This Loras College Business Analytics Symposium breakout session presentation by Kiran Garimella, Ph.D., president and founder of XBITALIGN, explored the analytics center of excellence (CoE). A business analytics program is more than the application of data science and Big Data technology to data. Success should be measured not only by the valuable insights the program delivers, but also by how well it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is not only from data to information, but also from information to knowledge, and from knowledge to intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence (CoE).

1. The Analytics COE
Positioning Your Analytics Program for
Success
Kiran Garimella
Principal Consultant, XBITALIGN
Excellence in Business & IT Alignment
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2. Overview
A business analytics program is more than the application of data science and Big Data technology to data.
Success should be measured not only by the valuable insights the program delivers, but also by how well
it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is
not only from data to information, but also from information to knowledge, and from knowledge to
intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence
(CoE).
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Business Analytics Program =
(Big) Data + Technology + (Data) Science + ???
Business Analytics Program =
Setup + Analysis -> Insight + then what ???
Business Analytics Program =
A few expert data scientists OR part of the Corporate DNA?
The spectrum of COEs:
Do it for them versus enable them to do it

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The Main Thing
It isn’t about technology, but what’s in it for the decision-makers.
My stakeholder – ex-Vice Chairman of GE – said to me:
“20 years ago, my MIS department would put in front of me,
every morning, a reliable report about revenue and other
metrics from various regions based on products and services. It
looks like that’s not possible anymore.”
If you can’t help decision-makers make better decisions faster while
minimizing risk, you have done nothing.

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OWL
BAM
BI
CAF
Portals
BPEL
WYMIWYR
CMS
Web 2.0
BPEL
Cloud
Social
Complex Event Processing
ICE
PLM
PPM
SAAS
Agile
Big Data
AnalyticsHadoop python
scala
BPM
d3js
wsdl
Mobile
What we give them: CTAs
(Collage of Terrifying Acronyms)
R
Data Science

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What your users care about
Process Cycle Time
Throughput Yield
Bottlenecks
Wait-times
Defects per million opportunities
Latency Process Variance
Inventory Turns
SLA Violations
False Demand Triggers
Return Rate
Percentage Rework
Cost of Poor QualityUnnecessary Motion
Excess Processing
Economic Value AddTransportation Waste
Process Variance
Process Capability
Process Capacity
Excess Transactions
Root Cause
Voice of the Customer
Run Chart
Reduction of Waste
Overall Equipment EffectivenessKey Performance Indicators
Baseline Conditions
Compliance
Customer Satisfaction
Customer Satisfaction

6. The Ecosystem of Analytics
 Strategies &
Strategic Objectives
 Products
 Services
 Projects
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Providers
PayersPharmacists
Clinicians
Device
Suppliers
Employers
Patients (&
Families)
R&D
Academia
Professional
Bodies
Regulators
Big Pharma
 Business Capabilities
 Technical Capabilities
 IT Applications
 IT Services

7. The Taxonomy of Analytics
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Insight
Knowledge
Information
Data
Competitive Advantage
Transformation
Standardization,
Simplification
IT
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Foundation
Value

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Raw data
generation
Extraction
Collection
Cleansing
Analyzing
Packaging/
(Information)
Consuming
Decisioning
The Lifecycle of Data

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The Analytic Landscape

10. Key Elements of a COE
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People
TechnologyProcess
LearningGoverning
Sustaining
Enabling Communicating
Tooling
IntegrationBuilding
Improving

11. Maturing a COE
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12. Example of a COE Roadmap
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Rationale & Business Case Assessment, Analysis, & Prioritization COE Organization Model
COE Governance Model Competency Model & Action Plan Training Plan
COE Roadmap

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Enterprise Architecture
Technical
Architecture
Applications
Technical
Infrastructure
Services
Business
Architecture
Information
Process
Performance
Execution
Solution
Development
Project
Management
Program
Management
Operations
Operations:
Transition &
Deployment
Release
Management
Operations:
Monitor &
Control
Business
Alignment
Strategy
Change
Management
Governance
Enablement
Knowledge
Management &
Education
Personal & Team
Effectiveness
Continuous
Improvement
Business
Structure
Business Model
Ecosystem
Organization
Enterprise Capabilities Alignment Framework
for Centers of Excellence

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BusinessAlignment
Strategy
Change
Management
Governance
BusinessStructure
Business
Model
Ecosystem
Organization
ECAF: Business Structure & Alignment
Analytics that relate to strategies and strategic objectives
Strategies and strategic objectives of the analytics COE or function
Analytics that relate to change & innovation, how effectively the company is
dealing with it
Management of change and innovation within the analytics COE or function
Analytics that report on governance and compliance (are we doing the right things,
are we doing them efficiently, and are we realizing the benefits
Governance of the analytics COE or function
Analytics about and for Industry, Economy, Competition, Products, and Services
The model of the analytics COE or function as an ‘internal company’
Analytics about Customers, Partners, Suppliers, Regulators, and other bodies that
impact the company
The management of the ecosystem of the analytics Coe or function
Analytics based on Title, Role, Function, LOBs, Locations
The RACI view of the analytics COE or function and its engagement model with the
rest of the company

15. ECAF: Enterprise Architecture
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TechnicalArchitecture
Applications
Technical
Infrastructure
Services
BusinessArchitecture
Information
Process
Performance
Analytics about various types of applications, external-facing as well as internal
Applications that the analytics COE or function uses
Analytics about servers, security, performance, etc.
Technical infrastructure of the analytics COE or function
Analytics about internal services as well as technology services
Technical services that are provided by or used by the analytics COE or function
Analytics about business rules, policies, usage, effectiveness
Management of information used in the analytics COE or function
Analytics about business processes, both outside-in (customer-centric and internal)
Processes of the analytics COE or function
Analytics about performance metrics, from high-level balanced scorecard to lower-
level operational metrics
Performance (measures, metrics, KPIs) of the analytics COE or function

16. ECAF: Execution & Operations
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Execution
Solution
Development
Project
Management
Program
Management
Operations
Operations:
Transition &
Deployment
Release
Management
Operations:
Monitor &
Control
Analytics about development of new solutions, their progress, impact, and value
Development of analytic solutions
Analytics about projects, their goals, metrics, value
Management of analytic projects
Analytics about programs, initiatives, value generation, constituent projects
Management of analytic programs
Analytics about transition and deployment of solutions, people, processes, and
technology
Transition and deployment of analytic capabilities
Analytics about releases of business capabilities or technical capabilities
Management of releases of analytic capabilities
Analytics about operational control, such as process performance controls, KPIs,
etc.
Monitor and control of analytics operations

17. ECAF: Enablement
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Enablement
Knowledge
Management &
Education
Personal & Team
Effectiveness
Continuous
Improvement
Analytics about knowledge management and training in the company
Knowledge management and education about analytics
Analytics about effectiveness within team and among people
Ensuring personal and team effectiveness about adoption and usage of analytics
Analytics about continuous improvement initiatives
Ensuring continuous improvement of the analytics function

18. General Observations on Analytics
• Data -> Information -> Knowledge -> Insight
• Insight also comes from a mass of interconnected knowledge, not
only from a dataset
• If you torture the data long enough, it’ll confess to anything you want
• Do you really want to buy a Ferrari to go get groceries?
• Perceptual errors are almost always errors of higher cognition!
(Pencil in a glass of water appears bent.)
• No amount of training will change perception (a Noble Prize physicist
and a peasant will both see a bent pencil in a glass of water)
• Humans are poorly equipped to deal with probability, statistics, and
consistency in logical thinking
• Training can mitigate cognitive errors
• Tooling must provide the safety harness and the do the grunt work
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19. Next Steps: Get Started!
Use to ECAF framework for COEs to determine:
Who you are (culture, stakeholders)
Why (drivers)
Who you want to be (vision, mission)
How (high level: strategies, goals)
Where you are (current level of maturity)
Focus areas: prioritize (don’t try to boil the ocean)
Include some elements to cover people, process, and
technology
Establish governance (top-down or bootstrap)
How (detailed: phases, roadmap, maturation)
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20. ‘ARISE’
Analytics for Business Transformation
• Align – Connect goals and strategy for
business transformation to tactics
• Reframe - Don’t be shackled by the
past (“it didn’t work before”)
• Innovate - Don’t do the same thing
again and expect different results
• Seek Help – Don’t try it alone
• Execute - To implement strategy
effectively, get close to the ground
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Adapted from www.forbes.com
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