 6CIO Magazine-State of the CIO 1 1 1 1 1 5UK CIO Survey 1 1Gartner Annual Priorities 1 1 1 1 1 1 1 7Informationweek Global CIO top 10 issues 1 1 1 3Accenture CIO Survey 1 1KPMG & Harvey Nash 1 1 1 1 1 1 6NASCIO Survey 1 1 1 1 1 1 1 7Robert Half Technology 1 1 1 1 1 1 6

3 6 9 7 8 8 7 48

2005 2006 2007 2008 2009 2010 2011 0.000

0.200

0.400

0.600

0.800

IT/Infor

mation S

ecurity

/Privacy

Virtualiz

ation

Data ce

nter/IT

effici

encie

s/Clou

d

Social M

edia

Impro

ving p

eople

/leade

rship

BI/ana

lytics

Standa

rdizat

ion/co

nsolida

tion

IT workfor

ce de

velop

ment

IT gover

nance

Risk m

anag

emen

t

Mobile

applic

ations/

techn

ologie

s

Inform

ation S

harin

g

Imple

menting

plans/

initativ

es/ach

ieving

resul

ts

Acquisit

ion/pr

oject m

gt

Process

/syste

m integ

ration

Strateg

ic plan

ning


Top Five CIO Concerns 2005-2011

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The Top Job• Finance• Operations• Sale/Marketing• HR• Risk• Technology/CIO

– Align IT initiatives with business goals– Improving IT operations performance– Cultivating the IT/business partnership– Cost control/expense management– Implementing new systems– Leading change efforts– Driving business innovation– Redesigning business processes– Developing and refining business strategy– Negotiating with IT vendors– Managing IT crises– Developing market strategies & technologies– Security management– Studying trends to identify opportunities


Where does data go?

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... data

Information


Chief Electrification Officer

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• Chief Electrification Officer – responsible for electrical generating and distribution systems. The title was used mainly in developed countries from the 1880s to 1940s during the electrification of industry, but is still used in some developing countries.

Not all roles are needed always!


It is difficult to remain data-focused• Question:

– What is the hardest part of doing analysis?• Answer:

– Not doing design!• Question:

– What is the hardest part of a CDO's job?

• Answer:– Remaining data focused!

• Anything the CDO does that is not ... – ... applying organizational data assets to the

implementation of organizational strategy ...... is a similar distraction

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Application-Centric Development

Original articulation from Doug Bagley @ Walmart

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Data/Information

Network/Infrastructure

Systems/Applications

Goals/Objectives

Strategy• In support of strategy, organizations develop specific goals/objectives

• The goals/objectives drive the development of specific systems/applications

• Development of systems/applications leads to network/infrastructure requirements

• Data/information are typically considered after the systems/applications and network/infrastructure have been articulated

• Problems with this approach:– Ensures data is formed to the applications and

not around the organizational-wide information requirements

– Process are narrowly formed around applications

– Very little data reuse is possible


Data-Centric Development

Original articulation from Doug Bagley @ Walmart

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Data/Information

Goals/Objectives

Strategy• In support of strategy, the organization develops specific goals/objectives

• The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage

• Network/infrastructure components are developed to support organization-wide use of data

• Development of systems/applications is derived from the data/network architecture

• Advantages of this approach:– Data/information assets are developed from an

organization-wide perspective– Systems support organizational data needs and

compliment organizational process flows – Maximum data/information reuse


Data-Centric Perspective• Measure success differently• Same project• Same process• Different measures for success

– Asking if the data is correct

– Valuing data more than valuing "on time and within budget";

– Valuing correct data more than correct processes

– Auditing data rather than project documents• Articulation by Linda Bevolo

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Data/Information

Goals/Objectives

Strategy


This represents a gradual shift from application to data-centric

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Data/Information



Goals/Objectives

Strategy


"Waterfall" model creates new data siloes

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Develop/Implement Software

Develop/Implement Data


Evolving Data is Different than Creating New Systems

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Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!


Data is not a Project• Durable asset

– An asset that has a usable life more than one year

• Reasonable project deliverables – 90 day increments– Data evolution is measured in years

• Data– Evolves - it is not created– Significantly more stable

• Readymade data architectural components– Prerequisite to agile development

• Only alternative is to create additional data siloes!

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Confusion• IT thinks data is a business problem

– "If they can connect to the server, then my job is done!"

• The business thinks IT is managing data adequately– "Who else would be taking care of it?"

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Top Operations

Job


CDO Reporting

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Top Job

Top Finance Job

Top InformationTechnology

Job

Top Marketing

Job

• There is enough work to justify the function• There is not much talent• The CDO provides significant input to the Top Information Technology Job

Data Governance Organization

ChiefData

Officer


CDO Reporting Particulars1. Report outside of IT and

the current CIO altogether;

2. Report to the same organizational structure that the CFO and other "top" jobs report into; and

3. Focus on activities that are outside of (and more importantly) upstream from any system development lifecycle activities (SDLC).

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New division of labor• Reporting to IT

– Data Development– Database Operations

Management• Shared with the

business– Metadata

Management– Data Security

Management• Reporting to

Business– Data Architecture

Management– Reference & Master

Data Management– Data Warehousing &

BI Management– Document & Content

Management– Data Quality

Management– Data Governance

Asking "why" repeatedly!


Ishikawa Fishbone Diagrams

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• Why is infant mortality so high?– Malnourished mothers• Why are mothers malnourished?

– Substandard biology educations in high school• Why do are biology programs substandard?

– Poor education of high school biology teachers• Why do we have poor biology teacher

education?– Biology profession unaware of consequences

• Why are so many organizational technology experiences so poor?–Misunderstanding of data's role in IT• Why do so few understand data's role in IT?

–Little, if any, focus on enterprise-wide data use in the educational system

• Why is the educational system not addressing this gap?–Lack of recognition by the system• Why has the system not yet been made aware

of this deficiency?–Lack of understanding at the C-level of these

issues• Why do they not understand?

–Little, if any, focus on enterprise-wide data use in the educational system














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