covits 16 presentation - why johnny can’t data - peter aiken phd

Peter Aiken PhD

Why Johnny Can't Data

DATA

and what you can do about it

Peter Aiken, Ph.D.

Copyright 2016 by Data Blueprint Slide #

• 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions:

– US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – …

• DAMA International President 2009-2013

• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd

• DAMA International Community Award 2005

PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’sMost Important Asset.

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

Peter Aiken andMichael Gorman

2

50 Years of IS at VCU

Copyright 2016 by Data Blueprint Slide # 3


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DATA

DATA


Why Johnny Can't Data?• If your are satisfied with your current

data leverage capabilities then … – Sit this talk out – Data is a by-product of IT operations – Detritus (debris, waste, refuse, rubbish, litter, scrap, flotsam and jetsam, rubble)

• IT Challenge – Lack of foundational data content/incorrect focus on technologies – Data is a resource – Growing awareness that it needs to be governed

• Knowledge Worker Challenge – The epitomy of knowledge work – Data is an asset – Most powerful, yet most underutilized and poorly managed asset

• What next? – A challenge to your organization

US DoD Reverse Engineering Program Manager


• "Your first project is to keep me from having to testify to a Congressional Hearing!"

• Problem: 37 systems paid personnel within DoD

– How many were needed?

– How many potential losers?

• What do you mean by employee?

• Process modeling - inconclusive results

• Data reverse engineering - definitive

– One legged engineer, working in waist deep waters, underneath rotating helicopter blades, on overtime

Data Reverse Engineering

Amazon Best Sellers Rank: #1,841,642 in Books


UntanglingThe Legacy knot


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

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy


Advanced Data

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

Foundational Data Practices

Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management StrategyTechnologies

Capabilities

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Governor's Data Interns Program


Commonwealth Agencies • VDOT • DARS • ELECT • DMAS • DHRM • SecHealth • DMV • VDH • …

Screening

Two Phase Approach


Packaging(INFO609)

ResultsINFO611/632

} Opportunities for

Results

Fast Track MBA

Other INFO Classes

Other University Participants

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.

DMM℠ Capability Maturity Model Levels


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

Performed (1)

Managed (2)

Our DM practices are defined and documented processes performed at the

business unit level

Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined

(3)

Measured (4)

We manage our data as a asset using advantageous data governance practices/structures

Optimized

(5)DM is strategic organizational capability, most

importantly we have a process for improving our DM capabilities

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Organizations Surveyed• Results from more

than 500 organizations

• 32% government

• Appropriate public company representation

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


Data Management Strategy

Data Governance

Data Platform & Architecture

Data Quality

Data Operations

0 1 2 3 4 5Client Industry Competition All Respondents

Challenge

Challenge

Challenge

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Comparative Assessment Results

Data Management Strategy

Data Governance

Data Platform & Architecture

Data Quality

Data Operations

Industry Specific Results

• 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"


Focus: Implementation and

Access

Focus: Guidance and

Facilitation

Optimizing (V)

Managed (IV)

Documented (III)

Repeatable (II)

Initial (I)

DM Maturity 2007-2011


1

2

3

4

5

Data

Man

agem

ent S

trate

gy

Data

Gov

erna

nce

Data

Plat

form

& A

rchit

ectu

re

Data

Qua

lity

Data

Ope

ratio

ns

2007 Maturity Levels 2011 Maturity Levels


What do they mean big?"Every 2 days we create as much information as we did up to 2003"

– Eric Schmidt


The number of things that can produce data is rapidly growing (smart phones for example)

IP traffic will quadruple by 2015 – Asigra 2012

The likely state of your data management efforts


Very

Silo

’ed

or co

nflic

ting

data

sour

ces

Multiple Data Sources Inconsistent

data definitions

of common

terms

ISD are data

ownersLots of

Data….Minimum

Informatio

n Inconsistent Data Quality

Difficult to report and mine against

Redundancy Multiple changes to source system

There will never be less data

than right now!Copyright 2016 by Data Blueprint Slide # 22

DATA








When I grow up ... Pole dancing home depot shovel sales

Dear Ms. Davis: I want to be very clear on my child's illustration. It is NOT of me on a dance pole on a stage in a strip joint. I work at Target and had commented to my daughter how much money we made in the recent snowstorm. This drawing is of me selling a shovel. Mrs. Harrington


Target Isn't Just Predicting Pregnancies


http://rmportal.performedia.com/node/1373 and http://www.predictiveanalyticsworld.com/patimes/target-really-predict-teens-pregnancy-inside-story/ http://rmportal.performedia.com/rm/paw10/gallery_01#1373

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Managing Data with Guidance?


Lewis in front of the cummins safe


And in this corner we have Dave!


John McClain Bruce Willis crawling thru air ducts


A high-level graphic showing the various routes that Verizon penetration testers were able to use to get all the way down to Target’s cash registers in 2013 and 2014


http://krebsonsecurity.com/2015/09/inside-target-corp-days-after-2013-breach/


Beth Jacobs abruptly resigned in March

These decisions have consequences!


and my point is …

33

DATA







Data

• Overly dependent upon:

– Human-beings

– Wetwear

– Tribal knowledge

– Informal communications

– Non-repeatable practices

Data / Information Gap

Information


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


What do we teach knowledge workers about data?


What percentage of the deal with it daily?

What do we teach IT professionals about data?


• 1 course

– How to build a new database

• What impressions do IT professionals get from this education?

– Data is a technical skill that is needed when developing new databases

• If we are migrating databases, we are not creating new databases and we don't need organizational data management knowledge, skills, and abilities (KSAs).

• If we are implementing a new software package, we are not creating a new database and therefore we do not need data management KSAs.

• If we are installing an enterprise resource package (ERP), we are not creating a new database and therefore we do not need data management KSAs.

Architecture and

Engineering

Architecture

Engineering

• Architecture enables complex "things" to be built

• Engineering ensures a disciplined approach to development


Niccolo Machiavelli (1469-1527)

He who has not first laid his foundations may be able with great ability to lay them afterwards ... ... but they will be laid with trouble to the architect and danger to the builder.


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

40


You cannot architect after implementation!

Good Architectural Foundation?


Poor Architectural Foundation


USS Midway & Pancakes

• It is tall

• It has a clutch

• It was built in 1942

• It is still in regular use!

What is this?


DAMA International Data Management Body of Knowledge


(DM BoK)


Data Management Maturity Model (DMM)


Develop/Implement Software

Develop/Implement Data

This approach can only work when no sharing of

data occurs!

"Waterfall" and other SDLC models create data silos

Evolving Data is Different than Creating New SystemsCommon 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!


My barn had to pass a foundation inspection …

• … before further construction could proceed • No IT equivalent



Managing Data wi th Guidance

What is Data Governance?

50

• Cash & other financial instruments • Real property • Inventory • Intellectual Property • Human

– Knowledge – Skills – Abilities

• Financial • Organizational reputation • Goodwill • Brand name • Data!!!

Many Examples of Organizational Assets


Data Assets

Financial Assets

RealEstate Assets

Inventory Assets

Non-depletable

Available for subsequent use

Can be used up

Can be used up

Non-degrading √ √ Can degrade

over timeCan degrade

over time

Durable Non-taxed √ √

Strategic Asset √ √ √ √

We believe ...• Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your

– Sole – Non-depletable – Non-degrading – Durable – Strategic

• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!

• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships


Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]

Data Assets Win!

John Snow's 1854 Cholera Map of London


While the basic elements of topography and theme existed previously in

cartography, the John Snow map was unique, using cartographic methods not

only to depict but also to analyze clusters of geographically dependent phenomena

• A certain university

• The Egg Man

• Admission Date

Three Examples


What can Rolls Royce Learn• Old model

– Sell jet engines

• New model – Sell hours of thrust power – No payment for down time – Wing to wing


from Nascar?

Anonymous Identifier Crime Sentence Gender Race Disability LEP …

1 92G-gqP-6ek-oy3 A 30 M W 0 N

2

3

First Name Last Name Date of Birth Zip Code Crime Sentence Gender Race Disability LEP …

1 Peter Aiken 1/17/1959 23192 A 30 M W 0 N

2

3

Hash Function(Replaces PII with Anonymous Identifier)

Hashing Process Illustrated


Data Amalgamation Process


Hash Function

CorrectionsDJJHealth DSS DMAS …

Hash Function

Hash Function

Hash Function

Hash Function

VCU Dataset (No PII)

Watson Analytics

Diagnosing Organizational Readiness


adapted from the Managing Complex Change model by Dr. Mary Lippitt, 1987

Culture is the biggest impediment to a shift in

organizational thinking about data!


PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’sMost Important Asset.

10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

Copyright 2016 by Data Blueprint Slide #60

covits 16 presentation - why johnny can’t data - peter aiken phd

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