DST lecture 03_Multidimensional data analysis.pdf

7/28/2019 DST lecture 03_Multidimensional data analysis.pdf

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Module Outline

Management Decision-Making

Multi-Dimensional Data Analysis

Stair, Reynolds & Chesney 124-125 & 219-221

Group and Executive Support Systems

Model-based Decision Support Systems

Intelligent Systems

Knowledge Management

Managing Decision Support Tools

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Business Intelligence (BI)3


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What is Business Intelligence?4

Business Intelligence is

A broad category of applications and technologies forgathering, storing, analysing, and providing access to

data, to help enterprise users make better business

decisions.

BI applications include the activities of decision support

systems, query and reporting, online analytical

processing (OLAP), statistical analysis, forecasting, and

data mining.

Definition from TechTarget.com


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Relate BI to decision support5

Intelligence stage (of decision-making)

Becoming aware of the problem (or opportunity) Defining the problem details and scope

Design stage

Identifying alternative solutions Evaluating feasibility of each solution

Choice stage

Deciding on the best solution

Internal or external data? Actual or estimated values?


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Knowledge Discovery7


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Multi-dimensional data analysis8

OLAP and Data Mining

Explores the relationship between multiple variables

Usually at least three variables involved

Relies on large data sets Usually has a time component

Graphical display aids understanding

Identifies patterns occurring in data

Can provide a basis for developing mathematical

models


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On-Line Analytical Processing (OLAP)

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Initiated by users

Reveals relationships between data items

Detects trends

Clarifies problem definition

Easy to use

Visual interface

Flexible

Drill-down

NOT done on operational database


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Example of OLAP output10


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Slice and dice views11

Different people may have

different interests in the

same dataset


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OLAP vs standard DBMS queries12

Quick to compose, run and modify

No programming skills needed

Visual output is more user friendly

Key measures are already calculated

OLAP structure can be used to build models (e.g.

financial)

Can provide input to other applications (e.g.

performance management)


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Equivalent Excel functions13 Pivot tables

Pivot charts

Statistical analysis

Correlation

Multiple regression

Analysis of variance

Cluster analysis


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18 months & R10 million15 The business value of Supply Chain Intelligence enabled

by a Global Procurement Intelligence solution would bederived through: The ability to identify, measure, manage and report

procurement spend, price and consumption variances, trendsand cost pressures across the group as well as by providing

contract and vendor spend visibility; The ability to drive group cost optimisation through best

practice sourcing strategies;

Risk identification and mitigation capability;

Visibility and standardisation of consolidated spend andrelated policies; and

Data consolidation across the group, aligned to budgets andexpenditure forecasts.


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Moving on to Data Mining16

Automated process to extract information from

large data setsOnly as effective as the data it uses

Relies on advanced logical, statistical and

mathematical techniques

Infers rules and relationships that allow the

prediction of future results

But cant explain underlying reasons


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How is data mining being used in SA?

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CRM and direct marketing

Customer and product planning

Fraud detection

Credit scoring

Benefits include:

Customer attraction and retention

Product design Bad debt reduction

Bank robbery prediction

Product cross-selling


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Comparing OLAP and data mining18

Characteristic OLAP Data Mining

Purpose Supports data analysis and

decision making

Supports data analysis and

decision making

Type of analysissupported

Top-down, query-driven dataanalysis

Bottom-up, discovery-drivendata analysis

Skills required

of user

Must be very knowledgeable

about the data and its

business context

Must trust in data mining

tools to uncover valid and

worthwhile hypotheses


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Web mining

Web Content Mining

Extraction of information from documents and databases

Web Structure Mining

Link structures within the Internet, most frequently visited

paths etc

Web Usage Mining

Analyses data from the actions of Internet users (web

and proxy server logs, user sessions, user profiles,registration data, cookies, bookmarks, mouse clicks etc.)

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And thats all for today20

Documents

DST lecture 03_Multidimensional data analysis.pdf