Lecture Business management information system - Lecture 25: Business intelligence

Business
Intelligence
Lecture 25


What is Business Intelligence (BI)


Definitions:

•

Business Intelligence (BI) refers to skills, processes, technologies, applications and
practices used to support decision making.

•

Systems that provide directed background data and reporting tools to support and
improve the decision-making process.

•

A popularized, umbrella term used to describe a set of concepts and methods to
improve business decision making by using fact-based support systems. The term is
sometimes used interchangeably with briefing books and executive information
systems.

•

Business Intelligence is a broad category of applications and technologies for
gathering, storing, analyzing, and providing access to data to help clients make better

business decisions.

•

A system that collects, integrates, analyses and presents business information to
support better business decision making.

•

Business Intelligence is an environment in which business users receive information
that is reliable, secure, consistent, understandable, easily manipulated and
timely...facilitating more informed decision making


What is BI (continued)
Improving organizations by providing
business insights to all employees leading to
better, faster, more relevant decisions

© 2008 Accenture. All Rights Reserved.


What is Business Intelligence?
Business Intelligence enables the
business to make intelligent, fact-based
decisions
Aggregate
Data

Database, Data Mart, Data

Warehouse, ETL Tools,
Integration Tools

Present
Data

Reporting Tools,
Dashboards, Static
Reports, Mobile Reporting,
OLAP Cubes

Enrich
Data

Add Context to Create
Information, Descriptive
Statistics, Benchmarks,
Variance to Plan or LY

Inform a
Decision

Decisions are Fact-based
and Data-driven


CPU – Content, Performance, Usability


Content





Performance




The business determines the “what”, BI enables the “how”

Minimize report creation and collection times (near zero)

Usability


Delivery Method Push vs Pull



Medium  Excel, PDF, Dashboard, Cube, Mobile Device



Enhance Digestion  “A-ha” is readily apparent, fewer clicks



Tell a Story  Trend, Context, Related Metrics, Multiple Views

Core Capabilities of BI

OLAP (online
analytical
processing) enables
a user to easily and
selectively extract
and view data from
different points-ofview.


Why do companies need BI?
What’s the best that can happen?

Competitive Advantage

Optimization
What will happen next?
Predictive Modeling
What if these trends continue?
Forecasting/extrapolation

Tactical /
Strategic BI

Why is this happening?
Statistical analysis
Alerts

What actions are needed?

Query/drill down
Ad hoc reports
Standard reports

Where exactly is the problem?

Operational BI

How many, how often, where?
What happened?

Sophistication of Intelligence
© 2008 Accenture. All Rights Reserved.


How Important is BI?
Top 10 Business and Technology Priorities for 2011:
1. Cloud computing
2. Virtualization
3. Mobile technologies
4. IT Management
5. Business Intelligence
6. Networking, voice and data communications
7. Enterprise applications
8. Collaboration technologies
9. Infrastructure
10. Web 2.0
Source: Gartner’s 2011 CIO Agenda (aka “

The July 2010 Forrester report “Technology
Trends That Retail CIOs Must Tap to Drive
Growth” identified the following technologies
that retail CIOs should be considering as
part of an overall architecture strategy:
Mobile

Social Computing

Cloud

Supply Chain

Micropayments
Business Intelligence/Analytics


Why is Business Intelligence So Important?
Time
Data

Opinion
(aka Best Professional
Judgment)
Making Business
Decisions is a Balance

In the absence of data, business decisions are often made by the HiPPO.

With Business Intelligence, we can get data to you in a timely manner.


Benefits of Business Intelligence
•

Improve Management Processes
–

•

Improve Operational Processes
–

•

planning, controlling, measuring and/or changing resulting in
increased revenues and reduced costs

fraud detection, order processing, purchasing.. resulting in
increased revenues and reduced costs

Predict the Future


Examples
- EMC
•
•


•
•

1998: Revenue $2.5b
1998: HW (90%) + SVCS (10%) + SW (0%)
Strategic BI: predictive modelling => decision
made
HW (10%) + SVCS (10%) + SW (80%)
2010: Revenue $16b


EMC Quarter Activity
Typical Activity by Week ($M) (Storage Products)

• Factories ship ≈40% of quarterly revenue in last
week!
• Build to Stock for orders in last two days!

700

$ Millions

600
500

Factory Shipments

400
300

Bookings

200
100
0
1

2

3

4

5

6

7

8

Fiscal Week

9

10

11

12

13


EMC Order Life Cycle

Suspect

Prospect

Lead

Account Planning

Oppty
Quota

Configure

Price

Quote

Order

Channel Integration

Produce

Ship

Invoice

Collect

Project
Commissions
Accounting

Service


Examples
- Walmart
•

Average daily sales of American Flags = 6,000


•

September 11th 2001


•

All competitors ran out of flags



•

Nearest rival sold 20,000


•

Walmart sold 116,000 flags on that day alone


Further examples
•

Call centres
–

e.g. Top Agent awarded bonus -> competition leading to performance improvements


•

Banks
–

jettison walk in customers to encourage online only


•

Criminal Minds

–

•

Information gathered on previous actions of serial killers allows the team to predict the actions of future serial killers

Revenue Service
–

who has the yacht but cannot afford it

•

Plagiarism detection in colleges

•

Customer Loyalty Programs

•

Twitter analysis for public mood

•

Dell

•

Healthcare

–

predicting infection in rural parts of third world


BI Golden Rules
•

Data Quality & Accuracy

•

Data Consistency

•

Data Timeliness



“Get the right information to the right people at
the right time”


Gartner BI Maturity Model


Major BI Trends


Mobile



Cloud



Social Media



Advanced Analytics


TDWI Executive Summit – August 2010
What BI technologies will be the most
important to your organization in the next 3
years?
Predictive Analytics
2. Visualization/Dashboards
3. Master Data Management
4. The Cloud
5. Analytic Databases
6. Mobile BI
7. Open Source
8. Text Analytics
1.

Advanced Analytics / Predictive Analytics
Data Mining
 Regression
 Monte Carlo Simulation
 “Statistically Significant”
 Predicting Customer Behavior


 Churn/Attrition
 Purchases
 Profiling


BI Today vs Tomorrow


“BI today is like reading the newspaper”
 BI

reporting tool on top of a data warehouse
that loads nightly and produces historical
reporting



BI tomorrow will focus more on real-time
events and predicting tomorrow’s
headlines

Collegiate Admissions Criteria










Test Scores: SAT, ACT, AP Exams
Grade Point Average
Class Rank
High School “Strength”
Extracurricular Activities: Band/Choir, Clubs, Sports
Non-School Activities: Work, Volunteer, Community Groups
Area of Focus – Intended Major
Family legacy
Home State or Country

Regression Outcome = Graduation (binary) + GPA (linear)


Retail Analytics
Market Basket Analytics
 Text Analytics
 Customer Segmentation/Clustering
 Tailored Product Assortments
 Inventory Forecasting




Amazon.com and NetFlix
Collaborative Filtering tries to predict other items a
customer may want to purchase based on what’s in their
shopping cart and the purchasing behaviors of other
customers

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