building a data-centric strategy and roadmap
TRANSCRIPT
Implementing a Data-centric Strategy & RoadmapFocus on what really matters …
Presented by Peter Aiken, Ph.D. and Lewis Broome
Copyright 2014 by Data Blueprint
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• 30+ years DM experience
• 9 books/many articles
• Experienced with 500+ data management practices
• Multi-year immersions: US DoD, Nokia, Deutsche Bank, Wells Fargo, & Commonwealth of VA
Lewis Broome Peter Aiken • CEO Data Blueprint • 20+ years in data
management • Experienced leader
driving global solutions for Fortune 100 companies
• Creatively disrupting the approach to data management
• Published in multiple industry periodicals
We believe ...
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 √ √ √ √
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Copyright 2015 by Data Blueprint
• Today, data is the most powerful, yet underutilized and poorly managed organizational asset
• Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic
• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!
• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
A popular quote from Bill Gates• Virtually everything in business
today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose. – Bill Gates
4Copyright 2015 by Data Blueprint
That quote in context• Application design and business are
now irrevocably linked. According to Bill Gates, “Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose. How you use information may be the one factor that determines its failure or success or runaway success” – Bill Gates
The Sunday Times 1999
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Copyright 2014 by Data Blueprint
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
Einstein Quote
Copyright 2013 by Data Blueprint
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"The significant problems we face cannot be solved at the same level of thinking we were at when we created them."- Albert Einstein
What
How
Simon Sinek: How great leaders inspire action
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Why
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
“…it’s not what you do, it’s why you do it”
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Why Data is Creating a Competitive Advantage
• Adds value to products & Services • Enhances the customer experience • Creates transparency & efficiencies • High-quality data enables ‘more with less’ • Creatively disrupts how we work • Volume & velocity exerting
pressure on operating models & infrastructure
“…it’s not what you do, it’s why you do it” – Simon Sinek
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
Copyright 2014 by Data Blueprint
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What is a Strategy?
• Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg]
• "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint]
Strategy in Action: Napoleon defeats a larger enemy
• Question?
– How to I defeat the competition when their forces are bigger than mine?
• Answer:
– Divide and conquer!
– “a pattern in a stream of decisions”
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– “a pattern in a stream of decisions”
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Wayne Gretzky’sDefinition of Strategy
He skates to where he thinks the puck will be ...
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The Importance of Strategy: Data Strategy in Context
Organizational
IT Strategy
Data Strategy
Organizational Strategy is Difficult to Perceive at the IT Project Level
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1 Organizational
Strategy
1 Set of Organizational
Goals/Objectives
• If they exist ... • A singular
organizational strategy and set of goals/objectives ...
• Are not perceived as such at the project level and ...
• What does exist is confused, inaccurate, and incomplete
• IT projects do not well reflect organizational strategy
Division/Group/Project
Q1 Keeping the doors open
(little or no proactive data management)
Q2 Increasing organizational efficiencies/effectiveness
Q3 Using data to create
strategic opportunities Q4 Both
(Cash Cow)
Improve Operations
Inno
vatio
n
Only 1 is 10 organizations has a board approved data strategy!
Enterprise Data Strategy Choices
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What to Expect from a Data Strategy
16Copyright 2015 by Data Blueprint
• Forces an understanding of data's importance
• Creates a vision for the organization
• Identifies the strategic imperatives
• Defines the benefits and key measures
• Describes needed data management improvements
• 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)
WHAT It take to make it
happen
• Benefits & Success Criteria • Capability Targets • Solution Architecture • Organizational Development
Solution
Data Strategy Framework
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• 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
Copyright 2014 by Data Blueprint
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
Copyright 2014 by Data Blueprint
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
Copyright 2014 by Data Blueprint
Common Problem
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No, I don’t see any problem with the data
Me either!
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Analyzing the Business
Business Goals & Objectives
Operating Model
Competitive Advantage
Market Positioning
Mission & BrandWhy a Company Exists
What a Company Produces & Sells
How a Company Does It
Business Needs
• Business Value Targets • Capability Targets • Tactics • Data Strategy Vision
Strategic Data Imperatives
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Mission & Brand Promises
Mission
Brand Architecture
Brand Clues
Underlying Values and Culture
A mission statement is a statement of the purpose of a
company; its reason for existing; a written declaration of an
organization's core purpose and focus that normally
remains unchanged over time. (Wikipedia: http://en.wikipedia.org/wiki/Mission_statement)
A Brand Promise is what you promise people will
receive when they do business with you. It is based
on what truly differentiates your company from
others.
• It must convey a compelling benefit • It must be authentic & credible • It must be kept, every time
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Brand Promises - Quick Examples
• FedEx - Your package will get there overnight. Guaranteed.
• Apple - You can own the coolest, easiest-to-use cutting-edge computers and electronics
• McKinsey & Company - You can hire the best minds in management consulting
• The Nature Conservancy - Empowering you to save the wilderness
• Data Blueprint – Tailored Solutions, Strengthening Capabilities and Lasting Relationships
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Porter’s Market Positioning Framework
Cost: Are you competing on cost? How cost-sensitive is your market?
Market Scope: Are you focused on a narrow market (i.e. niche) or a broad market of customers?
Overall Low-Cost Leadership
Strategy
Broad Differentiation
Strategy
Focused Low-Cost Strategy
Focused Differentiation
Strategy
Blue Ocean Brands
Lower Cost Differentiation
Broad Range of Buyers
Narrow Buyer
Segment
Product Differentiation: How specifically focused are your products?
Note: (Typically) Can’t be all things to all consumers – where are you?
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Market Positioning Example
Overall Low-Cost Leadership
Strategy
Broad Differentiation
Strategy
Focused Low-Cost Strategy
Focused Differentiation
Strategy
Blue Ocean Brands
Lower Cost Differentiation
Broad Range of Buyers
Narrow Buyer
Segment
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Porter’s Competitive Advantage Framework
Given Market Positioning, how does your organization further compete?
Bargaining Power of Buyers: The degree of leverage customers have over your company
Bargaining Power of Suppliers: The degree of leverage suppliers have over your company
Threat of New Entrants: How hard is it for new competition to enter the market?
Threat of Substitute Products: How easy (or hard) is it for customers to switch to alternative products?
Competitive Rivalry: How competitive is the market place?
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Case Study: Operating Model
Business Process StandardizationLow High
Hig
hLo
wB
usin
ess
Pro
cess
Inte
grat
ion
*Source: Gartner
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Business Goals & Objectives
• Definitions vary, overlap and fail to achieve clarity • The most common of these concepts are specific of
intended future results • Most models refer to as either goals or objectives
(sometimes interchangeably)
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Business Goals – Quick Examples
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Case Study: Logistic Company
• Fortune 450 • 4 Divisions
– Truck Load (OTR) – Intermodal – Outsourcing Service – Broker Services
• Significant Growth over the last 10 years • Enterprise-wide modernization program • Recognized need to be data-driven to compete
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Case Study: Mission & Brand Promises
Mission: “We compete with other transportation service companies primarily in terms of price, on-time pickup and delivery service, availability and type of equipment capacity,
and availability of carriers for logistics services.”
Reach $10 Billion in revenue by the year 2020
Brand Promises
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Case Study: Market Positioning
Lower Cost Differentiation
Broad Range of Buyers
Narrow Buyer
Segment
Overall Market Positioning
Low Cost; Quality Service; Availability and
Differentiated Equipment & Service
Brokered Services Truck LoadIntermodal Outsourced Services
Blue Ocean Brand – able to compete across multiple market positions
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Case Study: Competitive Advantage
• Buyer Power is moderate to weak – 4 divisions at multiple price points (“Full Service”) – High switching costs for some customers
• Threat of Entrant is weak – High capital requirements – Strong brand recognition
• Supplier Power is moderate to strong – Limited # of drivers; Very Poor Retention Rates – Limited railroad capacity (Intermodal)
• Threat of Substitutes is weak – Railroads are a strong substitute; they lead in Intermodal
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Operating Model Framework
Business Process StandardizationLow High
Hig
hLo
wB
usin
ess
Pro
cess
Inte
grat
ion
*Source: Gartner
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Case Study: Business Goals & Objectives
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Case Study: KPI’s
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
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Measuring Business Value• Define success criteria as specific metrics • Not always intuitive and at first seems difficult • Must be done in collaboration with your business
partners • If something is important to the business it can be
observed. If it can be observed, it is measureable! • Understanding ‘measurement’; reducing uncertainty, not
necessarily an exact value • Object of Measurement; often too ambiguously defined • Methods of Measurement; become familiar with multiple
methods and apply in the right context
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Great point of initial inspiration ...• Formalizing stuff forces
clarity • Special shout out to
Chapter 7 – Measuring the value of
information – ISBN: 0470539399 – http://www.amazon.com/
How-Measure-Anything-Intangibles-Business
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The Correct Concept of Measurement
• As far as the propositions of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality – Albert Einstein
• Measurement: A quantitatively expressed reduction of uncertainty based on one or more observations – Not elimination of uncertainty
• This means: – Measurements do not need to be precise – Measurement is information [information theory]
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Defining the Object of Measurement
• A problem well stated is a problem half solved – Charles Kittering
• What do you mean exactly (mentorship)? • Clarification Chain
1. If it matters at all, it is detectable/observable 2. If it is detectable, it can be detected as an amount (or range of
possible amounts) 3. If it can be detected as a range of possible amounts, it can be
measured • For example:
– Measure the value of crime reduction – Build me a business case for a specific biometric identification
systems for criminals
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Methods of Measurement
• Very small random samples – Useful in the face of great uncertainty
• Populations you will never see all of: – Number of attempts that go undetected
• Risk of rare events – Decision makers can be informed through observation
and reason • Subjective preferences and values
– The value of art, free time, risk reduction
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Enrico Fermi (Nobel Prize Physics 1938)
• How many piano tuners in the city of Chicago? – Count them all (yellow pages, licensing agency) – Current population of Chicago (3 million at the time) – Average number of people per household (2 or 3) – Share of households with regularly tuned pianos (1 in 3) – Required frequency of tuning (1/year) – How many pianos can a tuner tune daily? (4 or 5) – How many days/year are worked (250)
• Tuners in Chicago = Population/people per household X % households with tuned pianos X tunings per year/ (tunings per tuner per day X workdays/year)
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Example: Measuring Business Value-1
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• $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!
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Example: Objects of Measurement & Metrics-2
• Test Execution: Number of tests per customer product formulation. Grouped by product types and product complexity.
• Customer Satisfaction: Amount of time to develop a certified custom formulated product; time from initial request to certification
• Researcher Productivity: Tested and certified formulations per researcher
Note: Baseline measures were taken from historical data and anecdotal information
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Overview of Existing Data Management Process
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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
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Solution and Business Value Results• Solution:
– Business process improvements – Data Architecture Development – Data Quality Improvements – Integrated System Development
• Results: – Reduced the number of tests needed to develop products – Increase the number of tests per researcher – Reduce the time to market for new product development
• According to our client’s internal business case development, they expect to realize a $25 million gain each year thanks to this data integration
Copyright 2014 by Data Blueprint
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Summary – Measuring Business Value• If it’s important to the business, it’s measureable • Learning to measure business value requires:
– Understanding fundamentally what it means to ‘measure’ – Being clear about what is going to be the object of
measurement and the specific metrics – Methods that will ensure the metrics captured are meaningful
and consistent • The old adage – “if you don’t measure it, it can’t be managed” is
true
Next Step: • Develop a holistic solution and approach to address the business
needs identified in the data strategy
Copyright 2014 by Data Blueprint
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
Copyright 2014 by Data Blueprint
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Analyzing the Current State
• 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 ImperativesBusiness Needs
Existing Capabilities
ExecutionBusiness Value
New Capabilities
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Analyzing the Current State
Why we are analyzing the current state… •Identify existing assets & capabilities
•Identify gaps in assets & capabilities
•Identify constraints & interdependencies
•Measure Cultural Readiness
•Measure what is achievable
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Analyzing the Current State (ACS)-2
People & Organization
Data AssetsTechnology Assets
Data Mgmt. Practices
Business Processes
Business Goals and Objectives
Creates
Enables
Informs
Enables
Enables
Measures
Delivers
Enables
Enables
Provides Context
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Organizational Structures • Understand roles, responsibilities, authority & accountability
– Reporting Structures – Governance Structures – Matrix (e.g. Project) Structures
• Assess Skills Across Business, Data & Technology – Foundational Data skills (CDMP) – Subject matter expertise (SME) – Technology skills – Business process skills (Six Sigma) – Change management skills
Current State: Organization
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Current State: Cultural Readiness
Culture is the biggest impediment to a shift in organizational thinking about data
The Managing Complex Change model was copyrighted by Dr. Mary Lippitt, 1987.
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Current State: Business ProcessWhat we are looking for… • Process flow diagrams • Process actors, including data creators & consumers • Pain points • Existing performance measures
Why we want to look at business processes… • Where business value is realized • Most important events in the life of data (Dr. Tom Redman) • Describes the activities underpinning the competitive advantage
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Current State: Business Process
A CRUD Matrix captures current state processes and their impact on data. Specifically, data creation and consumption.
Data Creation
rqmtsrqmts
feedbackfeedback
input output
Support Tech
Data Supplier
Data Customer
How well this process is known & managed tells “everything”
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Current State: Data Management Practices
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• Published by DAMA International – The professional
association for Data Managers (40 chapters worldwide)
• DM BoK organized around – Primary data management
functions focused around data delivery to the organization
Why we want to look at Data Management Practices… • Where the data management practices are deficient, surely the data will be as well
Typical Thinking: Application-Centric Development
Original articulation from Doug Bagley @ Walmart
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• In support of strategy, organizations develop specific goals/objectives
• The goals/objectives drive the development of specific systems/applications
• Development of systems/applications leads to network/infrastructure requirements
• Data/information are typically considered after the systems/applications and network/infrastructure have been articulated
• Problems with this approach: – Ensures data is formed to the applications and
not around the organizational-wide information requirements
– Process are narrowly formed around applications – Very little data reuse is possible
Data/Information
Network/Infrastructure
Systems/Applications
Goals/Objectives
Strategy
New Thinking: Data-Centric Development
Original articulation from Doug Bagley @ Walmart
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Copyright 2015 by Data Blueprint
• In support of strategy, the organization develops specific goals/objectives
• The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage
• Network/infrastructure components are developed to support organization-wide use of data
• Development of systems/applications is derived from the data/network architecture
• Advantages of this approach: – Data/information assets are developed from an
organization-wide perspective – Systems support organizational data needs and
compliment organizational process flows – Maximum data/information reuse
Systems/Applications
Network/Infrastructure
Data/Information
Goals/Objectives
Strategy
Top Operations
Job
Top Data Job
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Top Job
Top IT
Job
Top Marketing
Job
Data Governance Organization
Top Data Job
• Dedicated solely to data asset leveraging • Unconstrained by an IT project mindset • Reporting to the business • There is enough work to justify the function
and not much talent • The CDO provides significant input to the
Top Information Technology Job
• 25 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/newsroom/ id/2659215?
• By 2020, 60% of CIOs in global organizations will be supplanted by the Chief Digital Officer (CDO) for the delivery of IT-enabled products and digital services (IDC)
The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset
Peter Aiken andMichael Gorman
Top Finance
Job
Maintain fit-for-purpose data, efficiently and effectively
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Manage data coherently
Manage data assets professionally
Data architecture implementation
Data lifecycle implementation
Organizational support
ACS DM Practice Areas
One concept for process improvement, others include:
• Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000 and focus on understanding current processes and determining where to make improvements.
Copyright 2013 by Data Blueprint
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts
Performed (1)
Managed (2)
Our DM practices are defined and documented processes performed at
the business unit level
Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices
Defined (3)
Measured (4)
We manage our data as a asset using advantageous data governance practices/structures
Optimized
(5)DM is strategic organizational capability, most importantly we have a process for
improving our DM capabilities
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Assessment Components
Data Management Practice Areas
Data Management Strategy
DM is practiced as a coherent and coordinated set of activities
Data Quality
Delivery of data is support of organizational objectives – the currency of DM
Data Governance
Designating specific individuals caretakers for certain data
Data Platform/Architecture
Efficient delivery of data via appropriate channels
Data Operations Ensuring reliable access to data
Capability Maturity Model Levels
Examples of practice maturity
1 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts
2 – ManagedWe have DM experience and have the ability to implement disciplined processes
3 – Defined
We have standardized DM practices so that all in the organization can perform it with uniform quality
4 – MeasuredWe manage our DM processes so that the whole organization can follow our standard DM guidance
5 – Optimized We have a process for improving our DM capabilities
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Maslow's Hierarchiy of Needs
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You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present
greaterrisk(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
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Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
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Avoid One Legged Stools – over relying on technology
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Governance is the major means of preventing over reliance on one legged stools!
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Current State: Data AssetsWhat we are looking for…. • Inventory of assets • Shadow data solutions • Organization of data assets (Architecture) • Specific pain points • Information capabilities (through a business lens) • Methods for data integration • Controls for data sharing
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Current State: Data Assets• Eating the data inventory “Elephant”
– Id what’s important – De-prioritize the Data ROT (Redundant, Obsolete, Trivial) – Organize thinking into data ‘roles’
Data Inventory
Transactional
Master Data
Metadata Temporal
Unstructured
Reporting
MessagingEvent DataSensor Data
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Business Entity Inventory Example
Customer Request
Front Office Transactional Business Entities
Back Office Transactional Business Entities
Transactional Data
Order
Order Plan
Load
Capacity
Load Plan
DispatchWarehouse Inv.
Claim Invoice
Credit Equip. Maint.
GL Payroll
Metadata
Provides a broad view of the data
assets
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Current State: Data Asset Example
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ACS: Data Assets – Data Quality Considerations
Prevention at Source
Find and Fix
Ad-Hoc Processes
An interpretation from Dr. Tom Redman’s ‘Three Approaches to Data Quality’
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Current State: Technology Assets & PracticesTechnology Assets… •Systems & System Flows (Architecture) •Shadow Systems •Technologies, Platforms, Language Standards •What’s Legacy, what’s permanent ‘temporary’, what’s new •Traceability to data and business processes
Technology Management Practices… •System Development Lifecycle •Governance & Production Support Practices •Project and Program Management Practices
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Current State: System Flow ExampleA view of systems mapped to functions and data
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
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Strategic Data Imperatives
• 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 ImperativesBusiness Needs
Existing Capabilities
ExecutionBusiness Value
New Capabilities
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Strategic Data Imperative Framework
Business Needs Current State
• Id Business Value Opportunities • Define Value Targets for Each
Data Value Imperatives
• Data Mgmt. Practices • Organizational & Leadership • Data Assets
Data Mgmt. Needs
• Net-Net DM Needs • Define Capability Targets for Each
DM Imperatives
• Data Mgmt. Program Requirements • Roadmap Project Requirements
Tactics
STRATEGIC DATA IMPERATIVES
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Finding Data Value Opportunities
• Transparency • Inefficiencies
– Checking & fixing – Finding & Accessing – Sharing & Controlling
• Proactive Workflows & Decision Making • Measuring Outcomes & Performance • Optimizing Asset Utilization • Predictive and ‘what-if’ Planning
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Define Data Value Imperatives & TargetsTransparency
• Transparency and control across the lifecycle of an order
• Amount of time to find and access the complete history of an order • Difference between the amount of time being reactive vs. proactive in a crisis
Efficiency• Maximize straight-through-processing from order capture thru dispatch
• # of order processed per account rep • # of auto-dispatched loads
Optimized Asset Utilization• Optimize equipment capacity across divisions
• Revenue per truck per day • # of errors for truck dispatched ETA data
Proactive Workflow • Improve customer experience
• % of on-time deliveries • # of customer self-monitored orders
KEY
IMPERATIVE
VALUE TARGET
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Data Management Capability Needs
• Function of Value Imperatives & Targets • At the core – Architecture, Quality & Leadership • Dimensions of Foundational & Technical Capabilities • Think about DM needs broadly…follows current state
assessment framework
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Capability Needs: Data Management Practices
• Foundational Data Management Practices create infrastructure that enables long-term DM capabilities
• Technology Data Management Practices deliver focused solutions in direct support of tactics
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Foundational Practice Capabilities• Governance: Little ‘g’ approach - where it matters the
most. • Data Strategy: Top-down approach. Cannot dabble, must
commit! • Data Architecture: Organizing data assets based on
business needs, not systems or applications. • Data Education: Changing organizational thinking about data.
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Technical Practice Capabilities
• Data Quality: – Focus on most important data – Address root cause issues – Data correct first time
• Data Integration: – Support multiple data uses – Requires a common language and semantic understanding
• Data Platforms: – Engineering/architectural & holistic systems thinking – Decouple functionality – No one data platform can do it all
• Business Intelligence: – Highly dependent on quality, metadata & integration – Exploratory in nature – Small ‘failures’ and on-going learning – Often exists in spread-marts and shadow IT solutions
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A Practice not a Project
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• Requires new organizational structures
• Changes in existing roles and responsibilities
• Continuous practice improvement • Constant investment • KPI’s • Enabled through technology
Project
Practice
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Data Mgmt. Capability Needs (1)
Transparency• Transparency and control across the lifecycle of an order
• New Data Assets: Event Data to describe lifecycle of an order • Enterprise Data Architecture: Defining & relating transactions and events • Data Quality: Quality controlled Transaction ids to maintain linkage across functions • Data Integration: ‘Order’ semantically defined across functions • Business Process Engineering: Redesign processes to leverage single view of an order • Organizational Roles: Business ownership of event data
• Amount of time to find and access the complete history of an order • Difference between the amount of time being reactive vs. proactive in a crisis
Data Value Imperatives
Data Mgmt. Needs
DM Imperatives
Tactics
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Data Mgmt. Capability Needs (2)
Efficiency• Maximize straight-through-processing (STP) from order capture thru dispatch
• # of order processed per account rep • # of auto-dispatched loads
• Master Data Mgmt.: Master data quality greatly reduces processing errors • Data Governance: Data standards & metadata enables automated workflows • Enterprise Data Architecture: Globally organized data only way to control data for STP • Data Quality: Enforce data ‘correct the first time’ at point of data entry • Business Process Engineering: Design exception-based workflows • Organizational Roles: Business ownership of exception workflows; Governance roles
Data Value Imperatives
Data Mgmt. Needs
DM Imperatives
Tactics
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Vision of the FutureA Vision that enables efficiency, transparency, control, stability and integration across the enterprise …. while also allowing the flexibility of each division to meet their own, specific requirements
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Detailed Vision
• Efficiency
• Transparency
• Control
• Stability
• Integration
• Across the enterprise
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Capability Imperative: People & Organization
Leadership• Establish clear and explicit leadership role for Data Mgmt.
• Given Authority, Responsibility and Accountability to meet demands • Given budget to match demands
Roles & Responsibilities• Define new and enhance existing roles and responsibilities
• Establish support organization for Data Mgmt. Leadership • Enhance existing roles across business, IT and Data teams to meet new demands
Skills & Experience• Acquire new and further develop existing skills
• Data training provided across business, IT & data teams • Hire and/or rent talent
KEY
CAPABILITY IMPERATIVE
CAPABILITY TARGET
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Capability Imperative: Data Mgmt. Practices
Data Quality• Establish repeatable data quality processes that deal with the root cause issues
• Id most important data • Define and standardize repeatable DQ process • Train cross functional teams on process • Set improvement targets and monitored progress
Data Architecture• Organize views of the data assets to convey meaning for multiple business and IT purposes
• Business level view provides awareness, participation & responsibility with business roles • Conceptual and logical views enable business, data & IT teams to effectively communicate • Data Security can only be effective with a controlled inventory of data assets • An operating model for creating and maintaining data architecture
KEYCAPABILITY IMPERATIVE
CAPABILITY TARGET
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Tactics – Preparing for the Roadmap
Increase Operational Efficiencies
As-
IsTo
-Be
As-Is Efficiency Challenges • Complex & un-integrated processes • Poor data quality requires constant manual intervention • Lack of transparency and controls creates work-around’s
To-Be Efficiency Tactics • Eliminate non-value added manual work-around’s • Develop straight-through-processing where possible • Automate exception-based workflows • Create transparency across the order lifecycle • Develop repeatable data quality processes
Create efficiencies in the order lifecycle will… • Lower cost per order by 15% • Increase resource capacity by 20%
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Improve the Customer Experience
To-Be Customer Experience Tactics •Automate exception identification & resolution •Predictively find on-time delivery issues •Provide customers cross-division service options •Provide customers real-time views of orders •Develop master data mgmt. solution for customer data
As-Is Customer Experience Challenges • Manual monitoring of orders needing attention • Reactive to customer status inquiries • Reactive to unexpected order booking issues
Improving customer experience will… • Maintain >98% on-time delivery services • Increase revenue per customer by 7%
As-
IsTo
-Be
Tactics – Preparing for the Roadmap
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Outline• Data Strategy Overview • Determining the Business Needs • Target Measurement & Success Criteria • Current State Analysis • Developing the Strategic Data Imperatives
– Business Value Targets – Data Management Capabilities – Tactics/Vision
• Developing a Roadmap • Q&A
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Analyzing the Current State
• 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 ImperativesBusiness Needs
Existing Capabilities
ExecutionBusiness Value
New Capabilities
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Roadmap FrameworkY1 Y2 Y3 Y4
• Data Strategy Leadership • Planning & Business Strategy Alignment • Program Management
• Tie Projects to Outcome-Based Targets • Business Case and Project Scope • Project Management and Execution • Measure Outcomes
• Create Leading Coalition • Communicate the Vision • Leverage Short-Term Wins • Institutionalize Data-driven Behaviors
“Fit for Purpose”
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Leadership & PlanningY1 Y2 Y3 Y4
• Data Strategy Leadership • Planning & Business Strategy Alignment • Program Management
• On-going and iterative activities • Responsible for other two streams • Data Strategy Execution accountability
and leadership (CDO) • Adjust strategic imperatives/tactics
based on changing business needs • Manage relationships with business
leaders and data strategy program stakeholders
Don’t Over-engineer the Process
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Leadership & PlanningY1 Y2 Y3 Y4
Clearly Defined Imperatives, Tactics & KPI’sOn-going Planning & Adjustments
Establish Leadership Organization and Processes
On-going Sponsor Engagement
Establish PMO Practices & Processes
Manage Project Portfolio
Budget Cycles
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Project Development & Execution
Y1 Y2 Y3 Y4
• Tie Projects to Outcome-Based Targets • Business Case and Project Scope • Project Management and Execution • Measure Outcomes
• Where “the rubber hits the road”
• Incremental Business Value
• Strengthen Capabilities
• Iterative and Additive
• Beyond Technology
• Hand-in-Hand with Cultural Readiness
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Roadmap Operating Model
Leadership & Planning
Execution Leadership
Planning
Program Mgmt.
Project Development & Execution
Define Milestones
Define Projects
Execute Projects
Imperative, Tactic & KPI Targets
Budgets & Resources
Recommended Projects
Approved Recommended Projects
Project Status & Outcome Measures
Project Oversight & Support
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Project Development & Execution
• Project Development – Initialize High-level Milestone Targets (value & capability) – Define the Initial Set of Projects (6 to 18 months out) – Process for Defining Projects (business case & scope)
• Project Execution – Define the Project Lifecycle by Project ‘Type’ – Focus on Execution – Measuring Outcomes
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Project Development
• Initialize Long-term Milestones
• Tie to Strategic Imperatives & Tactics
• Initialize Projects to Execute
• Establish On-going Project Definition Process
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Project Development: Initial RoadmapY1 Y2 Y3 Y4
Imperatives, Tactics & KPI’s (Value targets)
Strengthen Data Mgmt. Capabilities (Cap. targets)
Establish Project Definition Process
Short-Term Wins
Leverage Momentum & Strengthened Capabilities
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Initializing Milestones: Logistics ExampleY1 Y2 Y3 Y4
Lower Operational Costs per Order
Increase Revenue per Customer
Improve Service Quality
Streamline Order Capture
Proactive Exception Mgmt.
Enterprise View of Customers Cross-Divisional Selling
Proactive Exception Mgmt. 360º View of OrdersOptimized Routing & Equipment Utilization
Data Quality
Data Architecture
Data Analytics
Master Data Mgmt. First-Time Correct Policy
Business Entities Conceptual (Enterprise) Logical (by subject)
Equipment Tagging GIS and Telemetry Data
KEYValue Targets Capability Targets
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Initialize Projects
• Repeatable Process for Defining Projects (Initial & On-going) • Project Definition Process Inputs
– Milestone Targets (Value and Capability) – Cultural Readiness Goals – Existing Capabilities (People, Process, Data, Technology and Readiness
for Change) – Outcomes from Previous Projects
Y1 Y2 Y3 Y4
Establish Project Definition Process
Short-Term Wins
Leverage Momentum & Strengthened Capabilities
Start Here!
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Project Definition Process
Value Targets
Capability Targets
Readiness Goals
Existing Capabilities
Project Outcomes
Achievability
Identify Candidate Projects
Develop Business
Case
Recommend Projects
Define & Sequence Projects
• Measure • Analyze
• Priority • Justification • Agreement
• Scope • Resources • Expected
Value
• Use to define initial road map projects • Use iteratively for on-going project definition • Leverage PMO and Program sponsorship • Collaborate closely with Cultural Readiness teams
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Achievability
Level of Control (Influence)
Business Impact
“Over-promise & under-deliver”
“In the Tank”
“Cash Cow”
“Small Wins”
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The Approach of Crawl, Walk, Run
• Crawl: – Identify business opportunity and determine a scope that fosters early
learning yet delivers measureable value
• Walk: – Develop foundational &
technical data management practices ensuring they are repeatable. Enlarge the scope of projects that expand capabilities
• Run: – Continuous improvement and expanded application of maturing data
management practices
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Initializing Projects: Logistics Example (1)Y1 Y2 Y3 Y4
Improve auto-accept rates [Data Quality, Reduce Cost per Order]
Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue]
Reduce cycle time to id errors [Data Quality, Reduce Cost per Order]
Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order]
Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer]
Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order]
Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
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Initializing Projects: Logistics Example (2)Y1 Y2 Y3 Y4
Improve auto-accept rates [Data Quality, Reduce Cost per Order]
Reduce cycle time to id errors [Data Quality, Reduce Cost per Order]
Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue]
Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order]
Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer]
Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order]
Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
• Short-term Wins • Builds momentum for Data Strategy • Reduces non-value added work • Creates repeatable data quality processes • Coordinated Closely with Cultural Readiness Team
CRAWL
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Initializing Projects: Logistics Example (3)Y1 Y2 Y3 Y4
Improve auto-accept rates [Data Quality, Reduce Cost per Order]
Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue]
Reduce cycle time to id errors [Data Quality, Improve STP]
Increase # of Orders per Acct Rep [Data Quality, Architecture, Reduce Cost per Order]
Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer]
Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order]
Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
• Extended Short-term Wins • Order capture data shared to enhance cross divisional
selling • Automate order exception id & resolution • Coordinated Closely with Cultural Readiness Teams
WALK
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Initializing Projects: Logistics Example (4)Y1 Y2 Y3 Y4
Improve auto-accept rates [Data Quality, Reduce Cost per Order]
Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue]
Reduce cycle time to id errors [Data Quality, Improve STP]
Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order]
Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer]
Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order]
Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
• Foundational Data Management Projects • Ties directly to multiple Value Imperatives • Addresses multiple data management foundational capabilities –
quality, architecture, master data, analytics, .. • Typically would fall under CDO or Data Management Org.
JOG
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Initializing Projects: Logistics Example (5)Y1 Y2 Y3 Y4
Improve auto-accept rates [Data Quality, Reduce Cost per Order]
Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue]
Reduce cycle time to id errors [Data Quality, Improve STP]
Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order]
Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer]
Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order]
Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality]
• Enterprise, transformational initiatives • Ties directly to multiple Strategic Imperatives • Leverage foundational data mgmt. capabilities – quality,
architecture, master data, analytics, .. • Typically would fall under CIO or Business Executive
RUN
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Linking Projects to MilestonesY1 Y2 Y3 Y4
Lower Operational Costs per OrderStreamline Order Capture
Data Quality
Data Architecture
Master Data Mgmt.
First-Time Correct Policy
Business Entities
Conceptual (Enterprise)
Logical (by subject)
KEYValue Targets Capability Targets
Improve auto-accept rates
Reduce cycle time to id errors
Re-engineer Customer Master Data
Increase straight thru processing
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Summary: Project Development & Execution
• Projects must balance capability and business value creation
• Mix of projects: short-term wins, foundational data management projects, large enterprise initiatives
• Projects must directly-tie and measurably-support strategic imperatives and tactics
• Take a crawl, walk, run approach to project execution
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Cultural ReadinessY1 Y2 Y3 Y4
• Create Leading Coalition • Establish Goals and Communication Plan • Execute Communicate Plan • Institutionalize Data-driven Behaviors
• Level of effort estimated 5% - 10% of total program in the first year
• Cultural change needs often neglected and under-estimated
• Leadership, skills and activities needed are typically missing
• Tie to strategic imperatives and projects; cannot be executed in a vacuum
• “Data-driven” organizations must recognize the need for transformation in attitudes, behaviors, processes, skills and organizational structures
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Cultural Readiness RoadmapY1 Y2 Y3 Y4
Create Leading CoalitionId Data Strategy Ambassadors
Establish Cultural Readiness Goals & Communication Plan
Execute Comm. Plan •Vision •Recognizing Wins
Institutionalize Behaviors
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Cultural Readiness In More Detail (1)
"The thing I have learned at IBM is that culture is everything." - Louis V. Gerstner, Jr., Former CEO of IBM
• Leading Coalition that can make change happen – Find the right people – Create trust – Common vision
• Establish Goals & Communication Plan – Simplified Goals; Appeal to the Head and the Heart – Communicate, Communicate, Communicate!
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Cultural Readiness In More Detail (2)
• Execute Communication Plan – Multiple Forums – Repetition – Leadership by Example
• Institutionalize Data-driven Behaviors – Change comes last, not first – Results Dependent – May involve turnover
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10 Common Mistakes (1)1. Buy-in but not Committing
• Responsibility, Accountability but NO Authority
2. Ready, Fire, Aim • Starts without sufficiently defining the business needs
3. Trying to Solve World Hunger or Boil the Ocean • “Too big too fast” = Recipe for disaster
4. The Goldilocks Syndrome • Approach is at one extreme or another; too high-level or too in the
weeds
5. Committee Overload • Avoid too many chefs in the kitchen
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3/10/15
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
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10 Common Mistakes (2)6. Failure to Implement
• Communicate the vision
7. Not Dealing with Change Management • Its mostly a people and culture issue
8. Assuming that Technology Alone is the Answer • Shiny object syndrome
9. Not Building Sustainable and Ongoing Processes • DG is not a project!
10. Ignoring “Data Shadow Systems” • Missing the best part
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
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Conclusion
In Summary….
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Data Strategy Framework
• 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 ImperativesBusiness Needs
Existing Capabilities
ExecutionBusiness Value
New Capabilities
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Analyzing the Business
Business Goals & Objectives
Operating Model
Competitive Advantage
Market Positioning
Mission & BrandWhy a Company Exists
What a Company Produces & Sells
How a Company Does It
Business Needs
• Business Value Targets • Capability Targets • Tactics • Data Strategy Vision
Strategic Data Imperatives
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Data Strategy Solution Framework (DSSF)
People & Organization
Data AssetsTechnology Assets
Data Mgmt. Practices
Business Processes
Business Goals and Objectives
Enables
Enables
Informs
Creates
Enables
Measures
Delivers
Enables
Enables
Provides Context
The solution architecture and change management plans result from this framework
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Strategic Data Imperative Framework
Business Needs Current State
• Id Business Value Opportunities • Define Value Targets for Each
Data Value Imperatives
• Data Mgmt. Practices • Organizational & Leadership • Data Assets
Data Mgmt. Needs
• Net-Net DM Needs • Define Capability Targets for Each
DM Imperatives
• Data Mgmt. Program Requirements • Roadmap Project Requirements
Tactics
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Roadmap FrameworkY1 Y2 Y3 Y4
• Program (Portfolio) Management • Business Strategy Alignment • Sponsorship Relations Management
• Tie Projects to Outcome-Based Targets • Business Case and Project Scope • Project Management and Execution • Measure Outcomes
• Create Leading Coalition • Establish Goals and Communication Plan • Execute Communicate Plan • Institutionalize Data-driven Behaviors
“Fit for Purpose”
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Sessions: •Data Strategy 2.0: Focus on the Roadmap and Implementation •3 hour workshop with Lewis Broome
•Addressing Data Challenges using the Data Management Maturity Model •Melanie A. Mecca, CMMI Institute Peter Aiken, Data Blueprint
• 120+ thought leaders
• 800 attending Senior IT Managers, Architects, Analysts, Architects & Business Executives
• 5 full days of in-depth education and networking opportunities
• … and more!!! • Register here:
www.edw2015.dataversity.net
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Upcoming Events
Enterprise Data World, Washington D.C. March 29 – April 3, 2015 @ 2:00 PM ET/11:00 AM PT
Data Governance Strategies April 14, 2015 @ 2:00 PM ET/11:00 AM PT
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