Peter Aiken - irmac.ca Overview.pdf · • TQM • TQdM • TDQM • ISO 9000 And focus on understanding current processes and determining where improvements can be made. Our DM practices

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© Copyright 12/9/07 by Data Blueprint - all rights reserved!3 - datablueprint.com

Measuring DataManagement

Practice Maturity:A Community’s

Self-Assessment


Peter AikenPeter Aiken• Full time in information technology since 1981• IT engineering research and project background• University teaching experience since 1979• Seven books and dozens of articles• Research Areas

– reengineering, data reverse engineering, software requirements engineering, information engineering, human-computer interaction, systems integration/systems engineering, strategic planning, and DSS/BI

• Director– George Mason University/Hypermedia Laboratory (1989-1993)

• DoD Computer Scientist– Reverse Engineering Program Manager/Office of the Chief Information Officer (1992-1997)

• Visiting Scientist– Software Engineering Institute/Carnegie Mellon University (2001-2002)

• Published Papers– Communications of the ACM, IBM Systems Journal, InformationWEEK, Information & Management,

Information Resources Management Journal, Hypermedia, Information Systems Management, Journal ofComputer Information Systems and IEEE Computer & Software

• DAMA International Advisor/Board Member (http://dama.org)

– 2001 DAMA International Individual Achievement Award (with Dr. E. F. "Ted" Codd)– 2005 DAMA Community Award

• Founding Advisor/International Association for Information and Data Quality (http://iaidq.org)

• Founding Advisor/Meta-data Professionals Organization (http://metadataprofessional.org)

• Founding Director Data Blueprint 1999


Dogs New ClothesDogs New Clothes


Two Brilliant Einstein Quotes

• "The significant problems we face cannot besolved at the same level of thinking we wereat when we created them."

• "Everything should be made as simple aspossible, but no simpler."– Albert Einstein

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Misunderstanding Data ManagementMisunderstanding Data Management


IT Project Failure RatesIT Project Failure RatesRecent IT project failure rates statisticscan be summarized as follows:

– Carr 1994• 16% of IT Projects completed on time,

within budget, with full functionality

– OASIG Study (1995)• 7 out of 10 IT projects "fail" in some respect

– The Chaos Report (1995)• 75% blew their schedules by 30% or more• 31% of projects will be canceled before they ever get completed• 53% of projects will cost over 189% of their original estimates• 16% for projects are completed on-time and on-budget

– KPMG Canada Survey (1997)• 61% of IT projects were deemed to have failed

– Conference Board Survey (2001)• Only 1 in 3 large IT project customers were very “satisfied"

– Robbins-Gioia Survey (2001)• 51% of respondents viewed their large IT implementation project as unsuccessful

– MacDonalds Innovate (2002)• Automate fast food network from fry temperature to # of burgers sold-$180M USD write-

off

– Ford Everest (2004)• Replacing internal purchasing systems-$200 million over budget

– FBI (2005)• Blew $170M USD on suspected terrorist database-"start over from scratch"

http://www.it-cortex.com/stat_failure_rate.htm (accessed9/14/02)

New York Times 1/22/05 pA31


Why Data Projects Fail by Joseph R. Hudicka

• Assessed 1200migration projects!– Surveyed only

experienced migrationspecialists who havedone at least fourmigration projects

• The median projectcosts over 10 times the amount planned!

• Biggest Challenges: Bad Data; Missing Data; Duplicate Data

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

• "… problems are encountered rather than discovered"

Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31© Copyright 12/9/07 by Data Blueprint - all rights reserved!22 - datablueprint.com

Platform: UniSysOS: OS1998 Age: 21Data Structure: DMS (Network)Physical Records: 4,950,000Logical Records: 250,000Relationships: 62Entities: 57Attributes: 1478

Predicting Engineering Problem CharacteristicsPredicting Engineering Problem Characteristics

New System

Legacy System #1: Payroll

Legacy System #2: Personnel

Platform: AmdahlOS: MVS1998 Age: 15Data Structure: VSAM/virtual

database tablesPhysical Records: 780,000Logical Records: 60,000Relationships: 64Entities: 4/350Attributes: 683

Characteristics Logical PhysicalPlatform: WinTel Records: 250,000 600,000OS: Win'95 Relationships: 1,034 1,0201998 Age: new Entities: 1,600 2,706Data Structure: Client/Sever RDBMS Attributes: 15,000 7,073

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"Extreme" Data Engineering"Extreme" Data Engineering

• 2 person months = 40 person days• 2,000 attributes mapped onto 15,000• 2,000/40 person days = 50 attributes

per person dayor 50 attributes/8 hour = 6.25 attributes/hour

and• 15,000/40 person days = 375 attributes

per person dayor 375 attributes/8 hours = 46.875

attributes/hour• Locate, identify, understand, map, transform,

document, QA at a rate of -• 52 attributes every 60 minutes or

.86 attributes/minute!


Data Integration/Exchange ChallengesData Integration/Exchange Challenges

• Customer typically has had different meanings todifferent parts of the organization:– Accounting -> organization that buys products or services

– Service -> client

– Sales -> prospect

• Assigning the same mission to the DoD ‘lines ofbusiness’ to: “Secure the building” elicits verydifferent results from each ‘line of business’:– Army: Posts guards at all entrances and ensures no

unauthorized access

– Navy: Turns out all the lights, locks up, and leaves

– Marines: Sends in a company to clear the building room-by-room; forms perimeter defense around the building

– Air Force: Signs three year lease with option to buy[Second example courtesy of Burt Parker]


Typical System EvolutionTypical System Evolution

Payroll Application(3rd GL)

Payroll Data(database)

R& D Applications(researcher supported, no documentation)

R & DData(raw)

Mfg. Data(home grown

database) Mfg. Applications(contractor supported)

FinanceData

(indexed)

Finance Application(3rd GL, batch system, no source)

Marketing Application(4rd GL, query facilities, no reporting, very large)

Marketing Data(external database)

Personnel Data(database)

Personnel App.(20 years old,

un-normalized data)


Building from the Top

4


StudentStudentSystemSystem

DataDataModelModel


Proposed Data ModelProposed Data Model


Clinical Systems

Billing/RegistrationSystems

Financial Systems

Decisian Support

Personnel Systems

DepartmentalSystems StandAlone

Planned Systems

AssociatedPhysicians

External Agencies

OPEN HUB

MEDICALINFORMATION

SYSTEM

(ECLIPSYS)

RxOBOT

Radiation Oncology (VARIS)

CLINICAPPOINTMENT

(ECLYPSIS)

DELINQUENT MEDICALRECORDS

PATIENTTRANSPORTATION

OB

TRANSCRIPTION SYSTEMS

MIDAS

RADIOLOGY

DHT

PATHOLOGY

CERNER

SYNERSOURCE

PROVIDER

DB

OR(ORMIS)

EDNET

DECISION SUPPORT

EIS

MARKETING(SACHS)

REGISTRATIONAND BILLING

(HBOC)

(PARS)

MATERIALS

MANAGEMENT

PURCHASING

RECEIVING

ACCTS PAYABLE

(ESI)

OUTPATIENTPHARMACY

(PCSI)

LAB OUTREACHBILLING

Managed care (idx)/

Open Referrals

MCVPREGISTRATON/BILLING (IDX)

ENTERPRISE APPTSCHEDULING (IDX)

MEDICALRECORDS 3M

REIMBURSEMENT AGENCIES

EXTERNALAGENCIES

COLLECTIONSSYSTEMS

(HBOC)ER

CODING/BILLING

(DATA STRIPPER)

PROFIT/LOSS

(KREG)

REVENUEANALYSIS

(KREG)

GENERAL LEDGER(CONSIST)

BANK

VCUSYSTEMS

BUDGET

(KREG)

COSTACCOUNTING

MEDICUS

FIXED ASSETS

(AMERICAN APPRAISAL)

HR/PAYROLL

(GENSYS)

TIMEREPORTING

(DDI)

LANIERDICTATION

PACS

Future

CERNERBLOODBANK

SET OFF DEPT

ComputritionDietary System

AnesthesiologySystem

Poisiondex

EEG (Siemens)

Cardiology (H-P)

MCVH INFORMATION SYSTEMS8/27/99

OfficeAutomation

Credentialing(Morrissee)

Transplant

TraumaRegistry

GOVERNMENT

BENEFITVENDORS

BANK

CASHLOGS


Sample Conversation (Developing Constraints)Sample Conversation (Developing Constraints)

• I'd like to build a building.• What kind of building - do you want to sleep in it? Eat

in it? Work in it?• I'd like to sleep in it.• Oh, you want to build a house?• Yes, I'd like a house.• How large a house do you have in mind?• Well, my lot size is 100 feet by 300 feet.• Then you want a house about 50 feet by 100 feet.• Yes, that's about right.• How many bedrooms do you need?• Well, I have two children, so I'd like three bedrooms ...

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Data Data

Data

Information

Fact Meaning

Request

A Model Specifying Relationships Among Important TermsA Model Specifying Relationships Among Important Terms

[Built on definition by Dan Appleton 1983]

Intelligence

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 specificREQUEST

4. INFORMATION REUSE is enabled when one FACT is combined with more than oneMEANING.

5. INTELLIGENCE is INFORMATION associated with its USES.

Wisdom & knowledge are often used synonymously

Data

Data

Data Data


2000-

Data Quality, Data SecurityData Compliance, Mashups

(more)

1990-2000

Enterprise data management coordinationEnterprise data integration

Data stewardshipData use

1970-1990

Data requirements analysisData modeling

Expanding ScopeExpanding Scope

Years 1950-1970

Database designDatabase operation


Change RequestsChange Requests


Avoiding Unnecessary Work Using Business Rule MetadataAvoiding Unnecessary Work Using Business Rule Metadata

Person Job Class

Employee Position

BR1) Zero, one, or moreEMPLOYEES can be

associated with one PERSON

BR2) Zero, one, or moreEMPLOYEES can be associatedwith one JOB CLASS;

BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION

BR4) One ormorePOSITIONScan beassociatedwith one JOBCLASS.

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"Understanding thecurrent and futuredata needs of anenterprise andmaking that dataeffective andefficient insupporting businessactivities"

Aiken, P, Allen, M. D., Parker, B., Mattia, A., "MeasuringData Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007)

Data ManagementData Management


As Is InformationRequirementsAssets

As Is Data Design Assets As Is Data Implementation Assets

Exi

stin

gN

ew

Metadata EngineeringMetadata Engineering

O2 RecreateData Design

Reverse Engineering

Forward engineering

O5 Reconstitute Requirements

O9Reimplement

Data

To Be DataImplementationAssets

O8 RedesignData

O4Recon-stitute

DataDesign

O3 RecreateRequirements

O6RedesignData

To BeDesign Assets

O7 Re-developRequire-ments

To BeRequirementsAssets

O-1/3 reconstitute original metadataO-4/5 improve the current metadataO-6/9 improve system data capabilities based on the improved metadata

O1 Recreate Data Implementation

Metadata


One concept for processimprovement, othersinclude:• Norton Stage Theory• TQM• TQdM• TDQM• ISO 9000And focus onunderstanding currentprocesses and determiningwhere improvements canbe made.

Our DMpractices are

ad hoc

We have DM experience andhave the ability to implement

disciplined processes

We have experience that wehave standardized so that all in

the organization can follow it

We manage our DM processes sothat the whole organization can

follow our standard DM guidance

We have a process forimproving our DM

capabilities

SEI CMM CapabilitySEI CMM CapabilityMaturity Model LevelsMaturity Model Levels

Initial(1)

Repeatable(2)

Defined(3)

Managed(4)

Optimizing(5)

"Self" Improving

Out of control

Inconsistent

Unpredictable

Unsustainable


Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.

Percentage of Projects on BudgetBy Process Framework Adoption

…while the same pattern generally holds true for on-time performance

Percentage of Projects on TimeBy Process Framework Adoption

Key Finding: Process Frameworks are not Created EqualKey Finding: Process Frameworks are not Created Equal

With the exception of CMM and ITIL, use of process-efficiencyWith the exception of CMM and ITIL, use of process-efficiencyframeworks does not predict higher on-budget project deliveryframeworks does not predict higher on-budget project delivery……

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StandardData

Organizational DM Functions and their Inter-relationshipsOrganizational DM Functions and their Inter-relationships

Data Program Coordination

OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

Organizational Strategies

Goals

IntegratedModels

BusinessData

Business Value

ApplicationModels & Designs

Feedback

Implementation

Direction

DataDevelopment

Guidance


StandardData




DataStewardship


Data Asset Use


Goals

IntegratedModels

BusinessData

Business Value


Feedback

Implementation

Direction

DataDevelopment

GuidanceDefining, coordinating, resourcing, implementing, andmonitoring organizational data program strategies,policies, plans, etc. as coherent set of activities.

Identifying, modeling, coordinating, organizing, distributing, and architecting datashared across business areas or organizational boundaries

Ensuring that specific individuals areassigned the responsibility for themaintenance of specific data asorganizational assets, and that thoseindividuals are provided the requisiteknowledge, skills, and abilities toaccomplish these goals in conjunctionwith other data stewards in theorganization

Specifying and designing appropriately architected dataassets that are engineered to be capable of supportingorganizational needs

Initiation, operation, tuning, maintenance, backup/recovery,archiving and disposal of data assets in support oforganizational activities.


StandardData




DataStewardship


Data Asset Use


Goals

IntegratedModels

BusinessData

Business Value


Feedback

Implementation

Direction

DataDevelopment

Guidance

Leverage data in organizational activities

Data managementprocesses andinfrastructure

Combining multipleassets to produceextra value

Organizational-entity subject areadataintegration

Provide reliableaccess to data

Achieve sharing of datawithin a business area


Data Program Coordination Individual ResponsesData Program Coordination Individual ResponsesData management process and infrastructure

1

2

3

4

5

Development

guidance

Data

Adminstration

Support

systems

Asset recovery

capability

Development

trainingResult 1 Result 2 Result 3 Result 4 Result 5

8


0 1 2 3 4 5

Development guidance

Data Adminstration

Support systems

Asset recovery capability

Development training

Nokia Industry Competition All Respondents

DataData Management Practices AssessmentManagement Practices Assessment

Challenge

Challenge

Challenge

Client

Result 1

Result 2

Result 3

Result 4

Result 5


Data Management PracticesData Management PracticesMeasurement (DMPA)Measurement (DMPA)

• Defined industrystandard

• Collaboration withCMU's SoftwareEngineeringInstitute (SEI)

• Attempt todetermine datamanagement's"state of thepractice"


Organizational Data Integration

Data Stewardship

Data Development

Data Support OperationsIn

itial (I)

Re

pe

ata

ble

(II)

De

fine

d (III)

Ma

na

ge

d (IV

)

Op

timizin

g (V

)

Focus:Guidance and

Facilitation

Focus:Implementation

and Access


Organizations SurveyedOrganizations Surveyed

Results from a survey of

more than 200 organizations

– Public Companies – State Government Agencies– Federal Government– International Organizations


The challenge aheadThe challenge ahead

0.00

1.00

2.00

3.00

4.00

5.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

The chart represents the average scoresThe chart represents the average scorespresented on the previous slide - interestingpresented on the previous slide - interestingthat none have apparently reached level-3that none have apparently reached level-3

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After more than a decade After more than a decade ……

Question How many software practices (surveyed) are above level 1 on theCMM?

Answer By far most organizations (95%) surveyed are producing softwareusing informal processes

Question How many organizations have demonstrated at least some proficiencyaccording to the DM3? (i.e., scored above level 1)

Answer One in ten organizations has scored above level 1 in the DM3according to our surveys


Service Orient or Be Doomed!Service Orient or Be Doomed!• Service Orient or Be

Doomed!– How Service Orientation

Will Change YourBusiness (Hardcover) byJason Bloomberg &Ronald Schmelzer

– I'm not quite sure what"doom" awaits by notservice orienting, otherthan remaining mired inarchaic, calcified andsiloed processes —which a lot of businessesdo anyway, and stillmanage to stay afloat.But that's the topic foranother posting.• Reviewer


ServicesServices

Integration Possibilities

• User Interface

• Business Process

• Application

• Data

AV Component

• Well defined components

• Self-contained

• No interdependencies

Analogy derived from D. Barry "Web Services" Intelligent Enterprise 10/10/03 pp. 26-47 - wiring diagram from sunflowerbroadband.com


Contractor Implemented WiringContractor Implemented Wiring

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Concise Notes onConcise Notes onSoftware EngineeringSoftware Engineering

– Published in 1979– 93 pages including appendices & references– Out of print– $1.99 at half.com

• Principles of Information Hiding(p. 32-33)

– Conceal complex datastructures whenever possible

– Allow only selected servicemodules to know about theconcealed data structures

– Bind together modules thatknow about concealed datastructures

– Package such modules alongwith the data itself


The basketball and golfball slide

How Does SOA Fit In Existing Architectures?How Does SOA Fit In Existing Architectures?

Bank


Data Quality Specific SOA RequirementsData Quality Specific SOA Requirements


SOA & Data & ???SOA & Data & ???

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New DM Realities New DM Realities –– Revised DM Goals Revised DM Goals

• Focus Short Term on Measurable Goals

• Implement Instead of Planning

• (Practically) Any Technology Can Help

• Identifying The Pareto Subsets forAnalyses

• Practice "Good Enough" Data Modeling

• The "Enterprise Model" Is Not Required

• Engineer Measurable Data QualityImprovements


http://peteraiken.net

Copyright 12/9/07 by Data Blueprint - all rights reserved!

Contact Information:

Peter Aiken, Ph.D.

Department of Information Systems School of BusinessVirginia Commonwealth University1015 Floyd Avenue - Room 4170Richmond, Virginia 23284-4000

Data Blueprint Maggie L. Walker Business & Technology Center501 East Franklin StreetRichmond, VA 23219804.521.4056http://datablueprint.com

office :+1.804.883.759cell:+1.804.382.5957

e-mail:[email protected]://peteraiken.net

Documents

Peter Aiken - irmac.ca Overview.pdf · • TQM • TQdM • TDQM • ISO 9000 And focus on understanding current processes and determining where improvements can be made. Our DM practices