your data strategy - o'reilly media

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


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


+ =

https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn

Please raise your hand when you recognize this song






• 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


https://ctb.ku.edu/en/table-of-contents/structure/strategic-planning/develop-strategies/main


https://www.cio.com/article/3088208/leadership-management/how-to-avoid-a-digital-strategy-failure.html

Simon Sinek: How great leaders inspire action


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?


Use the original definition derived from military ... • “a pattern in a stream of decisions”

– [Henry Mintzberg]

Former Walmart Business Strategy


Every Day Low Price

Wayne Gretzky’sDefinition of Strategy


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”



Sup

ply

Line

Met

adat

a

(as

part

of a

div

ide

and

conq

uer s

trate

gy)

First Divide


Then Conquer


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


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


Strategy Guides Workgroup Activities


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


Alleviate the Constraint

StrategyCycle


StrategyCycle


StrategyCycle


StrategyCycle

Note: Numerous upfront assumptions are required because plans must be detailed, specifying end objectives

Strategy Example 1


Good Guys (Us)

Bad Guys (Them)

Strategy Example 2


Good Guys (Us)

Bad Guys (Them)

Strategy that winds up only on a shelf is not useful


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


System

DataHardwareProcessesPeople Software Data

TWITTERUSERS SEND

473 400 TWEETS

SKYPEUSERS MAKE

176 220CALLS

INSTAGRAM

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

• ...


https://www.domo.com/learn/data-never-sleeps-6


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 ...


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

– Knowledge – Skills – Abilities

• Financial • Organizational reputation • Good will • Brand name • Data!!!

Organizational Assets






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


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]


• 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


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?


Focus evolve from reactive to proactive


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


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


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


https://people.well.com/user/adatoole/bio.htm

Recent Technology Realization


Garbage In➜Garbage Out!Recent

Garbage In ➜ Garbage Out!


GI➜GO!


Perfect Model

Garbage Data

Garbage Results

Data Warehouse

Machine LearningBusiness

IntelligenceBlock ChainAIMDMData GovernanceAnalyticsTechnology

GI➜GO!


Perfect Model

Garbage Data

Garbage Results

Data Warehouse

Machine Learning

Business IntelligenceBlock Chain

AIMDM

Analytics

Technology

Data Governance

GI➜GO!


Perfect Model

Quality Data

Garbage Results

Data Warehouse

Machine Learning


AIMDM

Analytics

Technology

Data Governance

GI➜GO!


Perfect Model

Quality Data

Garbage Results

Data Warehouse

Machine Learning


AIMDM

Analytics

Technology

Data Governance

QI➜QO!


Perfect Model

Quality Data

Garbage Results

Data Warehouse

Machine Learning


AIMDM

Analytics

Technology

Data Governance

Quality In ➜ Quality Out!


Perfect Model

Quality Data

Good Results

Data Warehouse

Machine Learning


AIMDM

Analytics

Technology

Data Governance

Data Strategy and Data Governance in Context


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



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


– 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


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!


Why

Eve

ry D

ata

Scie

ntis

t Nee

ds A

D

ata

Engi

neer


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

• Google

• AWS

• ...












Reasons for a Data Strategy


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


Incomplete

Put simply, organizations:


• 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)


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.



Similarly data failures cost organizationsminimally 20-40% of their IT budget


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


Running Query


Optimized Query


Repeat 100s, thousands, millions of times ...



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








Improve the way your people use its data

IT BusinessData

Perceived State of Data


Data

Desired To Be State of Data


IT Business

The Real State of Data


DataIT Business

What do we teach knowledge workers about data?


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)


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


Why should a knowledge worker• with a PhD in Chemical Engineering • have to know whether FoxPro

is Y2K compliant?


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!


Improving Knowledge Worker Productivity


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


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


Only 1 is 10 organizations has a board approved data strategy!

V2 Data Strategy: Increase

organizational efficiencies/effectivenessXX





Improve the way your data and your people

support your organizational strategy


“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



Data Strategy

IT Projects

Organizational Operations

Data Governance





Operational feedback


How IT supports strategy

Other aspects of




Data StrategyData

Governance



What the data assets do to support

strategy


(Business Goals)

(Metadata)

IT Projects


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


StrategicFocus


Constraints limiting goal achievement





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?


from Nascar?





















Data Strategy Framework (Part 1)


• 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


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)



Data Strategy








Making a Better Data Sandwich


Standard data

Data supply

Data literacy



Data literacy

Standard data

Data supply



Standard data

Data supply

Data literacy



Standard data

Data supply

Data literacy

This cannot happen without engineering and architecture!

Quality engineering/architecture work products do not happen accidentally!



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


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


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


You cannot architect after implementation!


Good Architectural Foundation?


Poor Foundation =


Unsuitable for

Further Investment

What they think they are purchasing!


The New Structural Engineer


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!


Engineering Standards


Concrete Blocks & Engineering Continuity



Data Strategy in Context



IT Strategy

Data Strategy


IT Strategy

Data Strategy

This is wrong!



IT Strategy

Data Strategy


IT Strategy

This is correct …


Data Strategy


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




Credit: Image credit: Matt Vickers


CIOs aren't


Chief Data Officer Combat

• Recasting the executive team. make full use of the most valuable assets


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


Change Management & Leadership


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!

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




Data Strategy









Data Strategy







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


Enro

n Sc

hool

of A

ccou

ntin

g


CFO Necessary Prerequisites/Qualifications• CPA

• CMA

• Masters of Accountancy

• Other recognized degrees/certifications

• These are necessary but insufficient prerequisites/qualifications


• 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?



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!


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


Bad Data Decisions Spiral


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.


[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


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


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.


Hiring Panels Are Often Not Qualified to Help


Unicorn License (There Are No Unicorns)



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.


Not all roles are needed always!



Data Strategy








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












































Exorcising the Seven Deadly Data Sins

NotUnderstandingData-CentricThinking

LackingQualifiedDataLeadership

FailingtoImplementaProgrammaticWayto

ShareData

NotAligningtheDataProgramwithITProjects

FailingtoAdequatelyManageExpectations

NotSequencingDataStrategyImplementation

NotAddressingCulturalandChange

ManagementChallenges

1 2 3 4

5 6 7




ShareData






1 2 3 4

5 6 7NotUnderstandingData-

CentricThinkingLackingQualifiedData

LeadershipFailingtoImplementaProgrammaticWayto

ShareData






1 2 3 4




ShareData






1 2 3 4

5 6 7




ShareData






1 2 3 4

5 6 7




ShareData






1 2 3 4




ShareData






1 2 3 4

5 6 7

George Box British Statistician

(1919-2013)

“All models are wrong, ... ... some are useful.”


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


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


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








Stable data structures preceding stable code


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



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



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








Results

Increasing utility of organizational data

Individual IT Project

Requirements

Design

Implement

Requests Results


Requirements

Design

Implement

Requests

Results


Requirements

Design

Implement

Requests

Organized, shared data



Shared Data preceding completed software


• 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








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


Program DProgram G

Application







the right, we value the items on the left more. http://www.thedatadoctrine.com













Data Strategy





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)


• 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


Alleviate


Constraints limiting goal achievement

By the Book


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


Data Strategy

Data Governance

Strategy

Data Quality

Strategy

Master/Reference

Data Strategy

Perfecting operations in

3 data management practice areas

Version 2


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


https://en.wikipedia.org/wiki/Theory_of_constraints

(TOC)

Each cycle has an articulated purpose


Theory of Constraints - Generic


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


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


Focus evolves from reactive to proactive


Cyclic, iterative application

Over time increase capacity and improve

StrategyCycle


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


• 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


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


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


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






Quantitative Resulting Costs


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


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





Quantitative Benefits


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?


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


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!"

Copyright 2017 by Data Blueprint Slide #!184

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.


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


Data Amalgamation Process


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


Suicide Mitigation


Potential Data Sources


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



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


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





Data Strategy and the Information Executive







The Abbreviated State


http://www.reelhipster.com/2016/05/the-abbreviated-state/

The Abbreviated State


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


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