Enhancing Management Decision Making

Week-10

Prof. Bharat Bhasker

DECISION SUPPORT SYSTEMS (DSS)

MANAGEMENT LEVEL COMPUTER SYSTEM

COMBINES DATA, MODELS, USER–FRIENDLY SOFTWARE FOR SEMISTRUCTURED & UNSTRUCTURED DECISION MAKING

EXAMPLES OF DSS SYSTEMS

• Logistics and Transportation– Home Depot, Frito Lay, Trucking Industry

• Air Line Reservation, Crew Scheduling, Manpower Planning– American Airlines (Sabre), Swiss Air

• Financial Portfolio Management

• Production and Supply Chain Management

Characteristics of DSS

• BRIEFING BOOKS: On-Line Data, Fixed Format Reports for Executives

• DRILL DOWN: Ability to move from Summary to Lower Levels of Detail

• Designed for Specific Needs of CEO

• Extensive Support Staff

• Executive has 24 Hour per Day Ability to Examine, Control Progress Throughout Organization

Introduction- Business Problem DefinitionBusiness Application domain of Decision Support

• Customer Retention- modeling those who defected, to identify patterns that led to their defection. Apply it to present set and devise prevention.

• Sales & Customer Service - By aggregating the proper information to frontline sales and service professionals. Based on customer profile, rule based recommendation of products, service plans. Cross-selling

• Marketing- Accurate information for retention campaigns, lifetime value analysis, trending, targeted promotions

• Risk assessment and fraud detection- Accessible customer base reduces the risk. A mail-order company can identify payment patterns from a mailing address. Insurance company can aggregate all drawn out policies, thus total exposure. A bank can identify risky companies

Introduction- Business Problem Definition

Class of Problems

• Retrospective Analysis - Analysis of past and present events. Analysis of zone wise, product wise sales

• Predictive Analysis- Predicting certain events or behavior based on historical data. Attrition rate of customers due to competition

Introduction- Business Problem ClassificationsApplication Techniques for Business Problems • Classification- based on predefined criteria, examples credit ratings of

individuals

• Clustering and Segmentation - Segment a database in subsets or clusters based on a set of attributes. In the process of understanding customer base, an organization may segment the known population. Thus, discover hitherto unknown attributes (school attended, # of vacations per year). Clustering may utilize statistical or AI techniques

• Associations- These techniques identify affinity among the collection reflected in the examined records. These affinities are often referred to as rules. For example, 60% of all record that contain A & B also contain C & D. Product affinity in Market basket analysis

• Sequencing- Identifies patterns over time,. For example a analysis of customer visits may reveal that a person who buys engine oil & filter in one visit, buys gas additive in next visit.

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Introduction- Business Problem ClassificationsThe organizations are faced with a wealth of data stored in archives,. It is the inability to discover, often previously unknown, information hidden in data prevents them from deriving the knowledge and value for the organization. The objective, therefore, should be to extract valid, previously unknown, and comprehensible knowledge/information from large database and use it for profit (Competitive advantage). To meet these objectives-

– Capture and integrate both external and internal data into a comprehensive view that encompasses whole organization

– Organize the present data and extracted information in ways that expedites the decision making

– Mine the integrated data for information

Introduction- Operational Data StoresFor variety of On Line Transaction Processing (OLTP) systems (Financial, Order processing, Point of sale applications) the organizations maintain an operational data store. For example, bank withdrawals and deposits.

– Organized by application

– Daily business processing on a detailed transactional level

– Update intensive

– Current data

– Optimized for high performance

– Access is few records per transaction, often on primary key

– Large number of short duration transactions

– Large number of concurrent applications

Introduction- Informational Data StoresNeed to organized around subjects such as customers, vendors, products. Focuses on answering questions like “ What two products resulted in most frequent calls on hotline?”. Tend to have redundant and non-updateable. Characteristics exhibited include:

– Data Model- to meet End-use analysis needs. No focus on ACID

– Data Access- Ad hoc queries

– Time base- Recent, Aggregated, derived and historical data

– Data changes- Periodic, scheduled batch updates.

– Unit of Work- Queries rather than concurrent updates

– Record range accessed- Millions for a query vs. few for operational

– Number of Concurrent users- Low for IDS vs. High for ODS.

– Transaction Volumes- Low for IDS vs. High for ODS.

– Type of users- Analytical & Managerial

– Number of Indexes- Often many complex, compound vs. few, simple.

Introduction- Managing Data for DSData Warehouse

Common characteristics:

– Database designed to meet analytical tasks comprising of data from multiple applications

– Small number of users with intense and long interactions

– Read intensive usage

– Periodic updates to the contents

– Consists of current as well as historical data

– Relatively fewer but large tables

– Queries results is large results sets, involving full table scan and joins spanning several tables

– Aggregation, vector operation and summarization are common

– The data frequently resides in external heterogeneous sources

What is a Data Warehouse?A Practitioners Viewpoint

“A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.”

-- Barry Devlin, IBM Consultant

Introduction- TerminologyCurrent Detail Data- data acquired directly from operational databases, often representing entire enterprise

Old Detail Data- Aged current detail data, historical data organized by subjects, it helps in trend analysis

Data Marts- A large data store for informational needs where scope is limited to a department, SBUs etc., In a phased implementation data marts are a way to build a warehouse.

Summarized Data- Aggregated data along the lines required for executive reporting,trend analysis and decision support.

Drill Down- Ability of knowledge worker to perform business analysis in a top down fashion,starting from the highly summarized data to the current and old detail data.

Metadata- It is data about the data, description of contents, location, structure, end-user views, identification of authoritative data, history of updates, security authorizations

Introduction- Architecture

Extract, Cleanup & Load

External

Currentl

Current

Repository

Meta data

Realizedor Virtual

MDDB

Management Information Delivery System

Report, Query & EIs

OLAP Tools

Data Mining Tools