decision effectiveness -- driving business value from analytics
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Introduction to Decision Effectiveness
April 29th, 2014
Rahul Saxena
rahul@cobotsystems.com
Agenda
Building an Intelligent Organization The intelligent organization has institutional capabilities to make effective decisions
Decision Inventory How to make a list of the decisions to be supported
Decision Models How decision models are used to make effective decisions
Analytics Deliverables How analytics deliverables convert the information deluge into effective decisions
Analytics Systems How analytics systems can support effective decisions
Confidential 2CobotSystems.com
BUILDING AN INTELLIGENT ORGANIZATION
Introduction to Decision Effectiveness
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The intelligent organization makes effective decisions, and make the best use of expertise and data. Sustained intelligence requires us to evaluate the outcomes and drive continuous improvement (learn & ingrain). Such intelligence is supported by the institutional capabilities to use decision cycles for continuous improvement. It also requires the supply of analytics to rise to the level of providing decision advice based on decision models.
Inconsistent
Decisions
Consistent
Decisions
Data
Oriented
Decision Models: make
smarter decisions
Learn & Ingrain: track results
evaluate, learn, & get better
The intelligent organization has Decision Effectiveness
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Direction: Intent, Purpose, Vision, Strategy, Execution, Metrics
Culture
Discipline
Intelligence
Learning
Matu
rity
Level
The institutional
capabilities
needed to
make intelligent
decisions
Not
Used
Data
Providers
Analysis
Providers
Decision
Modelers
Decision
Advisors
Inconsistent
Decisions
Decision Effectiveness gets you results from analytics
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ANALYTICS SUPPLY
Consistent
Decisions
Data
Oriented
Decision
Models
Learn &
Ingrain
AN
ALY
TIC
S D
EM
AN
D
Where are
you today?
Where do you
want to be?
Insights
Results
How do you
learn to
improve?Matu
rity
Le
vel
The institutional capabilities needed to support intelligent decisions
Today, leaders use analytics for insights … but driving results rests solely on people & change management
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Business Visibility: provide reports and dashboards; identify bottlenecks, errors, and
delays; see performance, issues, and opportunities across siloes2
Optimization: provide recommendations based on application of statistics
and operations research techniques for forecasting, optimization, etc.3
Managerial Control: people analyze data to track and manage their areas of performance,
disparate and disjointed analyses, little leverage of best practices1
Insights
Results
Change Management methods
Completely people-dependent
Decision Effectiveness provides the processes and systems to drive results from insights
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Execution Support: provide analytics to track execution, close gaps, identify
best practices, and drive successes1
Results Focus: provide analytics to track outcomes, provide early warning
of unworkable strategies, and identify winning strategies3
Insights
1
2
Decision Advice: provide analytics in the context of decision models that help people
take decisions in a timely, informed & consistent manner; always use the best model
ResultsAdd processes and systems that enable your
people to drive business results
The decision cycle process drives results from insights, in a learning loop that makes decisions effective
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Business Decision Inventory
What decisions do we make?
How do our methods improve
our decisions?
When are the decisions made? By whom?
What info do they need to make the decisions?
How can we track usage and outcomes?
Connect insights to decisions, provide
usable analytics for the decision to the
decision-makers, on time
Track if the analytics is used, and
assess how the decisions taken
reflect adoption of the analyses
Track if the decision is executed, when
(speed), to which degree, and by whom,
locate and address execution gaps
Track the results of the
decisions (costs incurred,
revenue increased, etc.)
Collaboration based on decision models is transparent and accessible, enables alignment to results
Collaboration based on decision models helps every participant add value
Minimize communication errors, avoid “black box” hand-offs
Everyone can see the effect on the decision cycles, drive from idea to execution
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Decision
Inventory
Analytics
Experts
Decision
Advisors
People who
execute the
decisions
Decision
Makers
Data
Experts
IT
Experts
Report &
Dashboard
Developers
Business
Operations
But today, we struggle on without decision models, and heroic “change managers” deliver results
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Experts drive results & value from the insights using “change management” techniques
• Develop proof-of-concept demonstrations or prototypes to drive awareness & feedback
• Update systems and processes to include the analytics, develop and deliver training
• Drive adoption using communication, education, metrics, leadership, etc., ensure that
behavior changes
INSIGHTS
VALUE
DECISION INVENTORY
Introduction to Decision Effectiveness
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The decision inventory lists the kinds of decisions that are being made and by whom. The lowest level are operating decisions that involve only one decision maker and the highest level are strategic decisions that involve many people (executives, stakeholders, and experts). The low level would be something like, "Which customer do I call on today?" The highest level would be, “What products do we produce for which markets and when?” or, “Should we sell the company?” – Dr. Steve Barrager
Your decision inventory describes where and how you provide decision support – your decision needs
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Analytics LevelsDrive business results
Business AspectsMonitor each aspect of your business
Decision LayersLink strategy to workflows
Data
Strategy
Operations
Environment
Business
Results
The decision inventory lists the decision needs, or potential demand for analytics
It can be used to measure the coverage of analytics delivery versus potential demand
The coverage of analytics delivery can be assessed for maturity of demand and supply
The decision inventory covers each aspect of your business: your strategy, structure, and capabilities
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Visibility at the summary & detail level from various aspects, to provide an end-to-end view of the
customer experience and business operations
HR, IT,
Facilities,
Finance
Distribution
Marketing
Customer
SegmentsStores
Merchandising
• Omni-channel & accessible
• Reliable & cost-effective
• Personalized & convenient
• Mobile & ambient
• Loyalty & relationship
• Responsive & agile
• Long-tail SKUs, in-store
availability, predictive
shopping-list
• Operations excellence
Product
Categories
Channels
Geos &
Regions
Any Industry Your Industry Your Structure Your Strategies, Your Differentiation
From strategy to the day-to-day execution of workflows, the decision inventory has different layers
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Support for all layers of decision-making needs, with consistent data to enable alignment
Strategy
Capability
Scheduling
Workflows
Determine the strategy: create segments, determine which
segments to address, set business objectives, enable objectives
with effective sales decisions
Align strategy with capabilities: fund and build the capabilities
needed to execute the business strategy, cascade the plan,
continually track and update
Use scheduling methods to utilize the capabilities effectively and
efficiently, control expenses and update plans
Use access to transactional data to identify bottlenecks and
opportunities, find out where and when to assist, manage load,
and closely control expenses
• Markets, Addressable Markets, Market Share
• Growth/Share Snapshot and Trends
• S-curves, Funnels, Roadmaps, Cases, Reviews
• Product Margin, Revenue, Share & Growth
• Product & Technology Roadmaps
• Go-to-Market Roadmaps & Plans
• Product & Technology Scheduling
• Go-to-Market Scheduling
• Promotions, Rebates & SPIF
• Sales Opportunity & Discounting Assistance
• Fulfillment Bottlenecks, Quality Issues
• Product Spend Approvals
The decision inventory builds up to four levels, from providing visibility to driving results
Confidential 15CobotSystems.com
Business Visibility: provide reports, dashboards & insights; identify bottlenecks,
errors, and delays; see performance, issues, and opportunities1
Decision Advice: provide analytics in the context of decision models that
help people take decisions in a timely, informed & consistent manner2
Execution Support: provide analytics to track execution, close gaps,
identify best practices, and drive successes3
Results Focus: provide analytics to track outcomes, provide early
warning of unworkable strategies, and identify winning strategies4
What happens if your decision inventory is flawed?It self-corrects in the learning loop
There is no downside to the decision inventory being flawed or incomplete, because each decision is monitored in the learning loop that drives improvement through decision cycles
You need to avoid the obverse danger, of “analysis paralysis” as you try to make the perfect inventory … there is no such thing, a decision inventory is like making a snapshot of LinkedIn
Just start with a best-efforts draft (a “straw man”) and then evolve it by the learning loop, not by spending time in the design
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DECISION MODELS
Introduction to Decision Effectiveness
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Decision models provide the “synthesis” step that is required to harness “analysis” steps. Decision models package the facts, findings, forecasts and recommendations made by analysts into a deliverable that can be used to make effective decisions. The decision model generally assists people to make decisions; only rarely does the model become fool-proof enough to be entirely automated. People implicitly use decision models to make decisions, and some of them are flawed, prone to biases, or open to exploitation. Explicit decision models provide transparency and enable improvement.
What is a decision model?
A decision model describes how a decision is to be made, such that it is the best or the most rational decision
Models differ depending on degrees of freedom – from strategy (few constraints) to workflows (tightly constrained)
This deck by Dr. Barrager (on SlideShare) describes how a key strategic decision was made: http://www.slideshare.net/barrager/design-of-a-new-corvette
The following example describes how a strategic concern was converted into simpler decision models that enabled operational decision-making to incrementally transform the organization
A simple model is better than no model (i.e., “gut feel”) because over cycles it can improve, and everyone can use the best model (a virtuous cycle to prevent decisions becoming worse)
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Example: Services had a problem that threatened their business margin
Benchmarking
Higher grades, higher costs
Lower utilization levels
Gap in leverage of offshore resources
Lost margin – we must “do something”!
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• Situation
• Options
• Decision is demanded from the
Operating Committee
• Initiative on a Page
• Investment & ROI
• Resources needed
• Assumptions
• Metrics (Project Dashboard)
• Implementation Plan
• Risk Analysis & Mitigation
Is this a crisis?
Not yet.
Not a crisis yet, but a clear business trend required us to change a business with thousands of people
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Higher grades, higher costs more expensive more experienced people
Lower utilization levels hired and promoted ahead of demand
Gap in leverage of offshore resources strong face-to-face delivery culture
Projects
Opportunities
Utilization
People Competitors• Same results for
lower cost
• Leverage the
learning curve to
sustain advantage• My project so it’s my
resource decision
• Apply the “best” (high
cost) resources
• Hungry for
utilization
• High prices lose business
• Sacrifice margin puts a
question on business model
To find a solution we looked for the valves, upstream of the inertia of massive headcounts and workflows
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Smart Global
Delivery
Face-to-Face
Delivery
Remote
Delivery
• Several examples
• Ready to scale
• Most delivery is
actually done in this
way
• The model that we
think is required by
customers
• Offshore Delivery
Pyramid
• Field Delivery
Pyramid
Hiring requisitions split to both models
FD (Field) GD (Global)
People leave … this occurs in all cases
ProjectsPeople
Governance to drive Smart Global
Delivery the “valve” is project staffing
… a.k.a., Resource Management (RM)
Control
Req. Split
Consultants stop fighting for utilization
(becomes the RM job), start focusing on
successful assignments
Project
Staffing
Solution: control the valves and you change the trends
Confidential 22CobotSystems.com
ReqsFD
Reqs
GD
Reqs
Control
Req. Split
FD
Tasks
GD
Tasks
Project
Staffing (RM)
Projects
• Thousands of people working on thousands of tasks undergo a frictionless transformation
over a period of two years because upstream valves change the flow of requisitions and
workloads
• This method combines with changes in the services portfolio and training to align people with
the new workloads, maintain market-leadership position, build margins, sustain customer
satisfaction and increase employee satisfaction
Decision models harness analytics insights to results
Confidential 23CobotSystems.com
Decision
AnalysisDecision Execution Results
• What is the decision
to be taken?
• By whom?
• When?
• Criteria?
• What information is
needed for this
decision?
• Scenarios
• Findings
• Options
• Criteria-weights
• Recommendations
• Decision-making
process
• Participants, Time &
Place
• Decision
• Recommended
Option
• Other option
• A new option
• Re-analysis
• A new option
proposed
• Other issues or
concerns
• Execution process
• Participants, Time &
Place
• Communication
Tracker
• Drive Adoption
• Targeted
reminders
• Successes
(fastest, highest
degree, etc.)
• Adoption Tracker
(Coverage, Speed,
Penetration,
Historical Trends)
• Results Tracker
• Outcome Analysis
• Execution Effect
• Decision Effect
• Other effects
• Alignment with
Rewards &
Recognition
• Environmental
factors
• Other issues or
concerns
INSIGHTS
Example: the Requisition Split Decision Model
Confidential 24CobotSystems.com
Decision
AnalysisDecision Execution Results
• What is the decision:
Req Approval or
Rejection by Finance
• By whom: AS Theater
Controller
• When: Weekly
• Criterion: Req Split
• What information is
needed for this
decision?
• Scenarios: Project
Pipeline
• Findings: Split, Split-
history, Affordability
• Options: Yes, No,
Hold-off
• Recommendation:
Yes or No
• Participants, Time &
Place
• Decision
• Recommended
Option
• Other option
• Hold off (delay for
next cycle)
• Execution process:
Req. Approvals, Hiring,
Hiring-blocks, Offer,
Acceptance, On-board
• Participants, Time &
Place
• Drive Adoption
• Targeted reminders
• Successes (fastest,
highest degree, etc.)
• Adoption Tracker
(Coverage, Speed,
Penetration, Historical
Trends)
• Results Tracker
• Outcome Analysis:
cost reduction,
change in resource
pools (FD, GD) size
• Execution Effect: by
Region
• Decision Effect: by
Region
• Other effects
• C-Sat
• Project on-time, on-
budget, on-cost
• Employee Sat
• Utilization
• Delivery Model
usage
Requisition Split Decision model generates tremendous value
Ramp-up of Global Delivery from 11% to 30% in 3 years $300+ million in cost reduction opportunity from the $700+ million in COGS every year
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Req Split
Decision Model
for
Req Finance
Approvals
Reqs for
Approval
Reqs
Approved
Reqs
Rejected
Reqs On
Hold
Project
Pipeline
Budget &
Actuals
Another Example: IT Service Desk Management
Confidential 26CobotSystems.com
Strategy
Capability
Scheduling
Workflows
• Set the service desk strategy to provide customer support and drive improvement processes to eliminate
root-causes. Enable customer satisfaction as well as product/service improvement balanced against the
cost of the service desk.
• Determine how many people you need, align needs to availability to manage utilization rates balanced
against response-time and response-quality. Service ticket workloads have spikes and toughs, fill the
troughs with improvement workloads.
• Decide training needs and drive towards aligning skill-needs to skill-availability
• Assign cases to people and build calendars based on most effective scheduling that accounts for
people’s time (including availability, vacations, etc.) and the case workloads
• Balance “problem tickets” workloads against “root-cause analysis” & “root-cause elimination” tickets
• Set and follow the calendar schedule to ensure the right balance of case-work (on-time follow-up,
easiest cases, oldest cases, etc.)
• Spend time to document the problem, cause and resolution such that it maximizes self-service later on,
reduces recurrence of the case.
ANALYTICS DELIVERABLES
Introduction to Decision Effectiveness
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An analytics deliverable is intended to support a decision. It must be provided to the right people at the right time, with the information and models needed to make an informed decision and the tools to navigate to the best decision. This assembly of disparate pieces of information may be done by business analytics people, managers, outside experts, committees, etc. A decision model can spawn many analytics deliverables, one for each instance of the decision.
Reports, Self Service BI, & Dashboards are widely thought of as “analytics deliverables”
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This report is used by the entire organization!• Selected information provided
• Focused on the business need
This single Self Service BI has the data to replace 64 reports!• No confusion about data source & integrity
• Easy to pull out the data you need … we
support 1,042 use cases in this one cube
This dashboard provides the KPIs to align performance!• Focus on the few important outcomes
• Get everyone on the same page
Then business analytics people consume these “analytics” to provide “relevant analytics”
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Report RequestReport
(Selected
Information)
Business
Analytics
Data RequestSelf-Service
(Query & Report,
Slice & Dice)
Business
Analytics
Dashboard
RequestDashboards
(KPIs in Context)
Business
Analytics
Review reports,
highlight findings
relevant for
business
Dig through the
data to get findings
or insights relevant
for business
Review dashboards and
provide relevant guidance
by combining them with
other data findings
• Descriptive
• Predictive
• Prescriptive
Analytics
Deliverables =
Relevant Reports
Analytics deliverables become relevant when they harness data to decision models and recommendations
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Scenarios• List scenarios for the decision
model (e.g., moderate, low, or
negative market growth)
• Provide an analysis of each
scenario (options and ratings)
Criteria & Ratings• List effective criteria, hard or
soft, for the decision
• Rate (assess) each of the
options for each criterion to
provide the basis for decisions
Recommendations• Highlight the
recommended option(s)
• Provide the reasoning for
the recommendation(s)
Options• Describe the options that the
decision-makers have
• Characterize each option
against the findings, decision
criteria and scenarios
Findings• Insights into the problem,
data, and decision frame
• Opportunities & problems
surfaced by the analysis
• Call out “low hanging fruit”
Data + Decision Model = Analytics Deliverable
REPORT
REPORT
Decision Relevance• Sent to the people who are
involved in the decision cycle
• Provided when needed in the
decision process (on
schedule, on demand, on
trigger)
But who needs 700 analytics deliverables? What does “relevant” mean if we face an information deluge?
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Business Need Report Request Report
Hundreds of
Reports
Each report has value:
the business need is
addressed
Later we see too
many reports, and
people ask if all of
them are neededProject to “Rationalize” Reports
Why will it never be
relevant again?
Why is it not
relevant now?
Not Relevant
Keep
Keep
Relevant
Today
Future
• Remove unused & overlapping reports
• Remove reports that are not relevant
Maybe we should “rationalize”
(i.e., radically reduce) the
number of these reports … but
which ones should we keep,
and which need to be removed?
We can’t provide analytics deliverables only when they are needed, because current systems don’t do that
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• When is it needed?
• Send to whom?
• In which context (combine
with other reports/findings)?
Where can we store this
information? Develop the
“decision memory”?
• Won’t we still waste time to
make the analysis even
when it is only needed in a
corner case?
Can we automate the
creation & inspection of an
analytics deliverable?
Decision Needs stored
in Decision Models
Automated running of
Decision Models
There is no
place to put
this kind of
functionality
in current
analytics
systems
We need a new tool to convert the information deluge into relevant analytics
Confidential 33CobotSystems.com
Continually assess
data versus decision
needs
Insights come as a torrent of findings
& math-model outputs continually
mined from data streams
Decision
models for
various
business
scenarios
Targeted recommendations for
decisions to be made or updated
based on the latest data, sent to
selected relevant decision-makers
Constantly compare a large variety of metrics
& insights against the massive variety of
decision models that apply to different
business scenarios to find the few things that
need to be acted on now. Send clear findings
& recommendations to relevant people.
… the Top Two Things to do Today
Decision Needs stored
in Decision Models
Automated running of
Decision Models
ANALYTICS SYSTEMS
Introduction to Decision Effectiveness
Confidential 34CobotSystems.com
Different decision needs require different types of analytics systems. Decision cycles are not supported in older generation systems. Recent technology advances enable faster analysis, richer data visualization, and much bigger data volumes. The design of analytics systems needs to expand to address the demands for value, scale, and speed.
Analytics systems need to address the demands of the decision inventory
Confidential 35CobotSystems.com
Get ongoing insights
from data
Dashboards, Reports,
Interactive Queries
Forecast, Optimize,
Simulate, Cluster, etc.
Operations Research &
Statistics
Using models to
improve decisions
Decision Cycle
Exploring data for
value
Prototypes Reports & Graphs Mathematical Models Decision Models
Explore
Understand
Visualize
Decisions,
Execution, Outcomes,
Inventory
OLAP
Dashboards
Reports
Develop, Deploy
& Assess Math
Models
Each decision in the decision inventory can be placed in a stage
Decisions that are supported by a decision model belong to the Decision Cycle stage
There is need for all four stages in the enterprise
Data Visualization, Mash-ups,
& Rapid Analytics
Decision Inventory
Let’s map how your analytics systems support the four stages in the decision inventory
Confidential 36CobotSystems.com
Get ongoing insights
from data
Dashboards, Reports,
Interactive Queries
Forecast, Optimize,
Simulate, Cluster, etc.
Operations Research &
Statistics
Using models to
improve decisions
Decision Cycle
Exploring data for
value
Prototypes Reports & Graphs Mathematical Models Decision Models
Data Visualization, Mash-ups,
& Rapid Analytics
Microstrategy SAS
Cognos
Business Objects
Qlikview
MiniTabSplunk
Many organizations are thinking of how to use analytics to drive value, scale and speed
Confidential 37CobotSystems.com
Dashboards, Reports,
Interactive Queries
Operations Research
& Statistics
Speed
Scale
Value
Data Services
Usage, quality, and supply
Decision Cycle
Data Visualization,
Mash-ups, & Rapid
Analytics
The scope of analytics systems must increase, and so the analytics systems framework must expand
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Data Services: usage, quality, and supply
Shared Analytics: store data to enable analytics at scale
Dashboards, Reports,
Interactive Queries
Operations Research &
StatisticsDecision Cycle
Data Visualization, Mash-ups,
& Rapid Analytics
The supply of usable data (with the required quality, at scale, and with ease-of-access) is foundational for analytics.
Data issues lurk in the data sources, as errors in the data transformations as the data flows into analytics deliverables,
in the assumptions analysts make about data, in data-movement process failures, obsolete lookups, etc.
Does your organization manage data quality like you manage product quality?
Monitor & Drive the Decision Cycles
Deliver Business Value
Create & Evolve Decision Models
Harness Innovation & Expertise
The new analytics framework connects business value to innovation
Confidential 39CobotSystems.com
Provide information
deliverables to support
the decision cycle
Get information needs
(gaps, overlaps,
opportunities …
decision needs drive
information collection)
A “LinkedIn” for Decisions… inclusive, extensible, open-ended
Domain-focused Models… focused, deep, bounded
We need to make a “LinkedIn” for Decision Effectiveness … inclusive, extensible & open-ended
Domain-focused Models
Each report or data-set is based on and constrained by the data model
It is important to design the data model to support the domain today and into the foreseeable future … this is very hard
A “LinkedIn” for Decisions
Each decision model is a module designed to drive results – we learn from each cycle, find opportunities to improve
Make views for the set of decision models to monitor and guide decision effectiveness
Confidential 40CobotSystems.com
Value, Agility, Memory, Purpose,
Integrity, Precision, Speed
Expertise, Understanding, Dedication
Exploration, Innovation, Imagination
Each decision model is a module (like a LinkedIn page) that drives results individually and in concert
Confidential 41CobotSystems.com
Deliverable Decision Execution Results
• What is the decision?
• Which reports (or
data inputs) are
needed to take the
decision?
• Who?
• When?
• How can we track
the decision?
• How can we track
execution?
• How can we track
results?
• How can we learn
from the outcomes?
The learning loop makes this a self-correcting system
Avoids the need to create the perfect decision inventory and the perfect decision model to start with …
we just go with what works or with a best-effort model and then evolve it based on how well it works
Different decision models, decision-adoptions, and execution-pathways are “natural experiments” that can be analyzed to find the best methods
Nominated “gold standard” models and pathways are continually evaluated Effectiveness gaps (delayed decision-making, deliverables that often fail or get delayed, etc.) are located
Build the intelligent organization with decision cycles, or risk stalling because you cannot link insights to value
Confidential 42CobotSystems.com
Implement the decision
cycle to harness insights to
results and assure your
progress to intelligence
and adaptability
If your organization does not
harvest the results coming
from the decision cycle, it is
difficult (and wasteful) to
sustain and grow the
creation of insights
Not
Used
Data
Providers
Analysis
Providers
Decision
Modelers
Decision
Advisors
Inconsistent
Decisions
ANALYTICS SUPPLY
Consistent
Decisions
Data
Oriented
Decision
Models
Learn &
Ingrain
AN
ALY
TIC
S D
EM
AN
D
Insights
Results
Decision EffectivenessDriving Business Value from Analyticsrahul@cobotsystems.com
This presentation extends the concepts
described in the book
Business Analytics: A Practitioner's Guide
By Rahul Saxena & Anand Srinivasan
Springer International Series in Operations
Research & Management Science
You can buy it from
http://www.springer.com/978-1-4614-6079-4
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