Building a Data-centric Strategy and Roadmap

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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• 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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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


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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


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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


• 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


• 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


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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




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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



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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• 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



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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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)


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


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


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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


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Analyzing the Current State


Road Map



Current State


Strategic Data ImperativesBusiness Needs



New Capabilities


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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”


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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• 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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Copyright

2015by Data Blueprint

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.


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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Copyright


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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Copyright


Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities


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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Road Map



Current State





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 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 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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Road Map



Current State





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


• 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.


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 – 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


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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Reduce cycle time to id errors [Data Quality, Reduce Cost per Order]







• 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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Reduce cycle time to id errors [Data Quality, Improve STP]

Increase # of Orders per Acct Rep [Data Quality, Architecture, Reduce Cost per Order]





• 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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• 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


111










• 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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3/10/15

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


Road Map



Current State





New Capabilities


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Analyzing the Business


Operating Model

Competitive Advantage

Market Positioning

Mission & BrandWhy a Company Exists

What a Company Produces & Sells

How a Company Does It

Business Needs




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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 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


• Create Leading Coalition • Establish Goals and Communication Plan • Execute Communicate Plan • Institutionalize Data-driven Behaviors

“Fit for Purpose”


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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Building a Data-centric Strategy and Roadmap

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