Business Intelligence
– Not Intuition –
Will Save Your Business
Claudia Imhoff
President
Intelligent Solutions, Inc.
© Copyright 2004, Intelligent Solutions, Inc.
Topics
 Business Intelligence
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What it is and how do I get it?
What are the benefits I should expect from it?
 Case Studies
 Evolution of the Business Community
 Making Life Simple
Business Intelligence
 An environment in which business
users receive data that is:
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Reliable
Consistent
Understandable
Easily manipulated
Flexible
Business Intelligence (continued)
 For analyses that yield an overall
understanding of:
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Where the business has been
Where it is now
Where it will be in the near future
How Business Intelligence Helps
 BI serves two main purposes:
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IT monitors the financial and operational health of
the organization
 Reports, alerts, alarms, analysis tools, key
performance indicators (KPIs) and dashboards
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IT regulates the operation of the organization
 Two-way integration with operational systems,
information feedback analysis
 BI, without the ability to act on it, is not worth
much
BI Monitors the Enterprise’s Health
 As competition intensifies and efficiencies are
harder to find, use BI to quickly discern:
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Trends in customer profitability, operating expenses,
market share and competitors
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Patterns in customer purchases, demographic
profiles and stores, investments and risk
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Relationships among product purchases, suppliers
and stores, campaigns and targeted customers
BI Regulates a Company’s Operations
 BI is no longer a luxury – it is essential
 Decisions are made at lower and lower levels
in organizations:
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Everyone is an analyst at some point in the day
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They must have access to the results of BI to make
appropriate decisions
Front-line workers are increasingly responsible for
making large decisions
Using BI Throughout Your Company
 Sales and Marketing
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Understand customer needs, respond to market
opportunities, gauge effects of pricing and
promotions, analyze buying behaviors, target
customer segments
 Product Development
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Access to critical customer and market data, as well
as supplier information, to determine solid costbenefit analyses about features and materials
Using BI Throughout Your Company
(continued)
 Operations
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A mechanism to analyze quality control, inventory
management and production planning performance
 Customer Service
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Accurately assess value of market segments and
individual customers plus determine retention
strategies for most profitable customers
BI Benefits
 Lowered operating costs
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A well-architected environment reduces support from
database administrators, analysts and other IT
specialists
 Reduced IT workload
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Individuals can perform their own queries and analyses
 Better access to information – no guesswork
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Solid, timely decisions are made
 Collective knowledge about business drivers
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Insight and experience is sharable across the
organization
A Few Statistics…
 IDC* studied analytic applications and their
impact on core business processes:
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Marketing campaign
Fraud detection
Portfolio management
 What was the ROI for Business Analytics?
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46% of organizations generated ROI < 100%
34% generated ROI between 101% and 1,000%
20% generated ROI > 1,000%
* “The Financial Impact of Business Analytics,” IDC, www.idc.com/analyticsroi/
A Few Statistics. . .
(continued)
 Value accrued through
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Increased business performance to reduced
operations to improved customer relations
Topics
 Evolution of Business Intelligence
 Case Studies
 Evolution of the Business Community
 Making Life Simple
Topics
Wal-Mart – 500% Growth with No Decline
Data Warehouse in Production
This slide and the following two slides from Greg McMullen – Ford Motor Company
3M Company - A Great 4 Year Stock Chart
Data Warehouse in Production
FedEx – Impressive 300% Gains
94
95
96
Data Warehouse in Production
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98
99
00
01
02
Case Study – HSBC Bank, USA*
 Offers checking, loan, other financial products
to customers
 Goal – maintain high customer acquisition &
retention rates, keep operations profitable
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Expand relationships with current customers
Keep marketing costs low, move into markets quickly
*www.spss.com – success stories
Case Study – HSBC Bank, USA*
(continued)
 How?
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Created predictive models to find cross-sells &
“rollovers”
Focused on best prospects
 Results
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Identified specific customer needs – boosted sales
by 50%
Reduced marketing costs by 30%
Increased ability to deploy timely marketing
strategies
*www.spss.com – success stories
Case Study – Ford Motor Company*
 Global auto maker – 33 million drivers
 Goal – Ensure high customer satisfaction
 How?
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Mix of reports, trending, segmentation, scoring &
life-cycle analyses to support key CRM activities
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Predictive modeling to leverage customer
understanding, target right customers for > 200
campaigns a year
*www.sas.com – success stories
Case Study – Ford Motor Company*
(continued)
 Results
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Reluctant to give dollar figures on results but . . .
ROI has been reached with large increases in
incremental sales
*www.sas.com – success stories
Case Study – The World Bank*
 ITs mission – fight poverty with passion
& professionalism
 Goal: To perform quick, insightful
analyses on many economic indicators
from a hundred countries
 How - BI environment that
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Allows users to perform complex calculations
in seconds in a context and format relevant to
individual users
Provides trustworthy economic data analyzed
on the fly
* www.microsoft.com
Case Study – The World Bank*
(continued)
 Results
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Economists have analytical, collaborative
capabilities contributing to improved oversight making governments more accountable
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Great variety of skills in users satisfied – heads of
state, finance ministers, chief economists,
educators, etc.
* www.microsoft.com
Case Study – Victoria’s Secret*
 Part of the Limited and Intimate Brands,
Inc.
 Goal
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Determine customers likely to respond to
targeted marketing campaigns and to help
influence behavior
*www.sas.com – success stores
Case Study – Victoria’s Secret*
(continued)
 How?
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Apply analytics to single, customer-centric
database giving comprehensive vie of all customer
interactions
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Identified right customers for specific marketing
campaigns that result in multi-channel shopping
 Results
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A potential 400% ROI from pilot test
*www.sas.com – success stores
Case Study – CompUSA*
 Largest computer retailer in North America
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Their business – helping customers improve their
personal and business productivity through
technology
 Goal – Decided to take own advice – improve
own productivity
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First to recognize a sales trend wins. Translation –
know your customer!
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Transform data into high value BI
* www.microsoft.com
Case Study – CompUSA*
(continued)
 How?
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Created sophisticated BI environment with low TCO
 Results
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From store floors to corporate HQ – employees put
BI to work, see product relationships, marginenhancing capabilities, improved bottom line
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ROI - $6 million a year!
* www.microsoft.com
Topics
 Evolution of Business Intelligence
 Case Studies
 Evolution of the Business Community
 Making Life Simple
Topics
Evolution of the Business Community
 The Business Community has matured
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Operators
Farmers
Miners
Explorers
Tourists
EVERYONE!
Farmers – Clear Sighted
 Monitor the effect of decisions on the
business by tracking key performance
metrics
 Provide Explorers/Miners with feedback on
the effectiveness of their predictions
Farmers – Clear Sighted
(continued)
 Demonstrate a fairly predictable pattern
of usage
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They know what data they want, how they want it
displayed, when they want it and in what media
 See the world in terms of dimensions
(time, product, geography) and metrics
(usage, counts, revenue, costs)
Farmers – Clear Sighted
(continued)
 Farmers predominantly use multidimensional
data marts
 Examples of Farmers
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Sales Analysts
Financial Analysts
Market Campaign Managers
Accounting Analysts
Data Delivery
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
Explorers - Innovative
 Endeavor to understand what makes the
business work by looking for hidden
meanings in corporate data
 Have little or no idea what to expect from
query execution
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An “out of the box” thinker
Launches large and often unpredictable queries often receives no results back
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Occasionally receives incredible insight
Explorers – Innovative
(continued)
 Strive to predict the future based on past
results
 Very knowledgeable about data content within
and outside the business
 Demonstrate an unpredictable pattern of usage
 See world in terms of data and data
relationships
Explorers – Innovative
(continued)
 Explorers may start with multidimensional
data marts but often require their own
environment
Exploration
Warehouse
DSI
Exploration
Warehouse
DSI
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
 Examples of Explorers
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Insurance Actuaries
Process Control Engineers
Market Research Analysts
Data
Delivery
Miners - Thorough
 Scan large amounts of detailed data
looking for confirmation of a hypothesis
or for suspected patterns
 Have a good idea what to expect prior to
query execution
 Operate on a base of data that is
preconditioned for analysis
Miners – Thorough
(continued)
 Demonstrate a reasonably predictable
pattern of usage
 Interested in finding meaningful
relationships in transactions
Miners – Thorough
(continued)
 Miners may start with multidimensional data
marts but often require their own
environment
 Examples of Miners
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Expert Marketers
Risk Controllers
Logistics Specialists
Statisticians
Data Delivery
Mining
Warehouse
DSI
Mining
Warehouse
DSI
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
Tourists - Generalists
 Have a broad business perspective and are
aware of the data produced by the business
 Use the Corporate Information Factory
frequently
 Cover a breadth of material quickly but in little
depth
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Are accustomed to a consistent graphical user
interface
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Need ability to search large banks of data
without a lot of typing
Tourists – Generalists
(continued)
 Demonstrate unpredictable patterns of usage
 See the world in terms of business functions
Tourists – Generalists
(continued)
 Tourists predominantly use multidimensional
data marts and/or informal warehouses
 Examples of Tourists
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Executives
Managers
Casual Users
Data
Delivery
OLAP
Data Mart
DSI
OLAP
Data Mart
DSI
OLAP
Data Mart
DSI
OLAP
Data Mart
DSI
Operators - Focused
 Use the intelligence derived by Explorers
and Farmers to improve business conditions
 Provide increasing pressure on the
Corporate Information Factory in terms of
availability, data freshness and query
performance
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Need fresh, detailed, day-to-day information
Expect transactional performance & response times
Operators – Focused
(continued)
 Demonstrate a fairly predictable
pattern of usage
 See the world in terms of process
Operators – Focused
(continued)
 Operators predominantly use the operational
data store and sometimes multidimensional
data marts
Operational
Data Store
Data
Delivery
TrI
 Examples of Operators
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Customer Support Representatives
Manufacturing Line Managers
Inventory Control Managers
Oper
Mart
TrI
Oper
Mart
TrI
OLAP Data
Mart
DSI
OLAP Data
Mart
DSI
Summary
 There are different BI communities that use
the Corporate Information Factory – each
using different parts of the architecture:
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Farmers
Explorers
Miners
Tourists
Operators
Topics
 Evolution of Business Intelligence
 Case Studies
 Evolution of the Business Community
 Making Life Simple
Topics
Making Life Simple
 Start with a solid, proven architecture
 Simplify the environment
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Mask the technological complexity where possible
Reduce the complexity of the access tools
Remove the barriers to information
Remove the clutter
The Corporate Information Factory
Library & Toolbox
Information Workshop
Workbench
Information Feedback
External
API
ERP
API
Internet
API
Legacy
API
Other
Data
Delivery
DSI
Data Mining
Warehouse
DSI
OLAP
Data Mart
DSI
Oper Mart
DSI
Operational
Data Store
TrI
Operational
Systems
Systems
Management
Data
Acquisition
Data
Warehouse
CIF Data
Management
Exploration
Warehouse
Meta Data Management
Data Acquisition
Management
Operation &
Administration
Service
Management
Change
Management
Corporate Information Factory
 A well-planned realization of data integration
and decision support technologies needed
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Back-end infrastructure for maintenance and sustainability
Front-end delivery mechanism for analyses or business
intelligence (BI)
 The CIF is a road map or blueprint
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For all CRM (and other) systems that support and drive
business analytics
ensuring coordinated deployment of CRM technologies
Demonstrating data flows into and out of technologies as
well as their process interactions
Mask Technological Complexity
 Business community should not have to
understand the technical architecture to use it
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BI tools should match whatever access methods and
needs the business community currently has
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Appropriate dashboards, portals or other interfaces
are then developed according to these needs
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The environment must be flexible
 Obtained by constantly monitoring usage patterns
and revamp, revise the interfaces as needed
Mask Technological Complexity
(continued)
 Create a workbench based on the workflow
for given activities
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Bring together appropriate BI and operational
capabilities to support each workflow.
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The environment must remain flexible!
Reduce Access Tool Complexity
 Don’t use a Cadillac when a Volkswagen
Bug will do
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Simple user needs require simple interfaces
 Access tools that can grow in complexity
as the need arises, should be chosen
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Bring in more capabilities when a definite
need is identified
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Everyone has Excel . . .
Reduce Access Tool Complexity
(continued)
 Don’t assume that one tool will fit all users
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You will eventually need at least one tool in each of
the major usage categories
 Query and reporting
 Multidimensional
 Data Mining
 Exploration
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BUT – walk before you run!
Remove Barriers to Information
 THINK SIMPLE!
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The vast majority of business users have very simple
BI requirements
 IT should study business MBOs
 KPIs are understood and presented in simple
formats
 Remember – Give IT the support it needs
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Don’t create a single data mart design to satisfy every
possible query – performance suffers
Simplify your requirements to just the needed bits –
more will come later
This simplifies the administration of these capabilities
– timely delivery is important
Remove the Clutter
 Present right person with right information,
at right time, using right technology
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Different analyses require specific technologies,
specific data model designs, and specific sets of
data
 Query and reporting
 Multidimensional analyses
 Data mining
 Ad hoc
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The business community must be able to choose
what data they want from the full offerings
available to them
Claudia Imhoff
Intelligent Solutions, Inc.
CImhoff@IntelSols.com
(303) 444-6650
www.IntelSols.com