Peter Aiken · 2012-07-27 · Peter Aiken [email protected] +1 804 382 5957 J. Brian Cassel...

- datablueprint.com 6/19/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Data - It Shouldn't Be This Hard

Lessons From The Trenches

Peter [email protected] +1 804 382 5957

J. Brian Cassel [email protected] +1 804 628-1926


Peter Aiken

• 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)

• 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 19992

• 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)

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

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

- datablueprint.com 6/19/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!3

J. Brian Cassel• J"Brian"Cassel,"PhD

• [email protected]"

• Visi9ng"Senior"Lecturer"/"Fulbright"Scholar

• King's"College"London"C"Cicely"Saunders"Ins9tute

• London,"England

• +44"(0)20"7848"5679"(office)

• +44"(0)78"2739"9421"(mobile)

• hVp://www.csi.kcl.ac.uk/"

• Massey"Cancer"Center

• Virginia"Commonwealth"University

• Richmond,"Virginia,"USA

• hVp://www.massey.vcu.edu/

Cassel is a senior analyst in the oncology administration department of the VCU Health System; assistant professor of Quality Health Care in the School of Medicine; adjunct assistant professor in Life Sciences; affiliate research associate professor in psychology; and member, Massey Cancer Center, VCU. Dr Cassel is the director of the VCU Life Sciences and Religion Initiative. In that role, he created and co-teaches the “Faith and Life Sciences” class (with Mark Wood and John Quillin), created and co-chairs the “Life Sciences and Religion Community Forum of Central Virginia” (with Mark Wood), funded by the Local Societies Initiative of the Metanexus Institute, and co-directs the “Science, Reason and Faith” series of six public lectures and debates this year (with Anthony Ellis, Thomas Huff and Donald Smith). His doctorate (City University of New York Graduate School and University Center, 1995) was in social-personality and health psychology. His dissertation, “Altruism is only part of the story: A prospective, longitudinal study of AIDS volunteers” defined a typology of AIDS volunteers; this prospective, longitudinal study revealed the antecedents and consequences of altruistic behavior. Dr Cassel has taught courses in research methods, social psychology, science-and-religion, clinical outcomes evaluation, and morality/justice at SUNY, NYU and VCU in BS, MS and PhD programs. He serves on several cancer and palliative-care focused committees for the VCU Health System. Dr. Cassel is recognized nationally for his studies of the financial and clinical outcomes of palliative care, and participates in state-wide and nation-wide training programs on palliative care. He presented a paper on the bio-psycho-social-spiritual aspects of end-of-life care at the 2006 Metanexus Institute conference and has published and presented papers on self-help group leaders, AIDS volunteers, end-of-life care, palliative care, cancer care, and the use of data systems in advancing cancer programs. With Everett Worthington he has submitted two research proposals on forgiveness at the end of life.


Agenda

• What do we mean by data management maturity?

• Four lessons:1. Organizational thinking must

change2. Crawl, walk, run3. No silver bullets4. Taking an agile approach

4


The File Naming Convention Committee's Output

5


24 hour observation of all of the large aircraft flights in the world, condensed down to just over a minute

6


IT Project Failure Rates (moving average)

7

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


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 $500,000

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. 29-318


StandardData

Data Management

Data Program Coordination

OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

Organizational Strategies

Goals

IntegratedModels

BusinessData

Business Value

Application Models & Designs

Feedback

Implementation

Direction

DataDevelopment

Guidance

9

Assign responsibilities for data.


Manage data coherently.

Share data across boundaries.

Engineer data delivery systems.

Maintain data availability.

11


Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations

Data Management

10


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

Data Management Capability Maturity Model Levels

Defined(3)

We have experience that we have standardized so that all in the organization

can follow it

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.


Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member 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 performancePercentage of Projects on TimeBy Process Framework Adoption

Key Finding: Process Frameworks are not Created EqualWith the exception of CMM and ITIL, use of process-efficiency frameworks does not predict higher on-budget project delivery…

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Assessment Components

Data Management Practice AreasData Management Practice AreasData 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 stewardship Designating 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 – Initial Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts

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

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

4 - Managed We 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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Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations


Data Management Practices Measurement (DMPA)

14

Focus: Implementation

and Access

Focus: Guidance and

Facilitation

Optimizing (V)

Managed (IV)

Documented (III)

Repeatable (II)

Initial (I)

• 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"


Agenda




15


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)

16

Typical System Evolution

t

t

Strategy

Goals/Objectives

Systems/Applications

Network/Infrastructure

Data/Information


t

• In support of strategy, the organization develops 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:– This ensures that data is formed

around the application and not the organizational information requirements

– Process are narrowly formed around applications

– Very little data reuse is possible

Application-Centric Development Flow

Original articulation from Doug Bagley @ Walmart

t

t

Strategy

Goals/Objectives

Data/Information

Network/Infrastructure

Systems/Applications


t

• 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/information needs and compliment organizational process flows

– Data/information reuse is maximized

Data-Centric Development Flow

Original articulation from Doug Bagley @ Walmart


Designing for Evolution is Different than Creating New Systems

19

Common Organizational Data (and corresponding data needs requirements)

New Organizational

Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Results

Increasing scope and depth of information architecture utility

Individual SDLC Effort


Individual SDLC efforts make increasing use from IA

• Over time the:– Number of requests increase– Utility of the results increase– Amount of metadata contributed by

new systems development increases

20

Requirements

Design

Implement

Requests Results


Requirements

Design

Implement

Requests

Results


Requirements

Design

Implement

Requests

Organized system

metadata

Organized system

metadata

Organized system

metadata

Top Operations Job


TDJ Reporting

21

Top Job

Top Finance Job

Top Technology Job

Top Marketing Job

TopData Job

• There is enough work to justify the function• There is not much talent

Data Governance Organization


Agenda




22


Reduce-Reuse-Recycle … Data?• Reduce the amount of organizational data ROT

– Redundant, obsolete, trivial• Reuse the remainder

– Fewer vocabulary items to resolve– Greater quality engineering leverage

• Integration is impossible without information architecture components (for mapping)– Maintenance of these components

promotes greater reuse• Shared data is typified by

organizational ability to use information as a strategic asset

• However, assets are useless without knowledge of the asset characteristics

23


Interdependencies

24

Data Governance

Master Data Data Quality


Inextricably Intertwined Interactions

25

Improved Quality Data

Master Data

Monitoring

Data Governance

Practices

Master Data Management

Practices

Governance Violations Monitoring

Data Quality Engineering

Practices

Data Quality

Monitoring

Monitoring Results:

Suspected/ Identified

Data Quality

Problems Data Quality Rules

Monitoring Results:

Suspected/ Master Data &

Characteristics

Routine Data

Scans

Master Data

Catalogs

Governance Rules

Routine Data

Scans

Monitoring Rules

Focused Data

Scans

Operational Data

Data Harvesting

Quality Rules

• Progress beyond crawling requires– Two distinct skill sets to must reach

agreement• Expert organizational knowledge• Data management engineering expertise

– Practice• Collaborating• Learning from mistakes

– Ongoing training commitment


Crawl, walk, run, fly

26


Experience Pebbles

27

http://myriadtechnicalresources.com


Myth Busters on Silver Bullets

28


Agenda




29


Data Governance Hype Cycle

http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp30


How to avoid becoming part of the parade?

31

SOA

ERP

OOPS NoSQL

CLOUD

MDM

Cloud


Response: Massey Cancer Center Data Analytics

Hospital billing claims• IP & OP encounter data• Cost, charge, reimbursement, &

utilization data • ICD-9 diagnosis, CPT procedure,

UBC revenue codes

Physician billing claims• IP & OP encounter data• Utilization & charge data• ICD-9 diagnosis, CPT

procedure codes

Pathology DB• Surgery/Cytology Path Reports• Test Values

UHCIP data for Academic Medical CentersICD-9, CPT, DRG codes, LOS, Mortality

Outpatient Pharmacy DBDrug Utilization Details•

VCUHS Internal Data Sources – linkable at patient level

Bone Marrow Transplant DBClinical data on donors & recipients•

Public Health Data• BRFSS• SEER• VCR• Vital Statistics

•Social SecurityDates of death

Patient Satisfaction

External Data Sources (useful for benchmarking)

Cancer Registry

•Site, stage, pathology details•Initial Treatment

US Census Population Data

Intellimed / VHHA• IP data for all Virginia hospitals• Can expand to other states as

well (Thomson-Reuters or AHRQ-HCUP)

SQL server system (MDAS)

32


$(3,000,000)

$(1,000,000)

$1,000,000(

$3,000,000(

$5,000,000(

$7,000,000(

$9,000,000(

$11,000,000(

FY(1997 FY(1998 FY(1999 FY(2003 FY(2004 FY(2005 FY(2006 FY(2007 FY(2008 FY(2009 FY(2010 FY(2011((est)

Total(Profit((Loss)(for(BMT@related hospital(accounts

Dr. Retchin (CEO), circa 2000: The hospital is losing $1-$2 million per year on this program. Fix it.


How?• Worked very closely with service line administrator, BMT

medical director, and managed care contracting director• Evaluation & improvement of all aspects of the revenue

cycle – direct costs, indirect costs, coding, pricing, contract negotiations, tracking denials, etc.

• Compared VCUHS program to external benchmarks (UHC)

• Developed significant familiarity with transplant types, phases of transplants, contract terms (stop-loss; second-dollar outlier payments; phases; global-billing), etc.

• Did this continuously• Once profitable, program expanded: from 60 transplants

per year to ~15034


Agile Programming (Doesn't Just Mean ... )

35


Agenda




36


“Our hospital wants us to use the existing system, can we create an

Oncology ‘cube’?”

• Can you get all the information you need in a “cube” from an existing business intelligence data system?– Would it include outpatient care?– Would it capture the whole care continuum?– Would it allow you to categorize by disease type?– Would it allow you to categorize by modality of care?

37


Patient-based analyses following patient from diagnosis through treatment

From%diagnosis:%Primary'site'of'cancerDate'of'diagnosis

Stage/spread'of'disease

xx

xx

x

xx

xx

xx

x

xxx

xx

xx

xx

xx

xx

x

xx

x

x

x

xxx

x xx

xxx

x x

Hospital Cancer Registry

Hospital, Physician, Pharm Claims

1111111

2222222

3333333

4444444

5555555

x

x

xx

x

xx

xxx

x

x

xx

xx x

x

xxxx x

xx

x

x x

x

xx

x

x

x

xxx

x xx

xxx

x

x

xx

xx

xx

x

x x

x

x

Follow%pa0ent%interac0ons%over%0me:Capture'all'encounter'dates'and'details

+

30

38


What we know from the cancer

registry…

What we gain from integrating

billing claims

!!

A closer look …

31

39


Consulting firm: “Close down palliative care program”

• VCU Health System opened one of first Palliative Care Units in the US, May 2000.

• Consultants recommended closing it in 2002.– They looked at net margin for hospitalizations ending on the

PC Unit and saw that the costs greatly exceeded reimbursement.

– They thought that getting rid of the unit would get rid of this problem.

• RWJ Foundation supported urgent response.• Appropriate financial analyses convinced consultants

that the unit actually produced valuable hospital outcomes.– See KR White & JB Cassel (2009). “The Business Case for a Hospital Palliative Care

Unit: Justifying its Continued Existence”. Practice of Evidence-Based Management, T Kovner, D Fine & R D’Aquila (Eds.), Chicago: Health Administration Press, pp 171-180.

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Slide 20

Cost-avoidance in drugs (-77%), labs (-95%), imaging (-95%), supplies (-60%).

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Agenda




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http://peteraiken.net

Contact Information:

Peter Aiken, Ph.D.

Department of Information Systems School of BusinessVirginia Commonwealth UniversitySnead Hall Room B4217301 West Main StreetRichmond, Virginia 23284-4000

Data Blueprint 10124C West Broad StreetGlen Allen VA 23060804.521.4056http://datablueprint.com

office: +1.804.883.7594cell: +1.804.382.5957

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

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h"p://tw

i"er.com

/paiken

Peter Aiken · 2012-07-27 · Peter Aiken [email protected] +1 804 382 5957 J. Brian Cassel...

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Transcript of Peter Aiken · 2012-07-27 · Peter Aiken [email protected] +1 804 382 5957 J. Brian Cassel...