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Matakuliah
Tahun
Versi
: J0324/Sistem e-Bisnis
: 2005
: 02/02
Pertemuan 26
Understanding Business
Intelligence
1
Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
• memilih strategi penerapan inteligence
bisnis pada/bagi suatu institusi
2
Outline Materi
• Evolution of Knowledge Management
Applications
• Elements of Business Intelligence
Applications
• Business Intelligence in the Real World
• Core Technologies: Data Warehousing
3
Three-Layer Business Intelligence Solutions
Architecture
4
Enabling Technologies. Online Analytical
Processing (OLAP)
• OLAP, a key component of BI, is increasingly used to
improve business analysis. Historically, OLAP has been
characterized by expensive tools, difficult
implementation, and inflexible deployment. However,
with new innovations, OLAP is seeing more widespread
use in a wide array of applications, ranging from
corporate reporting to advanced decision support.
• OLAP solutions provide a means to analyze complex
data by using a more intuitive set of business rules and
dimensions, such as profitability analysis by product,
channel, geography, customer, or fiscal period. In
addition, by insulating the user from the technical
aspects of data storage and data structures, OLAP
solutions enable less technically sophisticated users
within an organization to perform their own analyses.
5
Next…..
• Typically, OLAP solutions provide complex
computational capabilities, including time-series analysis
and ad hoc, drill-down, and interactive analysis. For
example, a marketing manager identifying a marketshare reduction can drill down to isolate the problem to a
specific product at a specific store.
6
The Data Warehousing Process
7
Data Warehouse Components
• Transactional applications to ensure that source data
can be stored in any format, from modern relational
databases to traditional legacy sources
• Extraction and transformation tools that read data from
transactional systems, make the data consistent, and
write it to an intermediate file
• Scrubbing tools to further cleanse raw data
• Movement tools that move data from the intermediate
files to the data warehouse, while automatically
managing data volume and cross-platform issues
8
Next…..
• Repository tools that maintain the metadatainformation about the datain the warehouse and
monitor transactional applications so that if a data
record changes, the data extraction and
transformation tools are updated
• Access tools-on the user's desktop-that retrieve,
view, manipulate, analyze, and present data:
spreadsheets, query engines, report writers, and
even Web browsers
• Data delivery for continuous customer access,
including instant messaging among all manner of
devices-browsers, e-mail, pagers, fax, Palm Pilot,
Windows CE, and wireless-regardless of the
communications medium
9
Blueprint of Knowledge Management
10
Steps to Guide Setting Up Knowledge
Framework
• Identify the goal of the BI project
• Determine where knowledge resides in the company
• Determine what information the company need to
capture
• Collect, clean, and prepare data
• Balance external and internal data
• Develop new approaches to categorizing information
• Build data model
• Deploy the model
• Monitor the model
• Measure the ROI
11
•
Source
: Kalakota, Ravi & Marcia
Robinson (2001). e-Business 2.0.
Roadmap for Success. Addison-Wesley.
PPT for Chapter : 11
12