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Business Intelligence

(0641611)Lecture Week-4

BI Using Data Warehousing

Beban: 2 SKS

SEMESTER: VI (Enam)/GenapDOSEN: Djadja Achmad Sardjana, S.T., M.M.

[email protected]

0818-658980 & 0858-61625868

5/26/2011 1Business Intellgence IF-UTama



Data Warehouse Topics� Decision Support Systems

± history� Requirements Gathering

± Where data located, owners,

definition, how often updated� Data Analysis

± Determine for table structures



Data Warehouse

� ETL Processes & Deliverables

± Cleaning & Conforming

� Valid, missing� Address, gender

± Schemas

� Dimension Tables

� Fact Tables



Data Consolidation & Storage

MRPCRMSCM Finance

Transaction

Layer

Shared Data

Layer Data Warehouse

Customers Sales Procurement Suppliers Operations Finance

Shared

Reporting

� Operations and financial information is sharedacross the organization from same core data



Data Warehouses

ODS* ODS ODS

Data Warehouse

Multi-DimensionalDatabase (Cube)

*ODS = Operational Data Store



How is data consolidated?

� This is difficult!!!!!

± Data is often spread across

multiple systems, stored in different

formats, and may even be localized

for different countries



Transforming Data� Data must be transformed for consistency

and meaning

± Transformations may be as simple as copying

columns or may be incredibly complex ± Common transformations include:

� Hard-coded changes (µT¶ to 1)

� Looking up values in a table (mapping a customer

number across disparate systems)

�Inserting dummy records and mapping them tounknowns (inserting an µUnknown¶ customer)



Cleansing Data� Data must be cleansed to be meaningful

± All companies have ³bad´ data in

their systems

± Data may be missing

± Data may be inconsistent

± Data may be wrong



Data Warehouses� ETL (extract, transform and load) processes are

needed to create data warehouses

± This is an arduous and technical

process that can account for alarge percentage of a BI project

cost!!!!



Data Mining



Data Mining

� The process of identifying patterns in

data

� Goes beyond simple querying of the

database

� Goes beyond multi-dimensionaldatabase queries as well



Data Mining

� Data Mining works for problems like:

± Develop a general profile for credit

card customers «

± Differentiate individuals who are

poor credit risks «

± Determine what characteristicsdifferentiate male & female

investors.



Data Mining Applications

� Fraud detection

� Targeted Marketing� Risk Management

� Business Analysis



Origins of Data Mining

� Mathematics

±Statistics

±Numerical Analysis� Artificial Intelligence/Machine Learning

� Computer Science

±Data Storage andManipulation



How does Data Mining

work?� Uses induction-based learning:

The process of forming generalconcept definitions by observing

specific examples of concepts to be

learned.



How does Data Mining

work?

Which of these are What-Cha-Ma-Call-Its?



Data Mining Process

List of Customers:

-some bicycle buyers

-some not

Data MiningSoftware Model

List of Prospective Buyers Model

List of Likely Buyers



Overview of Mining

Strategies

Note: This representation is over-simplified and data mining strategiesare continually being invented.



Skills� Written communication

� Problem Solving

± Analytical

± Troubleshooting� Software

± Microsoft SQL Server ManagementStudio

± SQL Server BI Development Studio ± SQL Server Reporting Services

± Pro Clarity



Jobs

� Business Analyst

� Data Analyst

� Functional Analyst� Marketing Analyst



Jobs

� Report Developer

� Data Modeler

� ETL Developer

� Data Architect

� Data Warehouse Designer

� Data Warehouse Developer

� Data Warehouse Administrator

� Database Administrator



Jobs

� Business Intelligence Consultant

� Business Intelligence Developer

� Business Intelligence Analyst

� Business Intelligence Project Team Member



Jobs

� One of the fastest growing segments of IT

� Less likely to be outsourced

� May exist in business units rather than IT

� Knowledge/understanding of the organization is key



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di kuliah minggu depan

5/26/2011 28Business Intellgence IF-UTama

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