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Copyright 2018 by Data Blueprint Slide # !1
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
Peter Aiken, Ph.D.
Alleviate
Strategy
Cycle
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Copyright 2018 by Data Blueprint Slide # !2
Peter Aiken, Ph.D.• I've been doing this a long time • My work is recognized as useful • Associate Professor of IS (vcu.edu) • Founder, Data Blueprint (datablueprint.com) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data
management practices worldwide • Multi-year immersions
– US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – …
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
!3Copyright 2018 by Data Blueprint Slide #
A Musical Analogy
!4Copyright 2018 by Data Blueprint Slide #
+ =
https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn
Please raise your hand when you recognize this song
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
!5Copyright 2018 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
!6Copyright 2019 by Data Blueprint Slide #
https://ctb.ku.edu/en/table-of-contents/structure/strategic-planning/develop-strategies/main
!7Copyright 2019 by Data Blueprint Slide #
https://www.cio.com/article/3088208/leadership-management/how-to-avoid-a-digital-strategy-failure.html
Simon Sinek: How great leaders inspire action
!8Copyright 2018 by Data Blueprint Slide #
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
What
How
Why• What motivates people – is not what you do, – It is why you do it”
• Rev. Martin Luther King Jr. gave the – "I have a dream speech"
(not the) – "I have a plan speech"
Strategy must provide the Why
What is a Strategy?
!9Copyright 2018 by Data Blueprint Slide #
Use the original definition derived from military ... • “a pattern in a stream of decisions”
– [Henry Mintzberg]
Former Walmart Business Strategy
!10Copyright 2018 by Data Blueprint Slide #
Every Day Low Price
Wayne Gretzky’sDefinition of Strategy
!11Copyright 2018 by Data Blueprint Slide #
He skates to where he thinks the puck will be ...
Strategy in Action: Napoleon faces a larger enemy• Question?
– How do I defeat the competition when their forces are bigger than mine?
• Answer:
– Divide and conquer!
– “a pattern in a stream of decisions”
!12Copyright 2019 by Data Blueprint Slide #
!13Copyright 2018 by Data Blueprint Slide #
Sup
ply
Line
Met
adat
a
(as
part
of a
div
ide
and
conq
uer s
trate
gy)
First Divide
!14Copyright 2018 by Data Blueprint Slide #
Then Conquer
!15Copyright 2018 by Data Blueprint Slide #
Complex Strategy• First
– Hit both armies hard at just the right spot
• Then
– Turn right and defeat the Prussians
• Then
– Turn left and defeat the British
!16Copyright 2018 by Data Blueprint Slide #
While someone else is
shooting at you!
Mike Tyson
• “Everybody has a plan until they get punched in the face.” – http://f--f.info/?p=23071
!17Copyright 2018 by Data Blueprint Slide #
Strategy Guides Workgroup Activities
!18Copyright 2019 by Data Blueprint Slide #
A pattern in a stream of decisions
Planning Options• Plan the entire
process before beginning – One attempt
• Use iterative strategy cycles – Incorporate corrective feedback on initial assumptions
!19Copyright 2019 by Data Blueprint Slide #
Alleviate the Constraint
StrategyCycle
Alleviate the Constraint
StrategyCycle
Alleviate the Constraint
StrategyCycle
Alleviate the Constraint
StrategyCycle
Note: Numerous upfront assumptions are required because plans must be detailed, specifying end objectives
Strategy Example 1
!20Copyright 2018 by Data Blueprint Slide #
Good Guys (Us)
Bad Guys (Them)
Strategy Example 2
!21Copyright 2018 by Data Blueprint Slide #
Good Guys (Us)
Bad Guys (Them)
Strategy that winds up only on a shelf is not useful
!22Copyright 2019 by Data Blueprint Slide #
Data
Strategy
System• A set of detailed methods, procedures, and routines established or
formulated to carry out a specific activity, perform a duty, or solve a problem.
• An organized, purposeful structure regarded as a whole and consisting of interrelated and interdependent elements (components, entities, factors, members, parts, etc.). These elements continually influence one another (directly or indirectly) to maintain their activity and the existence of the system, in order to achieve the goal of the system. http://www.businessdictionary.com/definition/system.html#ixzz23T7LyAjJ
!23Copyright 2019 by Data Blueprint Slide #
System
DataHardwareProcessesPeople Software Data
TWITTERUSERS SEND
473 400 TWEETS
SKYPEUSERS MAKE
176 220CALLS
GIPHY
USERS POST
PHOTOSSPOTIFYSTREAMS OVER
750,000SONGS
TUMBLRUSERS PUBLISH
POSTS
USERS WATCH
VIDEOS
SHIPS
PACKAGES
SNAPCHATTHE WEATHERCHANNEL
NETFLIXUSERS STREAM
97222 HRS
OF VIDEO
VENMOPROCESSES
$68493PEER-TO-PEER
TRANSACTIONS
TINDER
AMAZON
USERS MATCH
TIMES
TEXTS SENTNEW COMMENTSRECEIVES
USERS SHARE
SNAPS
YOUTUBE
LINKEDINGAINS
120+NEW
2 083 333 ,
4 333560 , ,
1 388 889
,79740,
BITCOINNEW
FORECASTREQUESTS
1.25ARE CREATED
RECEIVES
,
49 380,AMERICANSUSE
OF INTERNET DATA
3138420, , GB,6940,
18055555,,
1111
UBERUSERS TAKE
RIDES1389,
1944,
SERVES UP
GIFS
12 986 111,,
PROFESSIONALS
GOOGLECONDUCTS
SEARCHES
3 877140, ,
,, , ,
REDDIT MINUTEevery
DAYof the
PRESENTED BY DOMO
2018
,
How much Data,by the minute!
For the entirety of 2018, every minute of every day:
• 18 million weather forecast requests
• Netflix streams almost 100,000 hours of video
• LinkedIn adds 120+ individuals
• 1,300 Uber rides
• (almost) a half million tweets
• 7,000 Tinder matches
• 1.25 new cryptocurrencies are created
• ...
!24Copyright 2018 by Data Blueprint Slide #
https://www.domo.com/learn/data-never-sleeps-6
!25Copyright 2018 by Data Blueprint Slide #
As articulated by Micheline Casey
There will never be less
data than right now!
Complex & detailed • Outsiders do not
want to hear aboutor discuss any aspects of challenges/solutions
• Most are unqualified re: architecture/engineering
Taught inconsistently • Focus is on
technology • Business impact is
not addressed
Not well understood • (Re)learned by
everyworkgroup
• Lack of standards/poor literacy/unknown dependencies
Wally Easton Playing Piano https://www.youtube.com/watch?v=NNbPxSvII-Q
As a topic, Data is ...
!26Copyright 2019 by Data Blueprint Slide #
• Cash & other financial instruments • Real property • Inventory • Intellectual Property • Human
– Knowledge – Skills – Abilities
• Financial • Organizational reputation • Good will • Brand name • Data!!!
Organizational Assets
!27Copyright 2019 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
Copyright 2019 by Data Blueprint Slide # !28
How CEOs are Recognizing Data as an Asset[Check all that apply]
Data Assets Win!
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 √ √ √ √
Data Assets Win!• 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!
• As such, data deserves: – It's own strategy – Attention on par with similar organizational assets – Professional ministration to make up for past neglect
!29Copyright 2018 by Data Blueprint Slide #
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]
!30Copyright 2018 by Data Blueprint Slide #
• Highest level data guidance available ...
• Focusing data activities on goal achievements ...
• Providing guidance when faced with a stream of decisions or uncertainties
Your Data Strategy
Strategy provides context for the guidance
!31Copyright 2019 by Data Blueprint Slide #
Managing Data with Guidance
Managing Data with Guidance• How should data be used and in which business
processes? • How is data shared among users, divisions, geographies
and partners? • What processes and
procedures allow for data to be changed?
• Who manages approval processes?
• What processes ensure compliance?
• Most importantly, in what order should I approach the above list?
!32Copyright 2019 by Data Blueprint Slide #
Focus evolve from reactive to proactive
!33Copyright 2018 by Data Blueprint Slide #
Strategy helps your data program
Over time increase capacity and improve
StrategyCycle
Data Strategy Menu• Forces an understanding
of data importance
• Creates a vision for the organization
• Identifies the strategic imperatives
• Defines the benefits and key measures
• Describes the data management improvements needed
• Outlines the approach and activities
• Estimates the level of effort and investment
!34Copyright 2018 by Data Blueprint Slide #
WHY A data strategy is
important to the Org.
HOW It will impact the
organization
WHAT The future look like
(Paint a picture)
WHEN Can we
make it happen
• Effectiveness
– Over time
• Volume (length)
– Should be shorter than the organizational strategy
• Versions
– Should be sequential (with score keeping)
• Understanding
– Common agreement can be measured
!35Copyright 2018 by Data Blueprint Slide #
Data Strategy Measures
It isn't possible to go digital
Digital!36Copyright 2018 by Data Blueprint Slide #
aBy just spelling 'data'
Dat!37Copyright 2018 by Data Blueprint Slide #
It requires more work
Data!38Copyright 2018 by Data Blueprint Slide #
a
Lady Ada Augusta King Rule
!39Copyright 2018 by Data Blueprint Slide #
https://people.well.com/user/adatoole/bio.htm
Recent Technology Realization
!40Copyright 2018 by Data Blueprint Slide #
Garbage In➜Garbage Out!Recent
Garbage In ➜ Garbage Out!
!41Copyright 2018 by Data Blueprint Slide #
GI➜GO!
!42Copyright 2018 by Data Blueprint Slide #
Perfect Model
Garbage Data
Garbage Results
Data Warehouse
Machine LearningBusiness
IntelligenceBlock ChainAIMDMData GovernanceAnalyticsTechnology
GI➜GO!
!43Copyright 2018 by Data Blueprint Slide #
Perfect Model
Garbage Data
Garbage Results
Data Warehouse
Machine Learning
Business IntelligenceBlock Chain
AIMDM
Analytics
Technology
Data Governance
GI➜GO!
!44Copyright 2018 by Data Blueprint Slide #
Perfect Model
Quality Data
Garbage Results
Data Warehouse
Machine Learning
Business IntelligenceBlock Chain
AIMDM
Analytics
Technology
Data Governance
GI➜GO!
!45Copyright 2018 by Data Blueprint Slide #
Perfect Model
Quality Data
Garbage Results
Data Warehouse
Machine Learning
Business IntelligenceBlock Chain
AIMDM
Analytics
Technology
Data Governance
QI➜QO!
!46Copyright 2018 by Data Blueprint Slide #
Perfect Model
Quality Data
Garbage Results
Data Warehouse
Machine Learning
Business IntelligenceBlock Chain
AIMDM
Analytics
Technology
Data Governance
Quality In ➜ Quality Out!
!47Copyright 2018 by Data Blueprint Slide #
Perfect Model
Quality Data
Good Results
Data Warehouse
Machine Learning
Business IntelligenceBlock Chain
AIMDM
Analytics
Technology
Data Governance
Data Strategy and Data Governance in Context
!48Copyright 2018 by Data Blueprint Slide #
OrganizationalStrategy
Data Strategy
IT Projects
Organizational Operations
Data Governance
Data asset support for
organizational strategy
What the data assets do to support strategy
How well the data strategy is working
Operational feedback
How data is delivered by IT
How IT supports strategy
Other aspects of
organizational strategy
!49Copyright 2018 by Data Blueprint Slide #
Data Science (2011)
The Sexiest Job of the 21st Century
The Quant Crunch• We project that by 2020
the number of positions for data and analytics talent in the United States will increase ... to 2,720,000
!50Copyright 2018 by Data Blueprint Slide #
– STEVEN MILLER Global Leader, Academic Programs and Outreach IBM Analytics
– DEBBIE HUGHES Vice President, Higher Education and Workforce Business-Higher Education Forum
Data Science (2017)
!51
Growth of Data vs. Growth of Data Analysts • Stored data accumulating at
28% annual growth rate • Data analysts in workforce
growing at 5.7% growth rate
Copyright 2018 by Data Blueprint Slide #
Supply/demand for data talent
https://www.logianalytics.com/bi-trends/3-keys-understanding-data/
Munge https://en.wikipedia.org/wiki/Mung_(computer_term)• Computer jargon
• For a series of potentially destructive or irrevocable
• Changes to a piece of data or a file
• Vague data transformation steps that are not yet clearly defined
• Common munging operations include: – Removing punctuation
– Removing html tags
– Data parsing
– Filtering
– Transformation
!52Copyright 2018 by Data Blueprint Slide #
Data Science Productivity
0255075
100
Current Improved
Munging Analysis
• Munging – 80% of time munging and – the remainder analyzing data
• A 20% munging improvement decrease doubles overall productivity! – Hidden productivity bottlenecks
• Data science challenges: – Skillsets are too generalized – Not enough interest in 'learning the business' – Not productive enough
• Focusing on only one end of the equation is not sufficient – Improving productivity must be part of the complete solution!
!53Copyright 2018 by Data Blueprint Slide #
Why
Eve
ry D
ata
Scie
ntis
t Nee
ds A
D
ata
Engi
neer
!54Copyright 2018 by Data Blueprint Slide #
https://www.datasciencecentral.com/profiles/blogs/why-every-data-scientist-needs-a-data-engineer
Amelia Matteson
Other recent data "strategies"• Big Data
• Data Science
• Analytics
• SAP
• Microsoft
• AWS
• ...
!55Copyright 2018 by Data Blueprint Slide #
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
!56Copyright 2018 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
Reasons for a Data Strategy
!57Copyright 2019 by Data Blueprint Slide #
Improve your organization’s data
Separating the Wheat from the Chaff• Data that is better organized increases
in value
• Poor data management practices are costing organizations money/time/effort
• 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
!58Copyright 2018 by Data Blueprint Slide #
Incomplete
Put simply, organizations:
!59Copyright 2019 by Data Blueprint Slide #
• Have little idea what data they have • Do not know where it is (and) • Do not know what their knowledge workers do with it
Data Knowledge is too little and too informal• Data management happens 'pretty well' at
the workgroup level – Defining characteristic of a workgroup – Without guidance, what are the chances that all
workgroups are pulling toward the same objectives? – Consider the time spent attempting informal practices
• Data chaff becomes sand in the machinery – Preventing smooth interoperation and exchanges – Death by 1,000 cuts that have been difficult to account for
• Organizations and individuals lack – Knowledge – Skills – Data Engineering (how) – Data Strategy (why)
!60Copyright 2019 by Data Blueprint Slide #
Tacoma Narrows Bridge/Gallopin' Gertie• Slender, elegant and graceful
• World's 3rd longest suspension span
• Opened on July 1st, collapsed in a windstorm on November 7, 1940
• "The most dramatic failure in bridge engineering history"
• Changed forever how engineers design suspension bridges leading to safer spans today.
!61Copyright 2019 by Data Blueprint Slide #
!62Copyright 2019 by Data Blueprint Slide #
Similarly data failures cost organizationsminimally 20-40% of their IT budget
!63Copyright 2019 by Data Blueprint Slide #
Data Footprints• SQL Server
– 47,000,000,000,000 bytes – Largest table 34 billion records 3.5 TBs
• Informix – 1,800,000,000 queries/day – 65,000,000 tables / 517,000 databases
• Teradata – 117 billion records – 23 TBs for one table
• DB2 – 29,838,518,078 daily queries
!64Copyright 2019 by Data Blueprint Slide #
Running Query
!65Copyright 2019 by Data Blueprint Slide #
Optimized Query
!66Copyright 2019 by Data Blueprint Slide #
Repeat 100s, thousands, millions of times ...
!67Copyright 2019 by Data Blueprint Slide #
!68Copyright 2019 by Data Blueprint Slide #
Data incoherence is a hidden expense• How does maltreated data cost money? • 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?
• Organizations spend 20-40% of their ITbudget 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
!69Copyright 2019 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
Reasons for a Data Strategy
!70Copyright 2019 by Data Blueprint Slide #
Improve your organization’s data
Improve the way your people use its data
IT BusinessData
Perceived State of Data
!71Copyright 2018 by Data Blueprint Slide #
Data
Desired To Be State of Data
!72Copyright 2018 by Data Blueprint Slide #
IT Business
The Real State of Data
!73Copyright 2018 by Data Blueprint Slide #
DataIT Business
What do we teach knowledge workers about data?
!74Copyright 2018 by Data Blueprint Slide #
What percentage of them deal with it daily?
Widespread Ignorance!75Copyright 2018 by Data Blueprint Slide #Shoshana Zuboff, The Age of Surveillance Capitalism
Who knows Who decides and
Who decides, who decides?
OPEN Government Data Act• Signed by President Trump
on 1/14/19
• Foundations for Evidence-Based Policymaking (FEBP) Act (H.R._4174,_S._2046)
• Title II, which includes the Open, Public, Electronic, and Necessary (OPEN) Government Data Act (Title II)
!76Copyright 2018 by Data Blueprint Slide #
OPEN Government Data Act
• Title II – Requires all non-sensitive
government data to be made available in open and machine-readable formats by default
– Establishes Chief Data Officers (CDO) at federal agencies
– A CDO Council • Goals
– Improve operational efficiencies and government services
– Reduce costs – Increase public access to
government information – Spur innovation and
entrepreneurship – This is a win for evidence-
based decision-making within the government
!77Copyright 2018 by Data Blueprint Slide #
Why should a knowledge worker• with a PhD in Chemical Engineering • have to know whether FoxPro
is Y2K compliant?
!78Copyright 2019 by Data Blueprint Slide #
International Chemical Company Engine Testing• $1billion (+) chemical
company • Develops/manufactures
additives enhancing the performance of oils and fuels ...
• ... to enhance engine/machine performance
– Helps fuels burn cleaner – Engines run smoother – Machines last longer
• Tens of thousands of tests annually
– Test costs range up to $250,000!
!79Copyright 2019 by Data Blueprint Slide #
Improving Knowledge Worker Productivity
!80Copyright 2019 by Data Blueprint Slide #
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
Data Integration Solution• Integrated the existing systems to
easily search on and find similar or identical tests
• Results:
– Reduced expenses
– Improved competitive edge and customer service
– Time savings and improve operational capabilities
• According to our client, they expect to realize a $25 million gain each year thanks to these improvements
!81Copyright 2019 by Data Blueprint Slide #
V1 Organizations
without a formalizeddata strategy
V3 Data Strategy: Use data
to create strategic opportunities
V4 Data Strategy: both
Improve Operations
Inno
vatio
n
The focus of data strategy should be sequenced
!82Copyright 2019 by Data Blueprint Slide #
Only 1 is 10 organizations has a board approved data strategy!
V2 Data Strategy: Increase
organizational efficiencies/effectivenessXX
Reasons for a Data Strategy
!83Copyright 2018 by Data Blueprint Slide #
Improve your organization’s data
Improve the way your people use its data
Improve the way your data and your people
support your organizational strategy
!84Copyright 2018 by Data Blueprint Slide #
“Your Organization is all about Data,
until it’s not about just Data”
What Business are you in?
Data Strategy with Data Governance in Context
!85Copyright 2018 by Data Blueprint Slide #
OrganizationalStrategy
Data Strategy
IT Projects
Organizational Operations
Data Governance
Data asset support for
organizational strategy
What the data assets do to support strategy
How well the data strategy is working
Operational feedback
How data is delivered by IT
How IT supports strategy
Other aspects of
organizational strategy
!86Copyright 2019 by Data Blueprint Slide #
OrganizationalStrategy
Data StrategyData
Governance
Data asset support for
organizational strategy
What the data assets do to support
strategy
How well the data strategy is working
(Business Goals)
(Metadata)
IT Projects
How data is delivered by IT
Data Strategy and Governance in Strategic Context
• A data strategy specifies how data assets are to be used to support strategy – What is strategy? – What is a data
strategy? – How do they work
together?
• Strategy evolves periodically
Recap
!87Copyright 2018 by Data Blueprint Slide #
StrategicFocus
A pattern in a stream of decisions
Constraints limiting goal achievement
Reasons for a Data Strategy
!88Copyright 2018 by Data Blueprint Slide #
Improve your organization’s data
Improve the way your people use its data
Improve the way your data and your people
support your organizational strategy
• Because data points to where valuable things are located
• Because data has intrinsic value by itself
• Because data has inherent combinatorial value
• Valuing Data – Use data to
measure change – Use data to
manage change – Use data to
motivate change
• Creating a competitive advantage with data
What did Rolls Royce Learn• Old model
– Sell jet engines • New model
– Sell hours of thrust power – Power-by-the-hour – No payment for down time – Wing to wing – When was it invented?
!89Copyright 2018 by Data Blueprint Slide #
from Nascar?
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
!90Copyright 2018 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
!91Copyright 2018 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
Data Strategy Framework (Part 1)
!92Copyright 2019 by Data Blueprint Slide #
• Benefits & Success Criteria • Capability Targets • Solution Architecture • Organizational Development
Solution
• Organization Mission • Strategy & Objectives • Organizational Structures • Performance Measures
Business Needs• Organizational / Readiness • Business Processes • Data Management Practices • Data Assets • Technology Assets
Current State
• Business Value Targets • Capability Targets • Tactics • Data Strategy Vision
Strategic Data Imperatives
Business Needs
Existing Capabilities
Execution
Data Strategy is Implemented in 2 Phases
!93Copyright 2018 by Data Blueprint Slide #
Data Strategy
What the data assets do to support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
Data Strategy is Implemented in 2 Phases
!94Copyright 2018 by Data Blueprint Slide #
Data Strategy
What the data assets do to support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
!95Copyright 2019 by Data Blueprint Slide #
Making a Better Data Sandwich
!96Copyright 2019 by Data Blueprint Slide #
Standard data
Data supply
Data literacy
Making a Better Data Sandwich
!97Copyright 2019 by Data Blueprint Slide #
Data literacy
Standard data
Data supply
Making a Better Data Sandwich
!98Copyright 2019 by Data Blueprint Slide #
Standard data
Data supply
Data literacy
Making a Better Data Sandwich
!99Copyright 2019 by Data Blueprint Slide #
Standard data
Data supply
Data literacy
This cannot happen without engineering and architecture!
Quality engineering/architecture work products do not happen accidentally!
Making a Better Data Sandwich
!100Copyright 2019 by Data Blueprint Slide #
Standard data
Data supply
Data literacy
This cannot happen without data engineering and architecture!
Quality data engineering/ architecture work products do not happen accidentally!
Arc
hite
ctur
e Ja
rgon
!101Copyright 2019 by Data Blueprint Slide #
Engineering
Architecture
Engineering/Architecting Relationship• Architecting is used to
create and build systems too complex to be treated by engineering analysis alone – Require technical details as
the exception
• Engineers develop the technical designs – Engineering/Crafts-persons
deliver components supervised by: • Manufacturer • Building Contractor
!102Copyright 2019 by Data Blueprint Slide #
Niccolo Machiavelli (1469-1527)
He who has not first laidhis 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
!103Copyright 2019 by Data Blueprint Slide #
You cannot architect after implementation!
!104Copyright 2019 by Data Blueprint Slide #
Good Architectural Foundation?
!105Copyright 2019 by Data Blueprint Slide #
Poor Foundation =
!106Copyright 2019 by Data Blueprint Slide #
Unsuitable for
Further Investment
What they think they are purchasing!
!107Copyright 2019 by Data Blueprint Slide #
The New Structural Engineer
!108Copyright 2019 by Data Blueprint Slide #
USS Midway & Pancakes
What is this?
• It is tall • It has a clutch • It was built in 1942 • It is cemented to the floor • It is still in regular use!
!109Copyright 2018 by Data Blueprint Slide #
Engineering Standards
!110Copyright 2019 by Data Blueprint Slide #
Concrete Blocks & Engineering Continuity
Copyright 2019 by Data Blueprint Slide # !111
!112Copyright 2019 by Data Blueprint Slide #
Data Strategy in Context
!113Copyright 2019 by Data Blueprint Slide #
OrganizationalStrategy
IT Strategy
Data Strategy
OrganizationalStrategy
IT Strategy
Data Strategy
This is wrong!
!114Copyright 2019 by Data Blueprint Slide #
OrganizationalStrategy
IT Strategy
Data Strategy
OrganizationalStrategy
IT Strategy
This is correct …
!115Copyright 2019 by Data Blueprint Slide #
Data Strategy
!116Copyright 2019 by Data Blueprint Slide #
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
!117Copyright 2019 by Data Blueprint Slide #
!118Copyright 2019 by Data Blueprint Slide #
!119Copyright 2019 by Data Blueprint Slide #
Credit: Image credit: Matt Vickers
!120Copyright 2018 by Data Blueprint Slide #
CIOs aren't
!121Copyright 2019 by Data Blueprint Slide #
Chief Data Officer Combat
• Recasting the executive team. make full use of the most valuable assets
!122Copyright 2018 by Data Blueprint Slide #
Change the status quo!• Keep in mind that the appointment of a
CDO typically comes from a high-level decision. In practice, it can trigger an array of problematic reactions within the organization including: – Confusion, – Uncertainty, – Doubt, – Resentment and – Resistance.
• CDOs need to rise to the challenge of changing the status quo if they expect to lead the business in making data a strategic asset. – from What Chief Data Officers Need to Do to
Succeed by Mario Faria https://www.forbes.com/sites/gartnergroup/2016/04/11/what-chief-data-officers-need-to-do-to-succeed/#734d53a8434a
!123Copyright 2018 by Data Blueprint Slide #
Change Management & Leadership
!124Copyright 2018 by Data Blueprint Slide #
Diagnosing Organizational Readiness
!125Copyright 2018 by Data Blueprint Slide #
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!
QR Code for PeterStudy
• Free Case Study Download• Free Case Study Download – http://dl.acm.org/citation.cfm?doid=2888577.2893482
or
http://tinyurl.com/PeterStudy or scan the QR Code at the right
!126Copyright 2018 by Data Blueprint Slide #
Data Strategy is Implemented in 2 Phases
!127Copyright 2018 by Data Blueprint Slide #
Data Strategy
What the data assets do to support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
Data Strategy is Implemented in 2 Phases
!128Copyright 2018 by Data Blueprint Slide #
Data Strategy
What the data assets do to support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
Enron• Fortune named Enron "America's Most Innovative Company"
for six consecutive years • Suffered the largest Chapter 11 bankruptcy in history
(up to that time) • August 2001: $90.00 → $42.00 → $0.26 • Dynegy (several $ billion) 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 Dynegy for more $? • Dynegy: What happened to the
several $ billion I gave you last week? • Enron:
http://en.wikipedia.org/wiki/Enron
!129Copyright 2019 by Data Blueprint Slide #
Enro
n Sc
hool
of A
ccou
ntin
g
!130Copyright 2019 by Data Blueprint Slide #
CFO Necessary Prerequisites/Qualifications• CPA
• CMA
• Masters of Accountancy
• Other recognized degrees/certifications
• These are necessary but insufficient prerequisites/qualifications
!131Copyright 2019 by Data Blueprint Slide #
• 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
What do we teach IT professionals about data?
!132Copyright 2018 by Data Blueprint Slide #
!133Copyright 2018 by Data Blueprint Slide #
If the only tool you know is a hammer, every problem tends to look like a nail (slightly reworded from A. Maslow)
The disks no longer rotate!
!134Copyright 2019 by Data Blueprint Slide #
Incorrect Educational Focus?• Building new systems
– 80% of IT costs are spent rebuilding and evolving existing systems and only 20% of costs are spent building and acquiring new systems
– Putting fresh graduates on new projects makes this proposition more ridiculous
– Only the most experienced professionals are typically allowed to participate in new systems development
!135Copyright 2019 by Data Blueprint Slide #
Bad Data Decisions Spiral
!136Copyright 2019 by Data Blueprint Slide #
Bad data decisions
Technical deci-sion makers are not data knowledgable
Business decision makers are not
data knowledgable
Poor organizational outcomes
Poor treatment of organizational data
assets
Poorqualitydata
A Singular Focus• Chief
– The head or leader of an organized body of people; the person highest in authority: the chief of police
• Chief Financial Officer (CFO) – Individual possessing the knowledge, skills, and abilities to be both
the final authority and decision-maker in organizational financial matters
• Chief Risk Officer (CRO) – Individual possessing the knowledge, skills, and abilities makes
decisions and implements risk management
• Chief Medical Officer (CMO) – Responsible for organizational medical matters. The organization,
and the public, has similar expectations for any of chief officer – especially after the Sarbanes-Oxley bill.
!137Copyright 2019 by Data Blueprint Slide #
[dictionary.com]
• Chief – The head or leader of an organized body of people;
the person highest in authority: the chief of police
• Chief Financial Officer (CFO) ← does not balance books – Individual possessing the knowledge, skills, and abilities to be both
the final authority and decision-maker in organizational financial matters
• Chief Risk Officer (CRO) ← does not test software – Individual possessing the knowledge, skills, and abilities makes
decisions and implements risk management
• Chief Medical Officer (CMO) ← does not perform surgery – Responsible for organizational medical matters. The organization,
and the public, has similar expectations for any of chief officer – especially after the Sarbanes-Oxley bill.
• Dedicated solely to data asset leveraging
• Unconstrained by an IT project mindset
• Reporting to the business
• 90 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2019 (Gartner website accessed January 26, 2016 http://www.gartner.com/newsroom/id/3190117?)
Top
Operations Job
Top Data Job
!138Copyright 2019 by Data Blueprint Slide #
Top Job
Top
Finance Job
Top IT
Job
Top
Marketing Job
Data Governance Organization
Top Data Job
Enterprise
Data Executive
Chief Data
Officer
Develop the first version of an organizational data strategy
Inventory data assets->decrease data ROT
Monetize your organization's data
!139Copyright 2019 by Data Blueprint Slide #
CDO Agenda The CDOs goal is to better manage data as an organizational asset in support of the organizational mission!
OPEN Government Data Act§ 3520. Chief Data Officers1. Lifecycle2. Coordinate agency data needs3. Lawful data management4. Consult with agency statistical official5. Agency requirements6. Conforms with best data management practices7. Encourage collaborative approaches improving
data use8. Support the Performance Improvement Officer9. Support the Evaluation Officer10. Review infrastructure impact on data asset
accessibility/coordinate with the Chief Information Officer
11. Ensure maximizes agency data use12. Identify points of contact for open data use13. Liaison to other agencies and the OMB on the
best way to use existing agency data for statistical purposes
14. Comply with any regulation and guidance issued under subchapter III, including the acquisition and maintenance of any required certification and training.
!140Copyright 2018 by Data Blueprint Slide #
Hiring Panels Are Often Not Qualified to Help
!141Copyright 2018 by Data Blueprint Slide #
Unicorn License (There Are No Unicorns)
!142Copyright 2018 by Data Blueprint Slide #
!143Copyright 2018 by Data Blueprint Slide #
The Enterprise Data Executive Takes One for the Team
Chief Electrification Officer
• 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.
!144Copyright 2019 by Data Blueprint Slide #
Not all roles are needed always!
Data Strategy is Implemented in 2 Phases
!145Copyright 2018 by Data Blueprint Slide #
Data Strategy
What the data assets do to support strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)
!146Copyright 2018 by Data Blueprint Slide #
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
Not Understanding Data-Centric Thinking
Lacking Qualified Data Leadership
Not implementing a Robust, Programmatic Means of Developing Shared Data
Not Aligning The Data Program with IT Projects
Failing to Adequately Manage Expectations
Not Sequencing Data Strategy Implementation
Failing To Address Cultural And Change Management Challenges
!147Copyright 2019 by Data Blueprint Slide #
Exorcising the Seven Deadly Data Sins
NotUnderstandingData-CentricThinking
LackingQualifiedDataLeadership
FailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7
NotUnderstandingData-CentricThinking
LackingQualifiedDataLeadership
FailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7NotUnderstandingData-
CentricThinkingLackingQualifiedData
LeadershipFailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7NotUnderstandingData-
CentricThinkingLackingQualifiedData
LeadershipFailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7
NotUnderstandingData-CentricThinking
LackingQualifiedDataLeadership
FailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7
NotUnderstandingData-CentricThinking
LackingQualifiedDataLeadership
FailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7NotUnderstandingData-
CentricThinkingLackingQualifiedData
LeadershipFailingtoImplementaProgrammaticWayto
ShareData
NotAligningtheDataProgramwithITProjects
FailingtoAdequatelyManageExpectations
NotSequencingDataStrategyImplementation
NotAddressingCulturalandChange
ManagementChallenges
1 2 3 4
5 6 7
George Box British Statistician
(1919-2013)
“All models are wrong, ... ... some are useful.”
!148Copyright 2019 by Data Blueprint Slide #
theDataDoctrine.comWe are uncovering better ways of developing
IT systems by doing it and helping others do it.Through this work we have come to value:
Data programmes preceding software development Stable data structures preceding stable code
Shared data preceding completed software Data reuse preceding reusable code
!149Copyright 2019 by Data Blueprint Slide #
That is, while there is value in the items on
the right, we value the items on the left more.
Data programmes preceding software development
!150Copyright 2019 by Data Blueprint Slide #
Common Organizational Data (and corresponding data needs requirements)
New Organizational Capabilities
Systems Development
Activities
Build
Evolve
Future State
(Version +1)
Data evolution is separate from, external to, and precedes system development life cycle activities!
Data management and software
development must be separated and
sequenced
Mismatched railroad tracks non aligned
Copyright 2017 by Data Blueprint Slide #!151
Data programmes preceding software development
theDataDoctrine.comWe are uncovering better ways of developing
IT systems by doing it and helping others do it.Through this work we have come to value:
Data programmes preceding software development Stable data structures preceding stable code
Shared data preceding completed software Data reuse preceding reusable code
!152Copyright 2019 by Data Blueprint Slide #
That is, while there is value in the items on
the right, we value the items on the left more.
Stable data structures preceding stable code
!153Copyright 2019 by Data Blueprint Slide #
Person Job Class
Position
BR1) One EMPLOYEE can be associated with one
PERSON
BR2) One EMPLOYEE can be associated with one POSITION
ManualJob Sharing
ManualMoon Lighting
Employee
Stable data structures preceding stable code
!154Copyright 2019 by Data Blueprint Slide #
Person Job Class
Employee Position
BR1) Zero, one, or more EMPLOYEES can be associated
with one PERSON
BR2) Zero, one, or more EMPLOYEES can be associated with one POSITION
Job Sharing
Moon Lighting
Stable data structures preceding stable code
!155Copyright 2019 by Data Blueprint Slide #
Data structures must be specified prior software development/acquisition
(Requires 2 structural loops more than the more flexible data structure)
More flexible data structure Less flexible data structure
theDataDoctrine.comWe are uncovering better ways of developing
IT systems by doing it and helping others do it.Through this work we have come to value:
Data programmes preceding software development Stable data structures preceding stable code
Shared data preceding completed software Data reuse preceding reusable code
!156Copyright 2019 by Data Blueprint Slide #
That is, while there is value in the items on
the right, we value the items on the left more.
Results
Increasing utility of organizational data
Individual IT Project
Requirements
Design
Implement
Requests Results
Individual IT Project
Requirements
Design
Implement
Requests
Results
Individual IT Project
Requirements
Design
Implement
Requests
Organized, shared data
Organized, shared data
Organized, shared data
Shared Data preceding completed software
!157Copyright 2019 by Data Blueprint Slide #
• Over time the: – Number of requests increase – Utility of the results increase – Data's contribution increases – and is recognized!
Shared data structures cannot exist without
programmatic development and evaluation
theDataDoctrine.comWe are uncovering better ways of developing
IT systems by doing it and helping others do it.Through this work we have come to value:
Data programmes preceding software development Stable data structures preceding stable code
Shared data preceding completed software Data reuse preceding reusable code
!158Copyright 2019 by Data Blueprint Slide #
That is, while there is value in the items on
the right, we value the items on the left more.
Program F
Program EProgram H
Program I
domain 2Applicationdomain 3
Data reuse preceding reusable code • Reusable software has been valued more than reusable data • Who makes decisions about the range and scope of common
data usage? • Change a program
– 9 max changes
• Change data – Worst case – (N * (N - 1)) / 2 – (9 * 8)/2 = 36
!159Copyright 2019 by Data Blueprint Slide #
Program DProgram G
Application
theDataDoctrine.comWe are uncovering better ways of developing
IT systems by doing it and helping others do it.Through this work we have come to value:
Data programmes preceding software development Stable data structures preceding stable code
Shared data preceding completed software Data reuse preceding reusable code
!160Copyright 2019 by Data Blueprint Slide #
That is, while there is value in the items on
the right, we value the items on the left more. http://www.thedatadoctrine.com
Your Data Strategy:It Should Be Concise, Actionable, and Understandable by Business & IT!
!161Copyright 2018 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
Data Strategy is Implemented in 2 Phases
!162Copyright 2018 by Data Blueprint Slide #
Data Strategy
Phase I-Prerequisites
1) Prepare for dramatic change and determine how to do the work
2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent)
3) Eliminate the Seven Deadly Data Sins
Phase II-Iterations (Lather, Rinse, Repeat)You are here
1) Identify the primary constraint keeping data from fully supporting strategy
2) Exploit organizational efforts to remove this constraint
3) Subordinate everything else to this exploitation decision
4) Elevate the data constraint
5) Repeat the above steps to address the new constraint
Data Strategy Framework (Part 2)
!163Copyright 2019 by Data Blueprint Slide #
• Benefits & Success Criteria • Capability Targets • Solution Architecture • Organizational Development
Solution
• Leadership & Planning • Project Dev. & Execution • Cultural Readiness
Road Map
• Organization Mission • Strategy & Objectives • Organizational Structures • Performance Measures
Business Needs• Organizational / Readiness • Business Processes • Data Management Practices • Data Assets • Technology Assets
Current State
• Business Value Targets • Capability Targets • Tactics • Data Strategy Vision
Strategic Data Imperatives
Business Needs
Existing Capabilities
ExecutionBusiness Value
New Capabilities
• A data strategy specifies how data assets are to be used to support strategy – What is strategy? – What is a data strategy? – How do they work
together?
• Strategy evolves periodically – Determine the most
fixable/highest value constraint
– Cycle through – If constraint not
addressed, start over
Recap
!164Copyright 2018 by Data Blueprint Slide #
Alleviate
A pattern in a stream of decisions
Constraints limiting goal achievement
By the Book
!165Copyright 2019 by Data Blueprint Slide #
Data Strategy
Data Governance
Strategy
Metadata Strategy
Data Quality
Strategy
BI/Warehouse
Strategy
DataArchitecture
Strategy
Master/Reference
DataStrategy
Document/ContentStrategy
DatabaseStrategy
DataAcquisition
StrategyX
Version 1
!166Copyright 2019 by Data Blueprint Slide #
Data Strategy
Data Governance
Strategy
Data Quality
Strategy
Master/Reference
Data Strategy
Perfecting operations in
3 data management practice areas
Version 2
!167Copyright 2019 by Data Blueprint Slide #
Data Strategy
Data Governance
Strategy
BI/Warehouse
Strategy
DataOperations
Strategy
Perfecting operations in
3 data management practice areas
• A management paradigm that views any manageable system as being limited in achieving more of its goals by a small number of constraints
• There is always at least one constraint, and TOC uses a focusing process to identify the constraint and restructure the rest of the organization to address it
• TOC adopts the common idiom "a chain is no stronger than its weakest link," processes, organizations, etc., are vulnerable because the weakest component can damage or break them or at least adversely affect the outcome
!168Copyright 2019 by Data Blueprint Slide #
https://en.wikipedia.org/wiki/Theory_of_constraints
(TOC)
Each cycle has an articulated purpose
!169Copyright 2019 by Data Blueprint Slide #
Theory of Constraints - Generic
!170Copyright 2018 by Data Blueprint Slide #
Identify the current constraints, the components of the system limiting goal realization
Make quick improvements to the constraint using existing resources
Review other activities in the process facilitate proper alignment and support of constraint
If the constraint persists, identify other
actions to eliminate the constraint
Repeat until the constraint is
eliminated
Alleviate
Theory of Constraints at work improving your data
!171Copyright 2018 by Data Blueprint Slide #
In your analysis of how organization data can best support organizational strategy one thing is blocking you most - identify it!
Try to fix it rapidly with out restructuring (correct it operationally)
Improve existing data evolution activities to ensure singular focus on the current objective
Restructure to address constraint
Repeat until data better supports
strategy
Alleviate
Increasing Implementation Effectiveness
!172Copyright 2018 by Data Blueprint Slide #
Focus evolves from reactive to proactive
!173Copyright 2018 by Data Blueprint Slide #
Cyclic, iterative application
Over time increase capacity and improve
StrategyCycle
!174Copyright 2019 by Data Blueprint Slide #
Sample: Reengineering the Location Data Element• First, fix the prerequisites! • The problem:
– Issuing new store numbers – Spreadsheet based – Pervasive – Not governed – Would issue the last available store number this year
• The solution: 1. Identify-data/systems inventory 2. Exploit-3 digit expanded to 10 digits 3. Subordinate-prioritize and sequence remediation 4. Elevate-EXECUTE! 5. Repeat the above steps to address the new constraint
!175Copyright 2019 by Data Blueprint Slide #
• Must turn in if not used for 90 days
• Many providers of mobile phones
• Warehouse integration strategy
– Months and millions to develop technologies
– Dozens of ETL streams to feed the warehouse
• Does not address some fundamental questions
– It has been 88 days, it that close enough?
– It has been 92 days, should I wait a bit more?
• Reset - 3 billing cycles
– Eliminates technology need - work level is clerical not technical
Mobile Phone Policy
!176Copyright 2019 by Data Blueprint Slide #
Improving Data Quality during System Migration• Challenge
– Millions of NSN/SKUs maintained in a catalog
– Key and other data stored in clear text/comment fields
– Original suggestion was manual approach to text extraction
– Left the data structuring problem unsolved • Solution
– Proprietary, improvable text extraction process – Converted non-tabular data into tabular data – Saved a minimum of $5 million
– Literally person centuries of work
!177Copyright 2019 by Data Blueprint Slide #
Unmatched Items
Ignorable Items
Items Matched
Week # (% Total) (% Total) (% Total)1 31.47% 1.34% N/A2 21.22% 6.97% N/A3 20.66% 7.49% N/A4 32.48% 11.99% 55.53%
… … … …14 9.02% 22.62% 68.36%15 9.06% 22.62% 68.33%16 9.53% 22.62% 67.85%17 9.5% 22.62% 67.88%18 7.46% 22.62% 69.92%
Determining Diminishing Returns
!178Copyright 2019 by Data Blueprint Slide #
BeforeAfter
Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Quantitative Benefits
!179Copyright 2019 by Data Blueprint Slide #
Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Quantitative Resulting Costs
!180Copyright 2019 by Data Blueprint Slide #
Time needed to review all NSNs once over the life of the project:NSNs 150,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 750,000
Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Minutes needed 750,000Minutes available person/year 108,000Total Person-Years 7
Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 7Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $420,000
Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Quantitative Benefits
!181Copyright 2019 by Data Blueprint Slide #
Logistics Company• Fortune 450
• Room of 100 associates
• Manually correcting everyitem on every customer invoice
• Upon noting this to the responsible manager - the reply was: – This is the best quarter
– Of the best year
– I've ever had
– Perhaps I need to double the number in that room?
!182Copyright 2018 by Data Blueprint Slide #
Example : Data Integration and Platforms• Straight Through
Processing – Auto Everything
• Workflow Event Mgr – 360 View of the
order • Exception Manager
– Alert and Notification system when human involvement is needed
!183Copyright 2019 by Data Blueprint Slide #
85%
Data Management Program Expenses
• 5 Data Professionals
• $100,000 each/annually
• When will you be done?
• "It's okay my CIO gave me 5 years!"
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improving how the state prices and sells its goods and services, and more efficiently matching citizens to benefits when they enroll.
“The first year of our data internship partnership has been a success,” said Governor McAuliffe. “The program has helped the state save time and money by making some of our internal processes more efficient and modern. And it has given students valuable real-world experience. I look forward to seeing what the second year of the program can accomplish.”
“Data is an important resource that becomes even more critical as technology progresses,” said VCU President Michael Rao, Ph.D. “VCU is uniquely positioned, both in its location and through the wealth of talent at the School of Business, to help state agencies run their data-centric systems more efficiently, while giving our students hands-on practice in the development of data systems.”
During their internships, pairs of VCU students work closely with state agency CIOs to identify specific business cases in which data can be used. Participants gain practical experience in using data to drive re-engineering, while participating CIOs have concrete examples of how to make better use of data to provide innovative and less costly services to citizens.
"Working with the talented VCU students gave us a different perspective on what the data was telling us,” said Dave Burhop, Deputy Commissioner/CIO of the Virginia Department of Motor Vehicles.
“The VCU interns provided an invaluable resource to the Governor’s Coordinating Council on Homelessness,” said Pamela Kestner, Special Advisor on Families, Children and Poverty. “They very effectively reviewed the data assets available in the participating state agencies and identified analytic content that can be used to better serve the homeless population.”
“It's always useful to have ‘fresh eyes’ on data that we are used to seeing,” said Jim Rothrock, Commissioner of the Department for Aging and Rehabilitative Services. “Our interns challenged us and the way we interpret data. It was a refreshing and useful, and we cannot wait for new experiences with new students.” The data internships support Governor McAuliffe’s ongoing initiative to provide easier access to open data in Virginia. The internships also support treating data as an enterprise asset, one of four strategic goals of the enterprise information architecture strategy adopted by the Commonwealth in August 2013. Better use of data allows the Commonwealth to identify opportunities to avoid duplicative costs in collecting, maintaining and using information; and to integrate services across agencies and localities to improve responses to constituent needs and optimize government resources. Virginia Secretary of Technology Karen Jackson and CIO of the Commonwealth Nelson Moe are leading the effort on behalf of the state. Students who want to apply for internships should contact Peter Aiken ([email protected]) for additional information.
!185Copyright 2019 by Data Blueprint Slide #
Virginia Governor's Data Interns Program
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
Anonymous Identifier Crime Sentence Gender Race Disability LEP …1 92G-gqP-6ek-oy3 A 30 M W 0 N
2
3
Hash Function(Replaces PII with Anonymous Identifier)
Hashing Process Illustrated
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Data Amalgamation Process
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Hash Function
CorrectionsDJJHealth DSS DMAS …
Hash Function
Hash Function
Hash Function
Hash Function
VCU Dataset (No PII)
Watson Analytics
Form
aliz
ing
the
Rol
e of
U.S
. Arm
y D
ata
Gov
erna
nce
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Suicide Mitigation
!189Copyright 2019 by Data Blueprint Slide #
Potential Data Sources
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Data Mapping
12
Mental illness
Deployments
Work History
Soldier Legal Issues
Abuse
Suicide Analysis
FAPDMSS G1 DMDC CID
Data objects complete?
All sources identified?
Best source for each object?
How reconcile differences between sources?
MDR
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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 29 30123456789101112131415161718192021222324252627282930
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Senior Army Official• Room full of Stewards
• A very heavy dose of management support
• Advised the group of his opinion on the matter
• Any questions as to future direction
– "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
!194Copyright 2019 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
Data Strategy and the Information Executive
!195Copyright 2018 by Data Blueprint Slide #
• A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together?
• A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy
• Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data,
the seven deadly data sins
• Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required
The Abbreviated State
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http://www.reelhipster.com/2016/05/the-abbreviated-state/
The Abbreviated State
!197Copyright 2018 by Data Blueprint Slide #
An imaginary documentary about abbreviating all 50 States down to two letter codes made up by Gary Gulman for this 7-13-2016 stand-up routine on CONAN • https://www.youtube.com/watch?v=dLECCmKnrys
28 of the fifty
states did not
conform to the
original algorithm
Questions? + = !198Copyright 2019 by Data Blueprint Slide #
10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056
Copyright 2018 by Data Blueprint Slide # !199