Management Information System Session 12 th Dated: -23-05-2010 BY: - Neeraj Gupta Figure 1.3 Several subsystems make up this corporate accounting system.

Management Information System

Session 12th Dated: -23-05-2010BY: - Neeraj Gupta

Figure 1.3 Several subsystems make up this corporate accounting system.

Intelligence• Intelligence is the aptitude to learn, comprehend, or to counter new or trying situations• It is the skillful use of reason and the capacity to apply knowledge to influence one's environment or to think conceptually• Business intelligence is a set of notions, methods, and practices, which improves business decisions. It uses information from multiple sources and applies experience and assumptions that helps in understanding accurately the intricacies of business dynamics.

• Business Intelligence (coined by Gartner in

the late 1980s) is “a user-centered process

that includes accessing and exploring

information, analyzing this information, and

developing insights and understanding, which

leads to improved and informed decision

making.”

Business Intelligence


contd• BI is the means by which organizations interpret the sea of organizational data to derive insights that are critical to competing in the new economy• BI aids in: - a deeper understanding of customer and partner relationships - indicating key performance indicators - a consistent view of the organization from the executive level to the front line • By translating these insights into action companies can: - increase profits - respond more quickly to changing market demands - improve accountability by giving every employee an accurate view of the organization


The major thrust of business intelligence theory looks at certain factors to make high quality decisions. These factors include: - Customers Competitors Business partners Economic environment Internal operations.

Knowledge Management

Knowledge is information, extracted, filtered or formatted in some way. Every company's knowledge requirements are a unique combination of knowledge strategy, tools and technologies, processes and procedures.

Knowledge management technologies capture this intangible element in an organization and make it universally available. This approach has come to be known as knowledge management: the practice of capturing and organizing information to make it more accessible and valuable to those who need it.

Management Information Systems, Sixth Edition 7

Data Mining Data mining: the process of selecting, exploring, and

modeling large amounts of data Used to discover relationships that can support decision

making Data-mining tools may use complex statistical analysis

applications Data-mining queries are more complex than traditional

queries Combination of data-warehousing techniques and data-mining

tools facilitates the prediction of future outcomes

Data Mining Knowledge discovery as a process and consists of an iterative

sequence of the following steps: Data cleaning (to remove noise and inconsistent data) Data integration (where multiple data sources may be combined) Data selection (where data relevant to the analysis task are

retrieved from the database) Data transformation (where data are transformed or consolidated

into forms appropriate for mining by performing summary or aggregation operations, for instance)

Data mining (an essential process where intelligent methods are applied in order to extract data patterns)

Pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures)

Knowledge presentation (where visualisation and knowledge representation techniques are used to present the mined knowledge to the user).

The first four steps are different forms of data preprocessing, which are used for data preparation for mining. After this the datamining step may interact with the user or a knowledge base.


Data Mining (continued) Data mining has four main objectives:

Sequence or path analysis: finding patterns where one event leads to another

Classification: finding whether certain facts fall into predefined groups

Clustering: finding groups of related facts not previously known

Forecasting: discovering patterns that can lead to reasonable predictions


Data Mining (continued) Data mining techniques are applied to various fields,

including marketing, fraud detection, and targeted marketing to individuals

Predicting customer behavior: Banking: help find profitable customers, detect patterns

of fraud, and predict bankruptcies Mobile phone services vendors: help determine factors

that affect customer loyalty Customer loyalty programs ensure a steady flow of

customer data into data warehouses


Data Mining

Data Mining Architecture

Data Mining Architecture

Information repository: Data cleaning and data integration techniques may be performed on the data.

Database or data warehouse server: The database or data warehouse server is responsible for fetching the relevant data, based on the user's data mining request.

Knowledge base: This is the domain knowledge that is used to guide the search or evaluate the interestingness of resulting patterns.

Data mining engine: Ideally consists of a set of functional modules for tasks such as characterisation, association and correlation analysis, classification, prediction, cluster analysis, outlier analysis, and evolution analysis.

Pattern evaluation module: This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search toward interesting patterns.

User interface: This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results.

How Data Mining Works

Data mining is a component of a wider process called "knowledge discovery from database". Before a data set can be mined, it first has to be "cleaned". This cleaning process removes errors, ensures consistency

and takes missing values into account. Next, computer algorithms are used to "mine" the clean

data looking for unusual patterns. Finally, the patterns are interpreted to produce new

knowledge.


Data Mining (continued)

Many industries utilize loyalty programs Examples include frequent-flier programs and

consumer clubs These programs amass huge amounts of data

about customers UPS has a Customer Intelligence Group

Analyzes customer behavior Predicts customer defections so that a

salesperson can intervene to resolve problems

Data Mining (continued) Identifying profitable customer groups

Financial institutions dismiss high-risk customers Companies attempt to define narrow groups of

potentially profitable customers Utilizing loyalty programs

Amass huge amounts of data about customers Help companies perform yield management and

price-discrimination Example: Harrah’s charges higher per-night rates

to low-volume gamblers



Data Mining (continued)

Inferring demographics Predict what customers are likely to purchase in

the future Amazon.com

Determines a customer’s age range based on his or her purchase history

Attempts to determine customer’s gender Advertises for appropriate age groups based on the

inferred customer demographics Anticipates holidays


Online Analytical Processing Online analytical processing (OLAP): a type of application

used to exploit data warehouses Provides extremely fast response times Allows a user to view multiple combinations of two

dimensions by rotating virtual “cubes” of information This ability to organize the data in the way users think about it

is known as multidimensionality. Drilling down: the process of starting with broad information

and then retrieving more specific information as numbers or percentages

Can use relational or dimensional databases designed for OLAP applications

OLAP

When you plug in the various dimensions, the intersection of multiple dimensions produces a location called a cell. That cell contains the intersecting values within all the dimensions.

A cell is a single data point that occurs at the intersection defined by selecting one value from each dimension in a multidimensional array. In our example, we have time, product, geography and price as dimensions.

This means the dimensional members May 1996 (time), Maruti (product), and Mumbai (geography) specify a precise intersection along all the dimensions that uniquely identify a single cell. In this example, the cell contains the value of all Maruti sales in Mumbai for May 1996.


Characteristics of OLAP

The FASMI Test Fast: -Means that the system is targeted to deliver most

responses to users within about five seconds, with the simplest analysis taking no more than one second and very few taking more than 20 seconds.

Analysis: -Means that the system can cope with any business logic and statistical analysis that it relevant for the application and the user, the keep it easy enough for the target user. Although some pre-programming may be needed, we do not think it acceptable if all application definitions have to be done using a professional 4GL.


The FASMI Test Shared: -Means that the system implements all the

security requirements for confidentiality (possibly down to cell level) and , multiple write access is needed, concurrent update location at an appropriate level. Not all applications need users to write data back, but for the growing number that do, the system should be able to handle multiple updates in a timely, secure manner.

Multidimensional: -Is our key requirement. If we has to pick a one-word definition of OLAP this is it. The system must provide a multidimensional conceptual view of the data, including full support for support for hierarchies and multiple hierarchies.


The FASMI Test Information: -Is all of the data and derived

information needed, whether it is and however much is relevant for the application.


Online Analytical Processing (continued) OLAP is increasingly used by corporations to

gain efficiencies Office Depot used OLAP on a data warehouse to

determine cross-selling strategies Ben & Jerry’s tracks ice cream flavor popularity

BI software is becoming easier to use Intelligent interfaces accept queries in free form

BI software is integrated into Microsoft’s SQL Server database software

OLAP Data Modelling

Review the following list highlighting differences between OLAP and warehouse data: An OLAP system stores and uses much less data compared with

a data warehouse. Data in an OLAP system is summarized. The lowest level of

detail as in the data warehouse is very infrequent. Every instance of the OLAP system is customized for the

purpose that instance serves. In other words, OLAP tends to be more departmentalized, whereas data in the data warehouse serves corporate-wide needs.

Implementation Considerations: Before we specially focus on modeling for OLAP, let us go over a few implementation issues. An overriding principle is that OLAP data is generally customized. When you build an OLAP system with system interfaces serving different user groups, this is an important point.

OLAP Data Modelling The following techniques apply to the preparation of OLAP data

for a specific group of users or a particular department such as marketing. Define Subset: Select the subset of detailed data the marketing

department is interested in. Summarize: Summarize and prepare aggregate data structures

in the way the marketing department needs for combining. Denormalize: Combine relational tables in exactly the same way

the marketing department needs denormalized data. Calculate and Derive: If some calculations and derivations of

the metrics are department-specific, use the ones for marketing. Data Modeling for MOLAP: As a prerequisite to creation and

storage of hypercubes in proprietary MDDBs, data must be in the form of multidimensional representations. You need to consider special requirements of the selected MDDBMS for data input for creation.


Dashboards

Dashboard: an interface between BI tools and the user Resembles a car dashboard Contains visual images to quickly represent

specific business metrics of interest to management

Helps management monitor revenue and sales, monitor inventory levels, and pinpoint trends and changes over time

Features of an Executive Dashboard The executive dashboard (also called business dashboard, corporate

dash-board, and dashboard for executives) is a powerful software solution that can provide information that executives can use to get an accurate picture of the organization’s performance.

Executive dashboards are so called because of the obvious metaphor of the business as a vehicle. Executives (drivers) need an instrument panel to provide them with the information they need to 'drive' the business.

Modern digital dashboards/business dashboards are designed by many vendors to work 'out of the box', enabling managers to simply install them on their office desktop or home computer and configure them to track performance across a wide range of business processes.

Perhaps most importantly, executive dashboards are configured to display performance of high-level processes, allowing executives to get a simple, broad overview of the performance of specific areas of the business or (see also Key Performance Indicators).

Benefits of Executive Dashboards Faster Decision Making Identification of Negative Trends

Business Process Management Executive dashboards can highlight inefficiencies in

business processes that may not be obvious to men on the front lines. Strategy Alignment and Organizational Goal

Generation: -An overview of business performance can aid managers in the development of new business strategies. With a clear overview of the strengths and weaknesses of the enterprise it can be much easier and more effective to formulate ideas about the best way to achieve business objectives.

Remote Viewing: -Most executive dashboard software can connect with corporate information systems from remote locations via the Internet. This capability makes it easy for executives to keep up to date with business performance whether in or out of the office.

That’s all for Today!

Management Information System Session 12 th Dated: -23-05-2010 BY: - Neeraj Gupta Figure 1.3 Several subsystems make up this corporate accounting system.

Documents

data miningdata mining

data cleaning

data relevant

multiple data sources

business decisions

business intelligence

sea of organizational

data patternspattern