MANAGEMENT INFORMATION
SYSTEM
Enhancing Decision
Making

Scenario
 Problem: Chain retailers need to determine
what products will sell at what prices at
different locations
 Solutions: Business analytics software to
analyze patterns in sales data, create pricing
profiles and buyer profiles for different regions,
locales, even times of day

Business value of enhanced
decision making
 Senior managers:
– Make many unstructured decisions
– E.g. Should we enter a new market?
 Middle managers:
– Make more structured decisions but these may include
unstructured components
– E.g. Why is order fulfillment report showing decline in Lahore?
 Operational managers, rank and file employees
– Make more structured decisions
– E.g. Does customer meet criteria for credit?

 Business value of improved decision making
– Improving hundreds of thousands of “small” decisions
adds up to large annual value for the business
 Types of decisions:
– Unstructured: Decision maker must provide judgment,
evaluation, and insight to solve problem
– Structured: Repetitive and routine; involve definite
procedure for handling so they do not have to be treated
each time as new
– Semistructured: Only part of problem has clear-cut
answer provided by accepted procedure

 Senior managers, middle managers, operational managers, and
employees have different types of decisions and information
requirements.

4 stages of decision making
process
1. Intelligence
 Discovering, identifying, and understanding the problems
occurring in the organization
1. Design
 Identifying and exploring solutions to the problem
1. Choice
 Choosing among solution alternatives
1. Implementation
 Making chosen alternative work and continuing to monitor
how well solution is working

 Three main reasons why investments in
information technology do not always produce
positive results
1. Information quality
 High-quality decisions require high-quality information
1. Management filters
 Managers have selective attention and have variety of biases
that reject information that does not conform to prior
conceptions
1. Organizational politics
 Strong forces within organizations resist making decisions
calling for major change

Business Intelligence
 Business intelligence
– Infrastructure for collecting, storing, analyzing data
produced by business
– Databases, data warehouses, data marts
 Business analytics
– Tools and techniques for analyzing data
– OLAP*, statistics, models, data mining
 Business intelligence vendors
– Create business intelligence and analytics purchased by
firms
* Online Analytical Processing

 Business intelligence and analytics requires a strong database
foundation, a set of analytic tools, and an involved management team
that can ask intelligent questions and analyze data.

 Six elements in the business intelligence
environment
1. Data from the business environment
 Businesses must deal with both structured and unstructured
data from many different sources, including big data.
 The data need to be integrated and organized so that they can
be analyzed and used by human decision makers.
1. Business intelligence infrastructure
 The underlying foundation of business intelligence is a
powerful database system that captures all the relevant data
to operate the business.

3. Business analytics toolset
 A set of software tools are used to analyze data and produce
reports, respond to questions posed by managers, and track
the progress of the business using key indicators of
performance.
4. Managerial users and methods
 Business intelligence hardware and software are only as
intelligent as the human beings who use them.
 Without strong senior management oversight, business
analytics can produce a great deal of information, reports, and
online screens that focus on the wrong matters and divert
attention from the real issues.

5. Delivery platform – MIS, DSS, ESS
 The results from business intelligence and analytics are
delivered to managers and employees in a variety of ways,
depending on what they need to know to perform their
jobs.
5. User interface
 Business people are no longer tied to their desks and
desktops. They often learn quicker from a visual
representation of data than from a dry report with columns
and rows of information.

 Today’s business analytics software suites emphasize
visual techniques such as dashboards and scorecards.
They also are able to deliver reports on BlackBerrys,
iPhones, and other mobile handhelds as well as on the
firm’s Web portal.
 Software is adding capabilities to post information on
Twitter, Facebook, or internal social media to support
decision making in an online group setting rather than in a
face-to-face meeting.

 Business intelligence and analytics capabilities
– Goal is to deliver accurate real-time information to
decision-makers
– Main analytic functionalities of BI systems
1. Production reports
– These are predefined reports based on industry
specific requirements (examples next slide)
2. Parameterized report
– Using filters/conditions to complete report processing

3. Dashboards/scorecards
– These are visual tools for presenting performance data
defined by users.
4. Ad hoc query/search/report creation
– These allow users to create their own reports based on
queries and searches.
5. Drill down
– This is the ability to move from a high-level summary to a
more detailed view.
6. Forecasts, scenarios, models
– These include the ability to perform linear forecasting,
what-if scenario analysis, and analyse data using
standard statistical tools.

BI users
 Casual users are consumers of BI output, while intense power users
are the producers of reports, new analyses, models, and forecasts.

 Over 80 percent of the audience for BI consists of
casual users who rely largely on production reports.
 Senior executives tend to use BI to monitor firm
activities using visual interfaces like dashboards and
scorecards.
 Middle managers and analysts are much more likely to
be immersed in the data and software, entering queries
and slicing and dicingthe data along different
dimensions.
 Operational employees will, along with customers and
suppliers, be looking mostly at prepackaged reports.

 Examples of BI applications
– Predictive analytics
 Use patterns in data to predict future behavior
 E.g. Credit card companies use predictive analytics to
determine customers at risk for leaving
– Data visualization
 Help users see patterns and relationships that would be
difficult to see in text lists
– Geographic information systems (GIS)
 Ties location-related data to maps

Questions??