Building an Analytics CoE (Center of Excellence)

Building an Analytics CoE
Rahul Saxena
Workshop at IIM Bangalore
July 20, 2013
This presentation is based on the material available in the book:
Business Analytics: A Practitioner's Guide by Rahul Saxena & Anand Srinivasan
Springer International Series in Operations Research & Management Science
http://www.amazon.com/dp/1461460794

Agenda
 Introductions – about the group, about me. What is "analytics"?
What is an "Analytics CoE"?
 What does the organization want to do with analytics? What is the
role of the CoE that they envision?
 What is the organizational context? Current providers of analytics?
Leadership support?
 What will the Analytics CoE need to be like (now and in the future,
up to the planning horizon)?
 Where do we stand with analytics capabilities now, compared to
what we need?
 How will we evolve the CoE? Set expectations, drive the evolution,
establish the value.
Confidential 2CobotSystems.com

About the group …
STUDENT WORKING
0-5 Years
5-10 Years
10-20 Years
>20 Years
Full-time
student, 0-5
years of
experience
Currently
working, 0-5
years of
experience
Demographics TEACHING
Currently
teaching, 0-5
years of
experience
PROVIDER
ANALYTICS
IT
(BI, DW)
BUSINESS
OTHER
(Academic,
etc.)
Provided analytics
as an analytics
professional
Provided analytics
as part of an IT role
Provided analytics
as part of a
business role
Provided analytics
as part of an
academic or other
role
CONSUMER
Consumed
analytics from
professional
analytics provider
Consumed
analytics from IT
provider
Consumed
analytics from
business provider
Consumed
analytics from
other provider
Perspectives

About me
NowCobotSystems
2013Cisco
2012Cisco
2011Cisco
2010Cisco
2009Cisco
2008McAfee
2007McAfee
2006Epiance
2005Epiance
2004IBM
2003IBM
2002IBM
2001IBM
2000IBM
1999IBM
1998IBM
1997IBM
1996LaitramCorp
1995LaitramCorp
1994TulaneMBA
1993TulaneMBA
1992IndianRailways
1991IndianRailways
1990IndianRailways
1989IndianRailways
1988IndianRailways
1987SCRAJamalpur
1986SCRAJamalpur
1985SCRAJamalpur
1984SCRAJamalpur
1983IITKanpur
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
Engineering
Mgmt
ERP, e-Biz,
CRM
MBA
Analytics
Management
Management
Consulting
US based
A timeline diagram
Business
Architecture
Business
Analytics

What is “analytics”?
Management
Information
Systems
Industrial
Engineering
Operations
Research
Finance
Planning &
Analysis
Statistics
Business
Intelligence
Strategy &
PlanningBiz Ops
(SCM, CRM,
Svc, etc.)
ANALYTICS
Computer
Science
… diverse
antecedents
Business
Strategy
Psychology
6 sigma,
Quality, Lean

What is “analytics” used for?
… create value by
driving decisions from
idea to execution
•Frame
•Inform
•Forecast
•Deliberate
•Recommend

What methods/techniques does “analytics” use?
ILLUMINATE DIAGNOSE FORECAST OPTIMIZE
• Charts & Tables in Reports & Dashboards
• Alerts, Visualizations, and Simulations
• Analysis, Interpretation & Recommendations
• Multi-Criteria Decision Models (MCDM), etc.
… use any method
that helps to drive
from idea to execution
MCDM, etc.

What data does “analytics” use?
TRANSACTIONS CONVERSATIONS OBSERVATIONS
• Structured
• ERP
• CRM
• HRMS
…
• Unstructured
• Documents
• Websites
• Social Media sites like
Facebook, Twitter,
LinkedIn, YouTube,
Blogs
…
• Sensors & Signals (M2M)
• Scans (Bar-codes, RFID,
proximity, etc.)
• SCADA (Control Systems)
• Temperature, pressure,
illumination, etc.
• Location (GPS, etc.)
• Smart-phone data feeds
• Network monitors
• Video feeds
…
… use any data as
needed, internal &
external

What domains is “analytics” used in?
Decision Layers
Strategy, Capacity, Scheduling, Execution
… use wherever there
is a need for making
rational decisions
Verticals
Telecom, High-Tech, BF&S, Oil &
Gas, Healthcare, Government, etc.
Functions
Marketing, Sales, e-Commerce, Fulfillment,
Supply Chain, Product Lifecycle, Strategy,
Finance, etc.

What systems/tools does “analytics” use?
Confidential CobotSystems.com
… use whatever
tools are needed
Business
EPM, Collaborative
Decision Making,
Decision-Support,
Alerts & Feedback
IT
Data Warehouses, ETL (Extract, Transform, & Load)
Business Intelligence (Dashboards, Reports, Self-service)
Big Data
Analytics
Innovation: from idea to value
Operations: continuous value realization
10

… a working definition
Analytics is used to
create value by driving
decisions from idea to
execution
… use any method
that helps to drive
from idea to execution
… use any data as
needed, internal &
external
… use wherever there
is a need for making
rational decisions
… use whatever
tools are needed
11

Applies insights to data to produce analytics deliverables and drive business value
Variety of Analytical Insights
Process
System
Skills
Knowledge
Decision
Support
Data
Variety of
Deliverables
for various Business
Decision Contexts &
Processes
Drive
Business
Value
Variety
of Data
Sources
&Errors
New
sources
& errors
New
insights
New
Opportunities
& Issues

Agenda
Leadership support?
what we need?

What do leaders talk about when they talk of analytics?
Supply DemandCurrent
State
Future
State
How should we manage
analytics investments to
gain efficiency?
Why do we need so many
reports? Why does it take
so long or cost so much?
How can we develop
analytics capabilities to
meet demands? My peer
has this, I want one too!
What can analytics do to
improve our business
results? Beat the
competition?

What do the leaders want to do with analytics?
 The CEO asked for it
 The CIO has plans for it
 XYZ business leader wants it
FOR WHOM?
Finance
Planning &
Analysis
Strategy &
Planning
Biz Ops
(SCM, CRM,
Svc, etc.)
… for whom will you
create value?
… what decisions will
you drive?
… do the current
decision-owners think
you’re helping?

What decisions will you drive with analytics?
Analyze contract renewal
data to find opportunities
Feed the workload (renewal
opportunities) to Sales
Schedule follow-ups by the
Sales Call Center
Get the renewals, improve
renewal rate
Finance
Planning &
Analysis
Sales Ops
Call Center
Ops
Call Center
Ops

Answer the “what”, enlist the “who”
Opportunity
Pipeline
Bookings Backlog Delivery
Deal P&L
Impact
Analytics
Strategic
Account
Analytics
Delivery
Optimization
Analytics
Revenue
Revenue
Gap
Analytics
Bookings
Trend
Analytics
Talent &
Skills
Analytics
Resource
Planning
Analytics
Revenue &
Cost
Alignment
… get clients by offering business value

 Protect margin using deal analysis
 Improve quality of business by targeting deals
• Analyze each deal for timing of costs (COGS
& PCOGS), revenues (milestones), & risks
(e.g., pay per SLA); assess FMV and VSOE
impacts; calculate deal impact on the P&L
• Plug-in deals pipeline into the P&L projections,
provide updates based on actual deals and
run-rate
• Work with Geo leaders to drive decisions to
improve the Sales P&L
• Approve or modify deals based on insight
into P&L impact
• Deal targeting to actively manage and
improve the Geo P&L
P&L
Deals
Analysis
Improved
P&L
Control
M1 M2 M3 M4 M5 M6
COGS $33 $33 $33 $33 $33 $33
PCOGS $21 $21 $21 $21 $21 $21
COSTS $54 $54 $54 $54 $54 $54
DELIVERY HLD LLD NRFU
REVENUE $50 $270 $310
Q1 Q2 Q3 Q4 Full Year
PROJECT MARGIN ($8) $34 $56 $43 $125
P&L IMPACT -1% 4% 7% 5% 4%
Q1 Q2
Deal P&L
Impact
Analytics
PROJECT
METHOD
DECISION
• Geo P&L Owner
• Geo Vertical Leads
OWNERS • Geo Finance Lead
• Geo Sales Ops Lead
… also track costs & value … keep clients by delivering business value

What is the role of the CoE that they envision?
… provide us the analysis,
we promise to use it to
create business value
… help us throughout the idea
to execution cycle to deliver
business value
… provide us decision advice
based on analytics

Agenda
Leadership support?
what we need?

What is the organizational context?
DRIVE
DECISIONS
Analytics
Providers
• Meetings (with customers, suppliers,
project teams, colleagues, etc.)
• Walking around, observing
• Mass media (Newspapers, TV, etc.)
• Internet, Books, Gut Feel, etc.
Suppliers
Substitutes
 Relevant
 True (data is
correct, models
are verified)
 Actionable
(provides the
basis for
specific action)
… apply the noise filter

What do analytics deliverables look like?
CommunicationDeliverable
Messages (email, SMS) to
Inform & Recommend
Documents (Reports,
Dashboards) to Inform,
Forecast, & Recommend
Decision Explorers to
Frame & Deliberate

Where will we find Analytics Providers?
Analytics providers are
found in many places, it
helps to cluster them
into these five domains
1. Business: managers
and their assistants
2. Analytics: analysts in
“analytics” teams
3. IT: in the BI, DW, or
Reporting teams
4. External BI Providers
5. External Analytics
Providers
Confidential CobotSystems.com 23

Who are the current providers of analytics?
Leader &
Department
Data Providers Noise Filter Analytics
Providers
Tom, VP, Sales IT, BizA-CoE,
MktDudes.com
Jack, Director Sales
Ops
Jack, Director Sales
Ops, MktDudes.com
Jim, VP, Engineering IT, PLM-Specialists.com PLM-Specialists.com Mary, PLM Manager
Harry, CFO BizA-CoE BizA-CoE BizA-CoE

Not
Used
Data
Provider
Trusted &
Relevant
Decision
Modelers
Decision
Advisors
Inconsistent
Decisions
Leadership support?
SUPPLY
Consistent
& Disciplined
Data
Oriented
Decision
Models
Learn &
Ingrain DEMAND
Harry
0% to
+10%
More
than
10%
Reduce
Jim
• The size of the bubble
denotes $ funding
• The color of the
bubble denotes growth
Tom

Agenda
Leadership support?
what we need?

Analytics Provider Network
The analytics function
cannot be the locus of
all the analytics assets
(and talent) for the
organization – it is,
instead, a capability that
enables the effective
use of analytics
… understand the
network, figure out your
partnering strategy
Confidential CobotSystems.com 27

Enable the Analytics Network for the organization
DRIVE
DECISIONS
BICC
(IT)
ERP
(IT)
CRM
(IT)
BizA
CoE
Sales
Ops
PLM
Mgr
SAS
D2
IIM B
PLM.com
Dir
FP&A
Zoom out of the “centralized / distributed / hybrid” debate … it’s a network
Is analytics
everyone’s job?
Isn’t it?

Enable the Analytics Network for the organization
Shared Data in an Agile Data Platform, Rapidly Extensible
Relevance & Trust built into Deliverables via a Rules-base
Decision Models built and provided where Relevant
Learn & Ingrain, rapidly adapt/evolve all layers
Data, in context
Relevance
Data-driven Decisions
Adapt & evolve
Provide the analytics platform to enable everyone in the network
… make “analytics” work for your organization

Where is the bottleneck? Demand or Supply?
Actual demand = Low
Potential demand = High
Actual supply = High
Potential supply = Low
Demand is a function of leadership
& imagination: stimulate the
imagination, deliver successes,
make analytics accessible with a
clear business value proposition
… transform the organization
culture to use analytics
... align supply to demand

Not
Used
Data
Provider
Trusted &
Relevant
Decision
Modelers
Decision
Advisors
Harry
Align supply to demand, and also stimulate demand
SUPPLY: Processes, Skills & Systems
Jim
Tom
Jim
Tom
Harry
Shared Data, Agile Data Platform
Relevance & Trust
Decision Models
Learn & Ingrain
… provide the
analytics platform
… stimulate demand:
in-house successes,
industry examples,
conferences, peer-to-
peer networks,
decision advisors

Analytics Network dynamics
 Nodes Evolve: Mature, Add, Re-organize, Disappear
 Deliverables Evolve: Innovation  Operation
 Data Evolves: Federate the data to keep pace
 Platforms Evolve: Avoid obsolescence, embrace agility
 Talent Flows: Enable people with career paths
 Funds Flow: Manage funding for nodes & the platform

Deliverables Evolve: Innovation  Operation
INNOVATE
CULTIVATE
EXECUTE
 Work with data providers to improve data
 Drive value realization through analytics usage
 Ensure ongoing validity of the analytics
 Provide analytics deliverables per SLA
 Develop new insights & concepts
 Drive from Idea to Execution
 Assess value & need
 Explore and Fail-fast
 Continual creation of Analytics Deliverables
 Continual assessment of Data & Model Validity
 Continual governance of Policies
 Continual monitoring of Usage
INNOVATION: TALENT-DRIVEN
OPERATIONS: PROCESS-DRIVEN

Deliverables Evolve: Innovation  Operation
Junk 1-time Useful
1 100% 0% 100% 80% 16% 4%
2 100% 4% 96% 77% 15% 4%
3 100% 8% 92% 74% 15% 4%
4 100% 12% 88% 71% 14% 4%
5 100% 15% 85% 68% 14% 3%
6 100% 18% 82% 65% 13% 3%
7 100% 22% 78% 63% 13% 3%
8 100% 25% 75% 60% 12% 3%
9 100% 28% 72% 58% 12% 3%
10 100% 31% 69% 55% 11% 3%
11 100% 34% 66% 53% 11% 3%
12 100% 36% 64% 51% 10% 3%
13 100% 39% 61% 49% 10% 2%
14 100% 41% 59% 47% 9% 2%
15 100% 44% 56% 45% 9% 2%
16 100% 46% 54% 43% 9% 2%
17 100% 48% 52% 42% 8% 2%
18 100% 50% 50% 40% 8% 2%
Repeat
Delivery
Total
Capacity
Capacity
for New
New Delivery
The suppliers’ dilemma: the more useful analytics
you produce, the more you get mired in repeat delivery
… which is valuable repeat business but boring workJunk
Always
Useful
1-time
Useful
Repeat
Operations
Repeat
Operations
over time, the operations workloads pile up
… this is a mark of success: your stuff is useful!
… manage it with automation
TIME
100% Innovation

Agenda
Leadership support?
what we need?

Analytics roles and functions

Agile & Production Analytics: Innovation & Operation

Workloads: what do your "Analytics" people do?
Strategic Scorecards, Metrics Portfolios, & Scenarios
Reports & Commentaries; including Forecasts
Consultative Analysis Projects (Stats, Optimization, Simulation, etc.)
Enterprise Modeling, Business Modeling, Industry/Market Analysis
Data Stewardship (Assess, Cleanse, Enrich)
Business Case Modeling, Business Value Analysis, Valuation (Financial)
Dashboards
Governance Meetings & Follow-ups
Target, Actual, Outlook, and Revision
Alerts & Follow-ups
Communication & Driving Adoption of Data-driven Management
o Frame the Decision Need
o Generate Ideas
o Support Decision Making
o Monitor Execution
o Drive Results

Regression & Correlation
Propensity Models (Logistic Regressions, Multinomial Models)
Statistical Hypothesis Testing
Forecasting
Clustering and Segmentation
Optimization (LP, NLP, etc.)
Sampling & Surveys
Benchmarking
Pricing Optimization & Revenue Management
Design of Experiments
Financial & Risk Management
Search & Search Optimization
Queuing, Simulation
Statistical Process Control (R Charts, P Charts, etc.)
Planning, Scheduling, & Dispatch; Campaign Tracking
Decision Analysis, Multi-Criteria Decision Making, Game Theory
Project & Program Management, PERT, CPM
Quality Management (TQM, 6Sigma, ZeroDefect, etc.)
Business Architecture & Systems Modeling
Controls & Compliance (SOX, Basel, ISACA, etc.)
Organizational Behavior, Organization Theory, and Strategic Mgmt
Techniques: what methods & techniques do you use?
o Frame the Decision Need
o Generate Ideas
o Support Decision Making
o Monitor Execution
o Drive Results

Systems: what tools do you use? … the BI view
 Data Loading & Processing
 Batch
 Stream
 Data Sourcing
 Transactions
 Conversations
 Observations
 Data Marts & Warehouses
 Closed Access (only via IT)
 Open Access
 Offline Analytics Tools
 Reporting
 Dashboards
 Self Service, Slice/Dice, Drill-down
 Data Quality
 In-line Analytics
 Surveying & Feedback

Systems: what tools do you use? … the Analytics gap
 Innovation
 Frame decision needs in an enterprise model
 Manage idea-to-execution pipeline
 Visualize, Explore, Cluster, Segment, Forecast,
Optimize, Simulate, Machine Learning, etc.
 Decision support (AHP, MCDM, Bayes, etc.)
 Operations
 Execution (Trusted, Actionable, Responsive)
 Learn & Ingrain
 Data Provisioning
 Agility, Reliability & Serviceability
 Traceability & Fidelity
 Data Assessment & Scrubbing
 Manage Data Problems
 Manage Data Dictionary
 Master Data & Hierarchies
 Enterprise Ontology Model
 Shared Ontology & Enrichments
 Privacy, Security and Availability
41

Capability: does the analytics team leverage the
spectrum of analytics techniques
1. Get/provide data, measure and improve data quality
2. Observe, understand and explain business issues – create analyses,
generate insights, monitor execution, guide value realization
3. Forecast trends, predict outcomes and behaviors (assess
possibilities)
4. Formulate, simulate, and assess scenarios, develop ideas and
courses of action (develop ideas)
5. Recommend courses of action (select the best ideas: feasible and
optimal or Pareto optimal)
 Statistics
 OR
 Decision Advice

Culture: how readily do business people use the
analytics?
1. Unaware of how analytics can be used
2. Aware of analytics but do not use it for various reasons
3. Use analytics for specific decisions and actions
4. Support the creation and usage of analytics, metrics and decision
rules in an organizational model, can assess the value of analytics
5. Leader in using analytics, recognize applicability, & demand the best
analytics; innovate, learn, & ingrain analytics in business processes
 Trusted
 Actionable
 Responsive
 Learn
 Ingrain

Technology: do the analytics teams get the systems,
tools and infrastructure support they need?
1. Unable to provide analytics systems (data, tools & infrastructure)
2. Partially provides the analytics systems and data required and
refreshes the data periodically for off-line analyses
3. Fully provides analytics systems and refreshes the data periodically
for off-line analyses
4. Enables analytics to be executed in-line, i.e., used seamlessly within
business processes
5. Delivers enterprise-wide in-line analytics, so the organization can use
analytics at scale and without delay
 Shared data
 Trusted, Actionable,
& Responsive
 Fast decisions
 Closed loop

Agenda
Leadership support?
what we need?

How will we evolve the CoE?
 What is the organizational
context? Current providers of
analytics? Leadership support?
 Where do we stand with
analytics capabilities now,
compared to what we need?
TARGET
STATE
CURRENT
STATE
 What does the
organization want to
do with analytics?
What is the role of
the CoE that they
envision?
 What will the Analytics CoE need
to be like (now and in the future,
 How will we evolve the CoE? Set
expectations, drive the evolution,
establish the value

Establish the value
Experimentation (A/B Compare)
 The model is used in an
“experimental” setting,
against the current “gold
standard” method
 Compare the results of the
model to the “gold
standard”
Before/After Compare
 The model is deployed
globally
 Compare the results before
and after the model is
deployed (problem remains
that there can be other
explanatory factors)

Move fast & patiently
What can be done fast
 Bring right expertise (skills) into the
analytics network
 Set up the analytics platform
What takes time
 Change the decision-making
culture of the organization
 Produce relevant, trusted
& actionable analytics
 Help people to use the
analytics deliverables
 Track the value created
What must be done right

Evolve the analytics process capabilities
Failure is part of innovation
… manage it by ensuring it occurs in the context of the
analytics platform and processes (back those innovation
efforts that have an idea-to-execution perspective,
remember and learn)
Concept to Value Realization
Process Value Management
Capability Value Management
Organizational Value Management
INNOVATION: TALENT-DRIVEN
OPERATIONS: PROCESS-DRIVEN
EVOLVE
Role
Coverage
Obsolescence is part of operations
… manage it by ensuring it occurs in
the context of long term relevance
(don’t lose memory)

Today 1 year 2 years 3 years 4 years
Inconsistent
Decisions
Change the decision-making culture of the organization
Consider leadership, trajectory, overshoot and undershoot
SUPPLY
Consistent
& Disciplined
Data
Oriented
Decision
Models
Learn &
Ingrain DEMAND
Jim
Harry
Tom
Jim Harry
Tom

This presentation is based on the material
available in the book:
Business Analytics: A Practitioner's Guide
By Rahul Saxena & Anand Srinivasan
Springer International Series in Operations
Research & Management Science
http://www.amazon.com/dp/1461460794

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