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8/6/2019 Business Intelligence - Week04
http://slidepdf.com/reader/full/business-intelligence-week04 1/28
Business Intelligence
(0641611)Lecture Week-4
BI Using Data Warehousing
Beban: 2 SKS
SEMESTER: VI (Enam)/GenapDOSEN: Djadja Achmad Sardjana, S.T., M.M.
0818-658980 & 0858-61625868
5/26/2011 1Business Intellgence IF-UTama
8/6/2019 Business Intelligence - Week04
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Data Warehouse Topics� Decision Support Systems
± history� Requirements Gathering
± Where data located, owners,
definition, how often updated� Data Analysis
± Determine for table structures
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Data Warehouse
� ETL Processes & Deliverables
± Cleaning & Conforming
� Valid, missing� Address, gender
± Schemas
� Dimension Tables
� Fact Tables
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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
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Data Warehouses
ODS* ODS ODS
Data Warehouse
Multi-DimensionalDatabase (Cube)
*ODS = Operational Data Store
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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
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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)
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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
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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!!!!
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Data Mining
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Data Mining
� The process of identifying patterns in
data
� Goes beyond simple querying of the
database
� Goes beyond multi-dimensionaldatabase queries as well
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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.
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Data Mining Applications
� Fraud detection
� Targeted Marketing� Risk Management
� Business Analysis
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Origins of Data Mining
� Mathematics
±Statistics
±Numerical Analysis� Artificial Intelligence/Machine Learning
� Computer Science
±Data Storage andManipulation
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How does Data Mining
work?� Uses induction-based learning:
The process of forming generalconcept definitions by observing
specific examples of concepts to be
learned.
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How does Data Mining
work?
Which of these are What-Cha-Ma-Call-Its?
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Data Mining Process
List of Customers:
-some bicycle buyers
-some not
Data MiningSoftware Model
List of Prospective Buyers Model
List of Likely Buyers
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Overview of Mining
Strategies
Note: This representation is over-simplified and data mining strategiesare continually being invented.
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Skills� Written communication
� Problem Solving
± Analytical
± Troubleshooting� Software
± Microsoft SQL Server ManagementStudio
± SQL Server BI Development Studio ± SQL Server Reporting Services
± Pro Clarity
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Jobs
� Business Analyst
� Data Analyst
� Functional Analyst� Marketing Analyst
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Jobs
� Report Developer
� Data Modeler
� ETL Developer
� Data Architect
� Data Warehouse Designer
� Data Warehouse Developer
� Data Warehouse Administrator
� Database Administrator
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Jobs
� Business Intelligence Consultant
� Business Intelligence Developer
� Business Intelligence Analyst
� Business Intelligence Project Team Member
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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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SekianSekian dandan
TerimaTerima KasihKasihSampai berjumpa
di kuliah minggu depan
5/26/2011 28Business Intellgence IF-UTama
History of Business
Intelligence-10m36