Decision Effectiveness -- Driving Business Value from Analytics

Introduction to Decision Effectiveness

April 29th, 2014

Rahul Saxena

[email protected]

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



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


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


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


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


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


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


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


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



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


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


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


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


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


DECISION MODELS



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)


http://www.slideshare.net/barrager/design-of-a-new-corvette

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”!


• 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


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


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


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


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


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


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


Place

• Decision

• Recommended

Option

• Other option

• Hold off (delay for

next cycle)

• Execution process:

Req. Approvals, Hiring,

Hiring-blocks, Offer,

Acceptance, On-board


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


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


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



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”


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”


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


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?


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


• 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


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



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


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


Get ongoing insights

from data


Interactive Queries

Forecast, Optimize,

Simulate, Cluster, etc.


Statistics

Using models to

improve decisions

Decision Cycle

Exploring data for

value

Prototypes Reports & Graphs Mathematical Models Decision Models


& Rapid Analytics

Microstrategy SAS

Cognos

Business Objects

Qlikview

MiniTabSplunk

Many organizations are thinking of how to use analytics to drive value, scale and speed



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


Data Services: usage, quality, and supply

Shared Analytics: store data to enable analytics at scale


Interactive Queries


StatisticsDecision Cycle


& 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


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


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


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?


execution?


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


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 [email protected]

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

http://images.springer.com/covers/978-1-4614-6079-4.tif

http://images.springer.com/covers/978-1-4614-6079-4.tif

Data & Analytics

Decision Effectiveness -- Driving Business Value from Analytics