Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.

1. @GregBonnette

2. How to Build a Winning Strategy for Data & Analytics 2
Misalignment on How We Achieve Our Goals
Failed Projects and Unmet Expectations
The Need to Show Real Value
Growing Competition for Resources
Exponential Rate of Technological Change
Shifting Demands of IT

3. How to Build a Winning Strategy for Data & Analytics 3

4. Run the Business
• High Volume
• Lower Value
• High Automation
• Larger Audience
• Low Volume
• Higher Value
• Smaller Audience
• Low Automation
Define Strategy
Decision
Support
DecisionContinuum
Quality
MDM
Completeness
Storage
Security
Accessibility
Understanding
Governance
How to Build a Winning Strategy for Data & Analytics 4
Data
Impact
Analysis
Observe
Orient
Reports & Dashboards
Analytics &
Data Discovery
Performance
Management
Predictive
Models
Acquire
Process
Capture
Models
Integrate
Cleanse &
Transform
Decide
Act
Prescriptive
Models
Process
Augmentation
Process Automation
Adapted from IDC Decision Continuum Model
and OODA Military Decision Loop.

5. How to Build a Winning Strategy for Data & Analytics 5
Assess
Current State
Envision
Future State
Assess Gaps
Evaluate &
Prioritize
Develop
Roadmap
Execute
Monitor, Measure and Refine
1
3 4 5 6
2

6. How to Build a Winning Strategy for Data & Analytics 6
3-5 Year Vision
• Where are we headed?
• What does success look like?
Goals & Targets
• Financial
• Operational
Strategic Growth Initiatives
• Where to Play
• How to Win
Organizational Structure
• Flat vs. Hierarchical
• Centralized vs. Decentralized
Decision Making Culture
• Instinctive vs. Quantitative
• Change Management
Mandate to Experiment
• Willingness
• Readiness (Bi-Modal)

7. How to Build a Winning Strategy for Data & Analytics 7
Deterministic
Top-Down Design
Requirements-Driven
High Certainty of Re-use
Enterprise Distribution
Probabilistic
Bottom-Up Experimentation
Opportunity-Driven
High Uncertainty of Usefulness
Individuals & Small Teams
Mode 1 Mode 2
VS.IT Supported
Centralized
Self-Empowered
Decentralized
Adapted from Gartner 2015

8. How to Build a Winning Strategy for Data & Analytics 8
0
Business ITCollaborative Pilots
Citizen-Developed
Applications
Guided Business
Applications
Data
Processing
Data
Provisioning
Data
Laboratory
Data
Acquisition
Ad-
Hoc
Data
Governance & Standards
Data Science
Hybrid Platform
Data Discovery

9. How to Build a Winning Strategy for Data & Analytics 9
Bimodal
Feedback
Loop
Prototype
• Proof of Value
• Proof of Usefulness
Operationalize
• Governance
• Scalability

10. How to Build a Winning Strategy for Data & Analytics 10
Business
Solution
Success
Looks Like
Consumption
Model
Then Now
Here’s a Data
Warehouse and a BI
Tool
“Providing the ability for
a user to query any field
in our data warehouse.”
Standalone Portals &
Tools
Here’s a Prescriptive
Answer to a Real
Business Problem
“Increasing average
customer lifetime value
by 10% with data-driven
cross-selling”
Embedded Analytics

11. How to Build a Winning Strategy for Data & Analytics 11
Employ Design
Thinking Methods
• Empathize
• Define
• Ideate
• Prototype
• Test
Organize and
Analyze By
• Audience
• Business Value
• Capability Needs
• Methods & Skills
• Data Domain
• Effort or Cost
Enterprise
Architecture
• Capabilities View
Creates Options
vs. Choices
• Map to Right
Tool for Right Job
Creativity and Use Case Synthesis Skills are Critical to Generating Value

12. How to Build a Winning Strategy for Data & Analytics 12
Goals &
Outcomes
• What are the
business goals?
• What is the
opportunity?
• What is the value?
Decisions &
Actions
• What decisions
are made?
• How are they
made?
• How often?
• How many?
• What are the
levers of change?
• What are the
“business
moments”?
Analysis
• Who wants to
know?
• What do we need
to know?
• What do we wish
we could know?
• What methods
and tools can we
avail?
Data &
Information
• What data is
required?
• What data do we
have today?
• What data might
we need to
acquire?
• How much
history?
• Of what quality?

13. How to Build a Winning Strategy for Data & Analytics 13
“Marketers need to know what
the most searched athlete
names are on the website in the
5 days leading up to an event so
they can better understand which
of the athletes are most
influential among different
groups of fans”
Use Case (User Story)Technical Requirement
The report should have the
following fields and criteria:
 [Search Query String]
 Contains [Athlete Name]
 [Date] Between [Event Date]-5
and [Event Date]

14. How to Build a Winning Strategy for Data & Analytics 14
What Happened?
Why Did It Happen?
What Will Happen?
What Should I Do?
Analysis Human Input
Adapted from Gartner 2015

15. How to Build a Winning Strategy for Data & Analytics 15
“Big Data Landscape”
© Turck, Hao, 2016

16. How to Build a Winning Strategy for Data & Analytics 16
Create Options
• Understand Your
Capability Needs
(via Use Cases)
• Invest in
Reconfigurable
Platforms
• Invest in Analytical,
Architecture &
Integration Skills
Run Experiments
• Leverage On-
Demand / Cloud
Services
• Run Outcome /
Value-Based Pilots
• Sponsor New Tech
Investigation

17. How to Build a Winning Strategy for Data & Analytics 17
Is It Revolutionary?
New Markets, New Business Models
Does It Keep the Lights On?
Essential, Non (Direct) Revenue Generating
Does it Make Money?
Directly Impacts Existing Business
No
Yes
Yes
Yes
Transform the Business
Run the Business
Grow the Business
No
Less Than
40%
Also Screen by Value, Effort/Cost, Risk, etc.
Adapted from Gartner 2016

18. How to Build a Winning Strategy for Data & Analytics 18
New Roles
• CAO
• CDO
• Product
Manager
• Integration
Architect
• UX Designer
• Business
Analyst
• Software
Engineer
• Data Scientist
Development Design & Architecture
Technical Executor Business Consultant
Business Generalist Business Specialist
IT Centric Business Centric
Internal Customers External Customers
Run Grow & Transform

19. How to Build a Winning Strategy for Data & Analytics 19
Optimize
Existing
• Certain
• Easy to Reverse
Make
Choices
• Certain
• Hard to Reverse
Create
Options
• Uncertain
• Hard to Reverse
Run
Experiments
• Uncertain
• Easy to Reverse
Mode 1
Mode 2
Compliance
Reporting
Data
Warehouse
MDM, LDW,
etc.
Predictive
Modeling
Current Trends
Strategic
Decision
Models
Delivery
Model
Example
App
Adapted from Gartner 2016

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Identify Sr. Executive
Sponsorship
Capture Business
Strategy
Benchmark Current
State
30 Days
Build Use Case
Portfolio
Envision Future State
Develop & Prioritize
Roadmap
Quantify the Benefits
Sell the Vision
60 Days
Execute Experiments,
Pilots and Prototypes
Procure New Tech and
Skills
Execute Choice and
Option-Based
Initiatives
90 Days+

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