Data-Ed: Data-centric Strategy & Roadmap

Data-centric Strategy & Roadmap

Date:

February 11, 2014

Time:

2:00 PM ET
11:00 AM PT

Presenters: Peter Aiken,
Lewis Broome

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Copyright 2014 by Data Blueprint

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2

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Data Management & Business Intelligence

3

Building a Data-centric Strategy &
Roadmap
What needs to be done… avoiding a haphazard
approach
Presented by Peter Aiken, Ph.D. and Lewis Broome

Lewis Broome
•  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

Peter Aiken
•  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
5

Building a Data-centric Strategy &
Roadmap
What needs to be done … avoiding a haphazard
approach

Presented by Peter Aiken, Ph.D. and Lewis Broome


Outline
•  Data Strategy Overview
•  Determining the Business Needs
–  Foundational Business Understanding
–  Identify Specific Business Needs
–  An Example

•  Measurement & Success Criteria
–  An Overview
–  An Example

•  Developing a Solution to Address Needs
–  Closing Foundational Gaps
–  Solving for Specific Needs

•  Developing a Roadmap and Plan
•  Q&A
7

Simon Sinek: How great leaders inspire action

WHY
HOW

“…it’s not what you do,
it’s why you do it”

WHAT

http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
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Summary: Enterprise Data Strategy Choices
Q4

Using data to create
strategic opportunities

Innovation

Q3

Both (Cash Cow)

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

Q1

Q2

Keeping the doors open
(little or no proactive data
management)

Increasing organizational
efficiencies/effectiveness

Improve Operations
9

‘Why’ a Data Strategy?
Data becoming inextricably linked to, and part of, the actual products &
services being sold
Customers see enhanced value in having relevant, accurate & meaningful information
combined with the products and services they purchase

Information is power in a competitive market place
Situational awareness (e.g. a 360º view) of your customers, suppliers, competition & operating
environment creates a competitive advantage that enables you to plan and react

Volume and velocity of data impacting operating models
Organizations are being put at greater operating and reputational risk because legacy business
processes and systems are straining under the requirements to process and understand everincreasing volumes and speed of data
Read more at my blog: http://www.datablueprint.com/winning-todays-information-economy-data-centric-business-strategy/

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Putting the Data Strategy Together
Get on the same
page with
business partners

Measure
Business Value

Develop a holistic
solution and
approach

Get a true understanding of your organization’s
competitive advantage and current business goals
Working with business leaders, managers and
operators, define specific opportunities to meet
the organizational goals
Collaborating with your business partners, define
the metrics that measure levels of success
Develop a comprehensive solution using people,
process, data and technology
Outline an achievable implementation plan in a
roadmap with timelines, milestones and level of
effort estimates

Note: For many organizations this requires a transformation in how they think and
operate – this is the greatest challenge in becoming a ‘data-driven’ organization
11

Outline
–  An Example

–  An Overview
–  An Example

–  Solving for Specific Business Needs

•  Q&A
12

Understanding Your Company’s Competitive
Advantage
•  Do you really know why your company has an
advantage over the competition?
–  You may be surprised!
–  Its not about being the best, its about being different
(counter intuitive)
–  Its about deciding between a set of trade-offs
–  Data strategy must align

•  Frameworks for understanding competitive
advantage
– 
– 
– 
– 
– 

Porter’s Five Forces
Porter’s Competitive Strategic Matrix
SWOT Analysis
PEST Analysis
Four Corners Analysis

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Porter’s Competitive Strategic Matrix
Product Differentiation: How specifically focused are your
products?
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?

Lower Cost

Differentiation

Broad
Broad Overall Low-Cost
Leadership
Differentiation
Range of
Strategy
Strategy
Buyers
Blue Ocean
Brands
Narrow
Buyer
Segment

Focused
Low-Cost
Strategy

Focused
Differentiation
Strategy

Note: (Typically) Can’t be all things to all consumers –
where are you?
14

Porter’s Competitive Strategic Matrix - Examples
Lower Cost

Differentiation

Broad
Range of
Buyers

Narrow
Buyer
Segment

15

Porter’s Five Forces
Once you find your place in the four quadrants…What is your competitive
advantage?
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?
http://www.strategy-keys.com/michael-porter-five-forces-model.html
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An Example – The Automotive Industry
Once you find your place in the four quadrants….
• 
What is your competitive advantage against those around you?

Lower Cost

Differentiation

Broad
Broad Overall Low-Cost
Leadership
Differentiation
Range of
Strategy
Strategy
Buyers
Blue Ocean
Brands
Narrow
Buyer
Segment

Focused
Low-Cost
Strategy

Focused
Differentiation
Strategy

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Applying the Five Forces
5 Forces

Porsche

Hyundai

Threat of New Entrants

Very Weak

Weak

Bargaining Power of Buyers

Moderate

Very Strong

Bargaining Power of Suppliers

Weak

Very Weak

Threat of Substitutes

Moderate

Strong

Competitive Rivalry

Moderate

Strong

Porsche
•  Customer relationship data is critical. Develop individualized customer interactions
•  High quality & efficient data processing to support R&D to further differentiate products

Hyundai
•  Price-sensitive customers. Use strength over suppliers to maintain low COGS.
•  Reduce non-value added to keep operational costs low by eliminating inefficiencies
created by poor data quality

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Data Value Generation Take-Away

Source: http://www.cioupdate.com/insights/article.php/3936706/The-4-Principles-of-a-Successful-Data-Strategy.htm

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Summary: Same Page with Your Business Partners

A Data Strategy must be Business Focused
•  Understand the business fundamentals of your organization
•  Develop a common language and shared perspective with your
business partners – enabling collaboration
•  Identify specific business opportunities or areas of improvement
•  Focus the data strategy solution on improving those
specific business needs

Next Step:
•  Measuring business value of
making improvements:
•  Metrics, Object of Measurement and Methods
20

One of two choices
•  Good business strategy
–  Understand what it really is:
•  Organizational strategy
•  IT strategy
•  Data strategy

•  Got to figure out/improve the business strategy
–  Analysis
–  What changes would be seen
as useful/important?
–  Plan to accomplishing
something useful …
21

Outline
–  An Example

–  An Overview
–  An Example


•  Q&A
22

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

23

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-AnythingIntangibles-Business
24

Measuring Business Value – An Example
International Chemical Company Engine Testing
•  $1billion (+) chemical company
•  Develops/manufactures additives
enhancing the performance of oils
and fuels ...
•  ... to enhance engine/machine
performance
–  Helps fuels burn cleaner
–  Engines run smoother
–  Machines last longer

•  Tens of thousands of
tests annually ($25K to $250K each)
25

Objects of Measurement & Metrics
•  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

26

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

Overview of Existing Process
27

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

28

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
29

Outline
–  An Example

–  An Overview
–  An Example


•  Q&A
30

The Data Strategy Solution
With an understanding of business needs and measures of
success criteria, align a solution leveraging the following:
• Rethink the SDLC: Application- vs. Data-Centric
• Make it Comprehensive:
–  People: Organizational Structure
–  Data Management Practices: Foundational & Technical
–  Data: Determine What is Important
–  Process: Business Process Changes
–  Technology: Engineering/Architectural Concepts

• Match your organization’s abilities to deliver

31

Typical Thinking: Application-Centric
• 

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

• 

Strategy

Goals/Objectives

Systems/Applications

Problems with this approach:
–  Ensures data is formed to the applications and not
around the organizational-wide information
requirements

Network/Infrastructure

–  Process are narrowly formed around applications
–  Very little data reuse is possible

Data/Information

32

New Thinking: Data-Centric
• 

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

• 

Development of systems/applications is derived from the
data/network architecture

• 

Goals/Objectives

Network/infrastructure components are developed to
support organization-wide use of data

• 

Strategy

Advantages of this approach:
–  Data/information assets are developed from an
organization-wide perspective

Data/Information

Network/Infrastructure

–  Systems support organizational data needs and
compliment organizational process flows
–  Maximum data/information reuse

Systems/Applications

33

People: Who is Involved?
•  Open question: Who is responsible for creating and implementing
the company’s Data Strategy?
- 

Organizational Leadership is required – a Chief Officer that reports up through the business
lines

- 

Data strategy requires governance – Business, IT and Data team representation

•  Stakeholders
- 

CEO, CFO, COO, CIO, etc..

- 

Lines of Business Senior Management and Operational Managers

- 

Functional Areas Senior Management and Team Leads

•  The Data Team – formal and implicit
- 

Architects

- 

Modelers

- 

Developers

- 

Analysts

- 

Stewards

-  CDO
34

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1.  Dedicated solely to
data asset leveraging
2.  Unconstrained by an
IT project mindset
3.  Reporting to the
business
IT
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Top Job

CDO Reporting
Top
Information
Technology
Job
Top
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Job
Chief
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Officer
Top Finance
Job

0.000


Top
Marketing
Job

Data Governance Organization

0.800
0.600
0.400
0.200

2011
2010
2009
2008
2007
2006
2005

35

Data: Determine What is Important
•  Think about it in terms of data ‘meta-types’:
–  Transactional Data
–  Workflow/Event Data
–  Master & Reference Data
–  Reporting & Analytical Data
–  Metadata

•  Not all of your data is important!
•  Concept of ROT
•  Understanding your business and their needs
makes this easier to determine

36

Data Management Practices
•  Foundational Data
Management Practices
create the organizational
infrastructure that enforces the
alignment of company
strategies with data assets
•  Technology Data
Management Practices
enable an organization to
leverage the data on the scale
needed to support informationbased strategies

Important Note: Not all DM
Practices needed all the time.
Tailor to meet the needs of the
business.

37

Foundational Practices
•  3-legged stool
–  Strategy
–  Architecture
–  Governance

•  For example:
–  Warehouses fail
–  Missing governance
–  Quality

38

Health Care Provider
Data Warehouse
The average DW costs $30M
and take 18 months to build!

• 
• 
• 
• 

1.8 million members
1.4 million providers
800,000 providers no key
1 User
"I can take a roomful of MBAs and accomplish this analysis faster!"
39

Foundational Practice: Data Strategy
•  Your data strategy must align
to your organizational
business strategy and
operating model
•  As the market place
becomes more data-driven,
a data-focused business
strategy is an imperative
•  For example, you must have
data strategy before you
have a Big Data strategy
40

Foundational Practice:
Data Architecture
•  Common vocabulary
expressing integrated
requirements ensuring that
data assets are stored, arranged,
managed, and used in systems in support of
organizational strategy [Aiken 2010]
•  Most organizations have data assets that are not
supportive of strategies
•  Big question:
•  How can organizations more effectively use their
information architectures to support strategy
implementation?
41

Foundational Practice:
Data Governance
•  Data governance is the
exercise of authority and
control over the management
of your mission critical data
assets.
•  Governance can seem like an added bureaucratic
layer with little value-add. The little ‘g’ approach develop governance where it matters the most.
•  Focus on organizational roles and responsibilities as
well as organizational change management
initiatives.
42

Technical Practices
•  Think like an engineer
–  Holistic
–  Integrated
–  Driven by Requirements

•  For example:
–  Unwinding Mainframes
–  Analytical Platforms

43

Technical Practices:
Data Quality
•  Quality is driven by fit for
purpose considerations
•  Improved directional accuracy is
the goal
•  Focus on your most important
data assets and ensure our
solutions address the root cause
of any quality issues – so that
your data is correct when it is
first created
•  Experience has shown that
organizations can never get in
front of their data quality issues if
they only use the ‘find-and-fix’
approach

44

Data Integration
•  Data integration requires a
common language and
semantic understanding
•  Needs to support multiple
perspectives on the same
data
•  Creates the broad, 360
degree view – where insight
comes from
•  An area where governance
can enable and sustain
•  A challenge in organizational
thinking
45

Data Platforms
•  Incorporate engineering/
architectural concepts into
holistic systems thinking
•  Decouple functionality. No
one data platform can answer
all questions (commonly
misunderstood & expensive)
•  Engineered components can
only be as strong as their
weakest component

46

Getting Data into the Cloud

Transform

Less
Cleaner
More shareable
... data

47

Business Intelligence
•  Highly dependent on quality,
metadata, governance,
integration and platforms
•  Exploratory in nature. Small
‘failures’ and on-going
learning are part of the
process
•  Often exists in spread-marts
and shadow IT solutions –
difficult to share and have a
common understanding
48

Process: Business Process Impacts
•  The Data Strategy Solution will impact existing business processes
and may create new business processes.
•  Business processes are how the data get Created, Read, Updated
and Deleted (CRUD)
•  A CRUD matrix shows business
processes and their data activity type
•  Leverage business process analysis,
design and development techniques
•  Capture baseline measures against
existing business processes to effectively measure improvements

49

Technology: Making the Right Choices
•  For example: Software selection
•  When it is discovered that the new software doesn't
match existing organizational practices …
1.  Change software
2.  Change your business practices
3.  Some combination of both
4.  Ignore the problem

•  Data strategy would have
revealed the problem in
advance of the selection
50

Match your Abilities to Deliver

Understanding your level of Data Management Practice is critical in developing
achievable solutions
Data management
processes and
infrastructure

Organizational Strategies

Implementation
Guidance

Data Program
Coordination
Goals

Organizational
Data Integration

Combining multiple
assets to produce
extra value

Organizational-entity
subject area data
integration

Integrated
Models
Achieve sharing of data within
a business area

Data
Stewardship

Standard
Data

Application
Models &
Designs

Provide reliable
data access

Direction

Data Support
Operations

Feedback
Leverage data in organizational activities

Data
Development

Business
Data

Data
Asset Use

Business Value

51

52

53

Summary: The Data Strategy Solution
•  Thinking differently about the solution
•  Its Comprehensive: People, Data Management,
Data, Process & Technology
•  Address foundational gaps to sustain solutions
•  Match your organization’s abilities to deliver
Next Step:
•  Outline an achievable implementation plan

54

Outline
–  An Example

–  An Overview
–  An Example


•  Q&A
55

Implementation Plan & Roadmap
•  Outline a long-term vision and implementation milestones
•  Achievable, realistic plans
•  Build momentum with specific, short-term win projects
–  Approach: Crawl, Walk, Run

•  More to come at EDW…

56

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
57

The Benefits of Crawl, Walk, Run
•  ‘Pilot-like’ projects create a unique opportunity for
organizational learning while providing measureable
value
•  Builds support for new approaches to data management
– i.e. supports change management activities
•  More achievable approach to managing data as an asset
•  Allows for foundational components to be developed
while concurrently executing more tactical solutions

58

Sessions:
• Implementing a Data-Centric
Strategy & Roadmap – Focus on
What Really Matters
–  3 hour workshop with Peter &
Lewis

• Choosing the Right Data
Warehouse Modeling Strategy
based on your business needs:
Kimball, Inmon, Data Vault
–  Lighting Talk with Data Blueprint
Team

•  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.edw2014.dataversity.net


Questions?

+

=

It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions now.
60

Upcoming Events

Emerging Trends in Data Jobs
March 13, 2014 @ 2:00 PM ET/11:00 AM PT
Data Quality Engineering
April 11, 2014 @ 2:00 PM ET/11:00 AM PT
Sign up here:
•  www.datablueprint.com/webinar-schedule
•  or www.dataversity.net
61

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Data-Ed: Data-centric Strategy & Roadmap

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