Business Intelligence and
Decision Support Systems
Ref: Chapter 12
Turban and Volonino
Identify factors influencing adoption of
business intelligence (BI) and business
performance management (BPM).
Describe data mining, predictive analytics,
digital dashboards, scorecards, and
multidimensional data analysis.
Identify key considerations for IT
Understand managerial decision making
processes, the decision process, and types of
5. Describe decision support systems
(DSSs), benefits, and structure.
Recognize the importance of real
and decision support for various levels of
Be familiar with automated decision
support, its advantages, and areas of
Saturation of existing market.
wireless capabilities to provide
managers with data that are analyzed
immediately to provide actionable
feedback to maximize sales.
gained decisive edge &
outsmarted its rivals. Data used as
Skills, processes, technologies, applications
and practices used to support decision
provide historical, current, and predictive
views of business operations.
Common functions of Business Intelligence
technologies are reporting,
(E)xtract (T)ransform (L)oad Tools
involves tools for extracting the data
from source systems (silos).
involves converting (transforming) the
data into standardized formats.
involves loading & integrating data
into a system (such as a data warehouse).
Risks and Issues
Responsiveness requires intelligence which
requires trusted data & reporting systems.
Silos arise creating decisions based upon
inaccurate, incomplete, possibly outdated
* Data that are too late
* Data that are wrong level of detail
too much or too
* Unable to coordinate with departments across
* Unable to share dat
a in a timely manner
Business Intelligence Technologies
1990s primarily associated with back office
workers & operations such as accounting,
finance & human resources.
2000s expanded to enterprise data to
include needs of managers & executives.
Vendors offered advanced analytic, decision
use interfaces, & improved
data visualization tools. Web
Evolved from reporting to predicting.
Power of Predictive Analytics, Alerts &
analyze current and
historical facts to make predictions about
time view of the data
Reactive to proactive with respect to future
Improved data quality
Shared, common vision of business activity
benefitting key decision makers across enterprise
Simple to view KPIs
Informed, fast decision making
Complete, comprehensive audit trails
Top five business pressure driving the
adoption of predictive analytics
Business Intelligence Solutions
A BI System:
Must be able to access enterprise data
sources such as TPS, e
business & e
commerce processes, operational platforms &
Needed for real
time decision making.
Enhanced operational understanding
Improved cost control & customer
BI Architecture Component 1
Extraction & Integration
Many sources such as OLAP, ERP, CRM,
SCM, legacy & local data stores, the Web
all lacking standardization & consistency.
provide data for analyses to support
Central data repository with data security
& administrative tools for information
BI Architecture Component 2
Enterprise Reporting Systems
Provide standard, ad hoc, or custom
95% of Fortune 500 rely on BI to access
information & reports they need.
Reduces data latency.
Decreases time users must spend
collecting the data; increases time spent
on analyzing data for better decision
Dashboards & Scorecards
Dashboards are typically operation &
tactical in application & use.
Scorecard users are executive, manager,
staff strategic level in application & use.
Multidimensional view of sales revenue data
BI Architecture Component 3
Business Performance Management
Requires methods to quickly & easily
determine performance versus goals,
objectives & alignment strategies.
Relies on BI analysis reporting, queries,
dashboards & scorecards.
Objective is strategic
overall performance of an organization.
Content that is mined include unstructured
data from documents, text from email
messages & log data from Internet browsing.
May be major source of competitive
Needs to be codified with XML & extracted
to apply predictive data mining tools to
generate real value.
Comprises up to 80% of all information
Advantages & Disadvantages of Data
Tools that are interactive, visual,
understandable, & work directly on data
warehouse of organization.
Simpler tools used by front line workers
for immediate & long
Techniques may be too sophisticated or
require extensive knowledge & training.
May require expert statistician to utilize
effectively, if at all.
Managers and the
Decision Making Process
Managers Need IT Support from DSS
Scenarios, alternatives & risks are many.
Time is always critical consideration &
stress level is high.
Require sophisticated analysis.
Geographically dispersed decision makers
with collaboration required.
Often requires reliable forecasting.
Automating Manager’s Job
Routine decisions by mid
(frontline employees) may be automated
fairly easily & frequently.
Automation of routine decisions leaves
more time for supervising, training &
Top level managerial decision making is
seldom routine & very difficult to
IT Available to Support Managers
communication & collaboration with web
provide support primarily to
analytical, quantitative types of decisions.
informational roles of executives.
supports managers & staff
working in groups, remotely or closely.
IT support for Decision Making
Phases in the decision
Decision Modeling & Models
representation, or abstraction of reality.
Simplicity is key.
Based upon set of assumptions.
Requires monitoring & adjustment
periodically as assumptions change.
virtual experiments reduce
cost, compress time, manipulate variables,
Framework for Computerized
routine & repetitive
lots of uncertainty, no
definitive or clear
between the extremes.
Most true DSS are focused here.
DSS & Managers
Need new & accurate information.
Time is critical.
Complex organization for tracking.
Existing systems could not support
Characteristics & Capabilities
Sensitivity analysis for “what if” & goal
seeking strategy setting. Increases system
flexibility & usefulness.
database, model base,
user interface, users & knowledge base.
ADS (Automated Decision Support)
based systems with automatic
solutions to repetitive managerial
Closely related to business analytics.
Automating the decision
is usually achieved by capturing manager’s
Rules may be part of expert systems or
other intelligent systems.
Characteristics & Benefits of ADS
Rapidly builds business rules to automate
or guide decision makers, & deploys them
into almost any operating environment.
Injects predictive analytics into rule
applications, increasing their power &
Combines business rules, predictive
models & optimization strategies flexibly
into enterprise applications.
Customizing products & services for
Revenue yield management
Uses filtering for handling & prioritizing
Why BI Projects
Failure to recognize as enterprise
Lack of sponsorship.
Lack of cooperation.
Lack of qualified & available staff.
No appreciation of negative impact on
Too much reliance on vendors.