Healthcare Analytics 101 Workshop

(1)

Healthcare Analytics 101 Workshop

Necessary Pre-requisites

The Case for the

Chief Data O

ffi

cer

Recasting the C-Suite to Leverage

Your Most Valuable Asset

Peter Aiken and

Michael Gorman

Peter Aiken, Ph.D.

2

• 25+ years of experience in data

management

• Multiple international awards &

recognition

• Founder, Data Blueprint

(datablueprint.com)

• Associate Professor of IS, VCU

(vcu.edu)

• President, DAMA International

(dama.org)

• 8 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, and the Commonwealth

of Virginia

(2)

With thanks to:

• J. Brian Cassel, PhD

Senior Analyst

Oncology Administration

VCU Health System

• Lisa Shickle, MS, former

director Massey Data Analytics

(now at Wellpoint)

• Gordon Ginder, MD & Mary Ann Hager, MSN, Massey /

VCUHS

• Kathleen Kerr, Kerr Healthcare Analytics

• Tom Smith, Johns Hopkins

• Massey / VCUHS palliative care team

Slide 39

3

(3)

5

IBM's Data Baby

6

(4)

7

Bills of Mortality

8

Mortality Geocoding

Where is it happening?

(5)

9

Plague Peak

When is it happening?

("Whereas of the Plague")

10

Black Rats or Rattus Rattus

Why is it happening?

Black Rats or Rattus Rattus

(6)

11

What will happen?

12

(7)

1. Adopting a crawl, walk, run strategy

2. Understanding current and potential

organizational maturity and corresponding

capabilities

3. Achieving an appropriate technology/human

capability balance

4. Implementing useful IT systems development

practices

5. Installing necessary non-IT leadership

13

101 Workshop: Necessary Pre-requisites

IT Project Failure Rates

Recent IT project failure rates statistics

can 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 (accessed 9/14/02)

New York Times 1/22/05 pA31

14

1 in 3 IT projects suffers on

• Price

• Schedule

(8)

IT Project Failure Rates

(moving average)

15

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

0

0.09

0.18

0.27

0.36

0.45 Successful

Partial Success

Don't know/too soon to tell

Unsuccessful

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"

16

1981

2007

(9)

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

17

Not Enough Data Management Involvement

18

Data Warehousing

XML

Data Quality

Customer Relationship Management

Master Data Management

Customer Data Integration

Enterprise Resource Planning

Enterprise Application Integration

(10)

£12bn NHS computer system is scrapped

• The biggest civilian IT project of its kind

in the world, it has already squandered at

least £12.7billion. Some estimates put

the cost far higher.

• Following an official review, the ‘one size

fits all’ IT project will be replaced by much

cheaper regional initiatives, with hospitals

and GPs choosing the IT system they

need.

http://www.dailymail.co.uk/news/article-2040259/NHS-IT-project-failure-Labours-12bn-scheme-scrapped.html#ixzz2R1yb9F1i

19

20

Data Strategy in Context

Organizational

IT Strategy

Data Strategy

Only 1 is 10 organizations has a board approved data

strategy!

(11)

What does it mean to treat data as an organizational asset?

• Assets are economic resources

–

Must own or control

–

Must use to produce value

–

Value can be converted into cash

• An asset is a resource controlled by the

organization as a result of past events

or transactions and from which future

economic benefits are expected to flow

to the organization [Wikipedia]

• Data are an organization's

–

Sole, non-depletable, non-degrading,

durable, strategic asset

• With assets:

–

Formalize the care and feeding of data

• Cash management - HR planning

–

Put data to work in unique/significant ways

• Identify data the organization will need

[Redman 2008]

21

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

(12)

Leverage is an Engineering Concept

23

What is meant by use

of an information

architecture?

• Application of data assets

towards organizational

strategic objectives

• Assessed by the maturity of

organizational data

management practices

• Results in increased

capabilities, dexterity, and

self awareness

• Accomplished through use

of data-centric development

practices (including

taxonomies, stewardship,

and repository use)

(13)

Data Leverage

• Permits organizations to better manage their sole depletable,

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 and the pace of innovation

25

Less ROT

Technologies

Process

People

Data Strategy Choices

26

Q1

Keeping the doors open

(little or no proactive

data management)

Q2

Increasing organizational

efficiencies/effectiveness

Q3

Using data to create

strategic opportunities

_Both

Q4

(Cash Cow)

Improve Operations

Innovation

Only 1 is 10 organizations has a

(14)

Great point of initial

inspiration ...

• Formalizing stuff forces clarity

• Special shout out to Chapter 7

–

Measuring the value of

information

–

ISBN: 0470539399

–

http://www.amazon.com/How-

Measure-Anything-Intangibles-Business

27

• This Virginia cancer center is a

leader in shaping the fight

against cancer

• Over 500 researchers and

staff tend to over 12,000

patients annually

• This requires robust

information management and

analytical services

• The problem: It takes 1 month

to run a report on an incident,

i.e. a patient’s hospital visit

that shows all touch points

A National Cancer Institute

(15)

• Data Blueprint engineered a

solution that provides a 360 degree

view of an incident, i.e. patient’s

hospital visit

• New solution provides reports in 2

days: 360 degree view of patient’s

data including diagnosis, treatment,

etc.

• Integrated hospital and physician

data enhances financial and asset

utilization

• Results include improved quality of

care, optimized workflow processes

as well as operational performance

A National Cancer Institute (cont’d)

29

30

1.Manual transfer of digital data

2.Manual file movement/duplication

3.Manual data manipulation

4.Disparate synonym reconciliation

5.Tribal knowledge requirements

6.Non-sustainable technology

(16)

0

25

50

75

100 Current

Improved

Reversing The Measures

• Currently:

–

Analysts spend 80% of their time manipulating data and 20% of their time

analyzing data

–

Used to take 1 month to produce key reports

• After rearchitecting:

–

Analysts spend 20% of their time manipulating data and 80% of their time

analyzing data

–

Two days to produce key reports

31

Manipulation

Analysis

Savings come from a variety

of agreed upon categories

and values:

• Reduced hospital

re-admissions

• Patient Monitoring:

Inpatient, out-patient,

emergency visits and ICU

• Preventive care for ACO

• Epidemiology

• Patient care quality and

program analysis

$300 billion is the potential annual value to health care

32

$165

$108

$47

$9$5

Transparency in clinical data and clinical decision support

Research & Development

Advanced fraud detection-performance based drug pricing

Public health surveillance/response systems

(17)

Book Recommendation

• Permits the

reorientation of

medicine

–

From populations

–

To individuals

• Big Data Capture

–

Wireless sensors

–

Genome sequencing

–

Printing organs

33

Analytics in Health Care

3 !

Organization-wide

!

Volume and Noise

!

Utility

!

Meaningful scoring

!

Actionable recs

!

Realistic goals

!

Support

!

Manage & measure

• Descriptive

Ask:

What happened?

What is happening?

Find:

Structured data

Show:

Profiles, Bar/pie charts, Narrative

Predictive

Ask:

What will happen?

Why will it happen?

Find:

Structured/unstructured data

Show:

Risk Profiles, Pros/Cons, Care Recs

Prescriptive

Ask:

What should I do? Why should I do it?

Find:

Unstructured/structured data

(18)

35

Results: It is not always about money

• Solution:

–

Integrate multiple databases into

one to create holistic view of data

–

Automation of manual process

• Results:

–

Data is passed safely and effectively

–

Eliminate inconsistencies,

redundancies, and corruption

–

Ability to cross-analyze

–

Significantly reduced turnaround

time for matching patients with

potential donor -> increased

potential to make life-saving

connection in a manner that is

faster, safer and more reliable

–

Increased safe matches from 3 out

of 10 to 6 out of 10

(19)

“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

9 Getting the C-suite’s attention:

How much of the hospital’s business is Oncology?

Disease-centered analyses are not limited to cost centers, divisions,

clinics, units, or other “silos” for strategic planning purposes.

(20)

Profitability by disease and modality

13

39

21 Market Analysis

• Two measures of market share indicate that 26% - 32% of

cancer patients in Central Virginia receive some or all of

their treatment here.

• In other words, 68% - 74% receive none of their care here.

• VCU is capturing only 20% of inpatient oncologic surgeries

originating in our primary service area (Bon Secours

captures 40%, HCA 36%). VCU is 4

th

_{in state for oncologic}

surgeries, behind UVA, Inova Fairfax, and St Mary’s.

• This gives us plenty of opportunity to increase volumes.

(21)

State-wide, regional context

22

41

Patient-based analyses following patient

from diagnosis through treatment

From diagnosis:

Primary site of cancer

Date of diagnosis

Stage/spread of disease

x

x x

x

Hospital

Cancer

Registry

Hospital, Physician, Pharm Claims

1111111

2222222

3333333

4444444

5555555

x

_x

x

x x

x

_x

x

Follow patient interactions over time:

Capture all encounter dates and details

+

(22)

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.

43

Cost-avoidance in drugs (-77%), labs

(-95%), imaging (-95%), supplies (-60%).

(23)

8 Hospital study of cost reduction

Slide 34

Morrison, Penrod, Cassel et al. (2008). Cost savings associated with US hospital palliative care consultation programs. Archives

of Internal Medicine 168 (16), 1783-1790.

45

Slide 35

250.0000

500.0000

750.0000

1000.0000

1250.0000

1500.0000

1750.0000

2000.0000

1

2

3

4

5

6

7

8 9 10 11 12 13 14 15 16 17 18 19 20 21

Direct Cost ($)

Day of Admission

8 Hospital Study of Cost Reduction

Morrison, Penrod, Cassel et al. (2008). Cost savings associated with US hospital palliative care consultation programs. Archives

of Internal Medicine 168 (16), 1783-1790.

PC consult day

10-11

PC consult day

12-13

Usual Care

46

(24)

What we know from the cancer

registry…

What we gain

from integrating

billing claims

!

A closer look …

47

1. Adopting a crawl, walk, run strategy

2. Understanding current and potential

organizational maturity and corresponding

capabilities

3. Achieving an appropriate technology/human

capability balance

4. Implementing useful IT systems development

practices

5. Installing necessary non-IT leadership

48

(25)

Not Enough Data Management Involvement

49

Data Warehousing

XML

Data Quality

Customer Relationship Management

Master Data Management

Customer Data Integration

Enterprise Resource Planning

Enterprise Application Integration

Initiative Leader

Initiative Involvement

Not Involved

0

0.09

0.18

0.27

0.36

0.45 Successful

Partial Success

Don't know/too soon to tell

Unsuccessful

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"

50

1981

2007

(26)

26%

68%

6%

9%

75%

6%

DM 1st

DBMS 1st

Simultaneously

DM Origins – Which arrives first – DM or DBMS?

• A key indicator of organizational awareness

• 75% reacting instead of anticipating

• Best practices are obvious

1981

2007

51

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. 29-31

(27)

• 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 Data

Management

Investments

53

Investment <= Return

10%

Investment > Return

20%

Return

≈

0 70%

(28)

Cruiser Collector

55

Data Program

Coordination

Feedback

Data

Development

Standard

Data

Five Integrated DM Practice Areas

Organizational Strategies

Goals

Business

Data

Business Value

Application

Models &

Designs

Implementation

Direction

Guidance

56

Organizational

Data Integration

Data

Stewardship

Data Support

Operations

Data

Asset Use

Integrated

Models

Leverage data in organizational activities

Data management

processes and

infrastructure

Combining multiple

assets to produce

extra value

Organizational-entity

subject area data

integration

Provide reliable

data access

Achieve sharing of data

within a business area

(29)

Organizational DM Practices and Inter-relationships

57

Assign responsibilities for data.

Manage data coherently.

Share data across boundaries.

Engineer data delivery systems.

Maintain data availability.

Data Program

Coordination

Organizational

Data Integration

Data

Stewardship

_Development

Data

Data Support

Operations

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 it

Defined

(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.

(30)

Assessment Components

Data 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 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

59

• 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)

60

Data Program Coordination

Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations

Focus:

Implementation

and Access

Focus:

Guidance and

Facilitation

Optimizing (V)

Managed (IV)

Documented (III)

Repeatable (II)

Initial (I)

(31)

Comparison of DM Maturity 2007-2012

61

1

2

3

4

5 Data Program Coordination

Organizational Data Integration

Data Stewardship

_{Data Development}

Data Support Operations

2007 Maturity Levels

2012 Maturity Levels

Service Orient or Be Doomed!

• Service Orient or Be

Doomed!

–

How Service Orientation Will

Change Your Business

(Hardcover) by Jason

Bloomberg & Ronald

Schmelzer

–

I'm not quite sure what "doom"

awaits by not service orienting,

other than remaining mired in

archaic, calcified and siloed

processes — which a lot of

businesses do anyway, and still

manage to stay afloat. But

that's the topic for another

posting.

• Reviewer

(32)

How SOA/Services are "Sold"

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

63

Contractor Implemented Wiring

(33)

Concise Notes on

Software 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 data

structures whenever possible

–

Allow only selected service

modules to know about the

concealed data structures

–

Bind together modules that

know about concealed data

structures

–

Package such modules along

with the data itself

65

All Contents © 2008 Burton Group. All rights reserved.

SOA is Dead; Long Live Services

8 April 2009

Anne Thomas Manes

VP & Research Director

[email protected]

www.burtongroup.com

• We have a replay of the presentation, which I

gave in February, on the Burton BrightTalk

Channel:

http://www.brighttalk.com/channels/750/view

(You have to page down to get to the SOA is Dead

presentation.)

(34)

All Contents © 2008 Burton Group. All rights reserved.

ECONOMY

SOAsaurus

SOA

met its demise on January 1, 2009,

when it was wiped out by the catastrophic

impact of the economic recession.

SOA is survived by its offspring: mashups,

SaaS, Cloud Computing, BPM, and all other

architectural approaches that depend on

"services."

SOA Obituary

SOA Postmortem: Why did it die?

• Vague abstract architectural concept

• No universally accepted meaning

• Indefensible value proposition

• How do you measure flexibility/agility?

• Cost savings are lower than anticipated

• Success rate is very low

• Ill defined term of dubious business value

67 SOA DM Maturity Requirements

1.00 _2.00

_3.00

4.00 Data Program Coordination

Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations

• Conclusion - more ground to cover than has been attained to

date

(35)

8/7/09 8:40 AM

Lack of Focus on Data Killing SOA - Leveraging Information and Intelligence

Page 1 of 6

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Leveraging Information and Intelligence

David Linthicum

Lack of Focus on Data Killing SOA

By

David Linthicum

on July 24, 2009 1:42 PM

14 Vote 0 Votes

For those of you that have been following me know that I'm very much an advocate of SOA. The

architectural pattern of SOA is helpful in defining an enterprise architecture that much more agile,

and thus pays for itself once the business has to shift and needs IT to follow.

SOA, however, is complex and requires that the architect understand all aspects of the "as is"

architecture before moving to the "to be." This means decomposing the existing architecture down to

a primitive state, and rebuilding it up again at sets of services, with a process configuration or

composite applications layer to define and redefine business functions. I think most get that.

What's missing within most typical SOA projects is the focus on the data, and that is killing SOA.

Since the "S" in SOA, means service, most architects focus on the service definition, abstracting the

existing data into collections of services, but don't pay much attention to the data within the

architecture. Not good.

The truth is that the foundation of a healthy and functional SOA is the data, and you have to deal

with the underlying data first, understand it, perhaps reorganize and abstract it, before defining the

services that will sit on top of the data. While this is architecture 101, the fact is that those driving

SOAs these days have little understanding of the importance of understanding and defining the data,

and thus the architecture ends up being a bunch of well defined services that sit on top of very

dysfunctional data. The end result is performance issues, data integrity issues, and even the lack of

agility which is why you build SOAs in the first place.

The truth is that most failed SOA projects can be traced to the lack of a data level understanding,

and while this is still an issue in this day and time is beyond me. There are many technology and

tools out there to assist you, and we've been doing data for a long long time. Nothing new here, just

data. However, if you ignore it your SOA will be still born.

« "Fighting cancer with Business Intelligence"

|

Main

|

Why IBM Buying SPSS is bad for BI »

Vijay Narayanan

|

July 26, 2009 10:42 PM

|

Reply

Completely agree David. I think data from a SOA standpoint is extremely critical for several reasons:

- reducing errors/rework in automated business processes that leverage enterprise data services

Industry expert Dave Linthicum's tells you what

you need to know about building efficiency into

the information management infrastructure

David Linthicum

David Linthicum is an internationally

known distributed computing and

application integration expert.

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Lack of Focus on Data is Killing SOA

69

What's missing within most typical SOA

projects is the focus on the data

The truth is that the foundation

of a healthy and functional

SOA is the data

Most failed SOA projects can be traced to the

lack of a data level understanding

Hierarchy of Data Management Practices (after Maslow)

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

Advanced

Data

Practices

• Cloud

• MDM

• Mining

• Big Data

• Analytics

• Warehousing

• SOA

• 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

Basic Data Management Practices

–

Data Program Management

–

Organizational Data Integration

–

Data Stewardship

–

Data Development

–

Data Support Operations

(36)

1. Adopting a crawl, walk, run strategy

2. Understanding current and potential

organizational maturity and corresponding

capabilities

3. Achieving an appropriate technology/human

capability balance

4. Implementing useful IT systems development

practices

5. Installing necessary non-IT leadership

71

101 Workshop: Necessary Pre-requisites

J. C. R. Lickleider's Man-Computer Symbiosis

72

Humans Generally Better

Machines Generally Better

• Sense low level stimuli

• Detect stimuli in noisy background

• Recognize constant patterns in varying situations

• Sense unusual and unexpected events

• Remember principles and strategies

• Retrieve pertinent details without a priori

connection

• Draw upon experience and adapt decision to

situation

• Select alternatives if original approach fails

• Reason inductively; generalize from observations

• Act in unanticipated emergencies and novel

situations

• Apply principles to solve varied problems

• Make subjective evaluations

• Develop new solutions

• Concentrate on important tasks when overload

occurs

• Adapt physical response to changes in situation

• Sense stimuli outside human's range

• Count or measure physical quantities

• Store quantities of coded information accurately

• Monitor prespecified events, especially infrequent

• Make rapid and consisted responses to input

signals

• Recall quantities of detailed information accurately

• Retrieve pertinent detailed without a priori

connection

• Process quantitative data in prespecified ways

• Perform repetitive preprogrammed actions reliably

• Exert great, highly controlled physical force

• Perform several activities simultaneously

• Maintain operations under heavy operation load

• Maintain performance over extended periods of

time

(37)

73

• 60 GB of data/second

• 200,000 hours of big data will

be generated testing systems

• 2,000 hours media coverage/

daily

• 845 million facebook users

averaging 15 TB/day

• 13,000 tweets/second

• 4 billion watching

• 8.5 billion devices connected

2012 London Summer Games

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.

(38)

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

75

Data Governance Definitions

• The other half of MDM – The Bloor Group

• 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

(39)

Suicide Mitigation

77

Suicide Mitigation

Data Mapping

12 Mental

illness

Deploy

ments

Work

History

Soldier

_Legal

Issues

Abuse

Suicide

Analysis

FAP

DMSS

G1

DMDC

CID

Data objects

complete?

All sources

identified?

Best source for

each object?

How reconcile

differences

between

sources?

MDR

(40)

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

79

Communication Patterns

80

Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the

(41)

Technique/Technical Interdependencies

81

Master Data Management

Data Quality

Data Governance

Benefits of a Database

• Data can be shared

• Redundancy can be reduced

–

All redundancy cannot be or necessarily should be reduced

• Inconsistency can be avoided

–

Data obtained by Physics department will be the same as

the Chemistry department

• Transaction support can be provided

–

Transaction is not complete until money is deleted from the

savings account after adding it to the checking account

• Integrity can be maintained

–

A student can be recorded as having obtained 1000 marks, as compared to 100 – this can be corrected by

enforcing integrity.

• Security can be enforced

–

Information on demand – Finance need to see the records related to Human resources

• Conflicting requirements can be balanced

–

Volume of data as compared to speed Business Standards can be enforced

• Data Dependency

–

Technique used to physically stored and accessed are dictated by the application, and the knowledge of physical

representation and access technique is built into the application code.

–

Not desirable in a Database System Different users require different views of the same data

–

Freedom to change the physical representation or access technique in view of the changing requirements

• Changing record types

• Physical storage location

(42)

Architecture is both the process and

product of planning, designing and

constructing space that reflects functional,

social, and aesthetic considerations.

A wider definition may comprise all design

activity from the macro-level (urban

design, landscape architecture) to the

micro-level (construction details and

furniture).

In fact, architecture today may refer to the

activity of designing any kind of system

and is often used in the IT world.

Architecture

83

Typically Managed Architectures

84

• Enterprise Architecture

• Business Architecture

• Systems Architecture

Network

Arrangement

Hierarchical

Arrangement

• Process Architecture

–

A

rrangement of inputs -> transformations = value -> outputs

–

Typical elements: Functions, activities, workflow, events, cycles,

products, procedures

• Systems Architecture

–

Applications, software components, interfaces, projects

• Business Architecture

–

Goals, strategies, roles, organizational structure, location(s)

• Security Architecture

–

Arrangement of security controls relation to IT Architecture

• Technical Architecture/Tarchitecture

–

Relation of software capabilities/technology stack

–

Structure of the technology infrastructure of an enterprise, solution or

system

–

Typical elements: Networks, hardware, software platforms, standards/

protocols

• Data/Information Architecture

–

Arrangement of data assets supporting organizational strategy

–

Typical elements: specifications expressed as entities, relationships,

attributes, definitions, values, vocabularies

(43)

Information Architectures

• The underlying (information) design principals

upon which construction is based

– Source: http://architecturepractitioner.blogspot.com/

• … are plans, guiding the transformation of

strategic organizational information needs into

specific information systems development

projects

– Source: Internet

• A framework providing a structured description

of an enterprise’s information assets —

including structured data and unstructured or

semistructured content — and the relationship

of those assets to business processes,

business management, and IT systems.

– Source: Gene Leganza, Forrester 2009

• "Information architecture is a foundation

discipline describing the theory, principles,

guidelines, standards, conventions, and factors

for managing information as a resource. It

produces drawings, charts, plans, documents,

designs, blueprints, and templates, helping

everyone make efficient, effective, productive

and innovative use of all types of information."

– Source: Information First by Roger & Elaine Evernden, 2003

ISBN 0 7506 5858 4 p.1.

• Defining the data needs of the enterprise and

designing the master blueprints to meet those

needs

– Source: DM BoK

85

Data Architecture – Better Definition

86

• All organizations have information

architectures

–

Some are better

understood

and

documented

(and therefore more

useful

to the organization) than

others.

• Common vocabulary expressing

integrated requirements ensuring

that data assets are stored,

arranged, managed, and used in

systems in support of

(44)

Vocabulary is Important-Tank, Tanks, Tankers, Tanked

87

How one inventory item proliferates data throughout the chain

88

555"Subassemblies"&"subcomponents

17,659"Repair"parts"or"Consumables

System 1:

18,214 Total items

75 Attributes/ item

1,366,050 Total attributes

System"2

47"Total"items

15+"A@ributes/item

720"Total"a@ributes

System"3

16,594"Total"items

73"A@ributes/item

1,211,362"Total"a@ributes

System"4

8,535"Total"items

16""A@ributes/item

136,560"Total"a@ributes

System"5

15,959""Total"items

22""A@ributes/item

351,098"Total"a@ributes

Total"for"the"ﬁve"systems"show"above:

59,350"Items

179"Unique"a@ributes

3,065,790"values

(45)

• National Stock Number (NSN)

Discrepancies

–

If NSNs in LUAF, GABF, and RTLS are

not present in the MHIF, these records

cannot be updated in SASSY

–

Additional overhead is created to correct

data before performing the real

maintenance of records

• Serial Number Duplication

–

If multiple items are assigned the same

serial number in RTLS, the traceability of

those items is severely impacted

–

Approximately $531 million of SAC 3

items have duplicated serial numbers

• On-Hand Quantity Discrepancies

–

If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can

be no clear answer as to how many items a unit actually has on-hand

–

Approximately $

5 billion

of equipment does not tie out between the LUAF and

RTLS

Business Implications

Information Architecture Representation

• Information architectures are the symbolic representation

of the structure, use and reuse of information resources

• Common components are represented using standardized

notation and are sufficiently detailed to permit both

business analysts and technical personnel to separately

read the same model, and come away with a common

understanding and yet they are developed effectively.

(46)

Architectural Answers

(Adapted from [Allen & Boynton 1991])

Computers

Human resources

Communication facilities

Software

Management

responsibilities

Policies,

directives,

and rules

Data

91

• Where do they go?

• When are they needed?

• What standards

should be adopted?

• What vendors

should be chosen?

• What rules should govern

the decisions?

• What policies should guide

the process?

• How and why do the components interact?

• Why and how will the changes be implemented?

• What should be managed organization-wide and what should

be managed locally?

Data structures organized into an Architecture

• How do data structures support

organizational strategy?

• Consider the opposite question?

–

Were your systems explicitly designed to be

integrated or otherwise work together?

–

If not then what is the likelihood that they will

work well together?

–

In all likelihood your organization is

spending between 20-40% of its IT budget

compensating for poor data structure

integration

–

They cannot be helpful as long as their

structure is unknown

• Two answers/two separate strategies

–

Achieving efficiency and

effectiveness goals

–

Providing organizational dexterity for rapid

implementation