Mata Kuliah : CSM 211 , Management Support SystemTahun Akademik : 2012/2013

BUSINESS INTELLIGENT :DATA WAREHOUSE , DATA

AQUITITION , DATA MINING , BUSINESS ANALYTICS DAN VISUALIZATION

Pertemuan-5

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Sasaran Pembelajaran• Describe issues in data collection, problems and quality• Describe the characteristic and organization of database

management systems• Explain the importance and use of a data warehouse and datamart• Describe business intelligence/business analytics and their

importance to organizations• Describe how online analytical processing (OLAP), data mining,

data visualization, multidimensionality, and real-time analytics can improve decision making

• Explain how the web impacts database technologies and methods and vice versa

• Describe how database technologies and methods as part of business intelligence/business analytics improve decision making

• Describe web intelligence/web analytics and their importance to organizations

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Hasil Pembelajaran

The student can shows relationship between Database Management Systems (DBMS) to Decision Support System (DSS)

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Data, Information, Knowledge• Data– Items about thins, eventy, activities, and transaction

are recorded, classified, and stored but not organized to convey any specific meaning

– Source from internal or external• Information– Data that have been organized in a manner that gives

meaning for the recipient• Knowledge– Consists of data items, information organized and

process to convety understanding, experience, accumulated learning that are applicable to current problem / activities

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Database Management System (DBMS) in Decision Support System/Business

Intelligence1. Software to management database2. Operating in operation systems to

management data (create, update, delete, etc)

3. Need Query and management reporting4. Data Security5. Need programming language to

construction Decision Support Systems5-5

Database Organization and Structures

• Hierarchical Database– Top down, like inverted tree– Fields have only one “parent”, each “parent”

can have multiple “children”– Fast

• Network Database– Relationships created through linked lists,

using pointers– “Children” can have multiple “parents”– Greater flexibility, substantial overhead

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Database Organization and Structures

• Relational Database– Flat, two-dimensional tables with multiple

access queries– Examines relations between multiple tables– Flexible, quick, and extendable with data

independence

• Object Oriented Database– Data analyzed at conceptual level– Inheritance, abstraction, encapsulation

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Database Organization and Structures

• Multimedia Based Database– Multimedia Database Management System

(MMDBMS) manage data in variety of format

• Document Based Database– Electronic Document Management (EDM) systems

• Intelligent Database– Artificial Intelligence (AI) technologies, Web based

intelligent agent and Artifical Neural Network (ANN), simplify access to manipulation of complex database

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Database Structures

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DATA WAREHOUSE

• Needed support DSS to analysis the big data from other resource by quick result and supporting critical processes

• Data warehouse Aspect :- Characterics data warehouse- Arsitecture data - Data warehouse development

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Characterics of Data Warehousing

1. Subject Oriented2. Integrated3. Time Variant (time series)4. Nonvolatile5. Summarized6. Non Normalized7. Sources8. Metadata

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ARCHITECTURE OF A 3TIER DATA WAREHOUSE

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ARCHITECTURE OF A 2 TIER DATA WAREHOUSE

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Data Warehouse DevelopmentThe process of migrating data to data warehouse involves :The extraction of data from all relevant

sourcesData sources may consist of files extracted

from OLTP databases, spreadsheets, personal database (microsoft access), external files

All of the input file are written to a set of staging table which are designed to facilitate the load process

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DATA MARTS• Data marts is a subset of the data warehouse, typically

consisting of a single subject area (example : Marketing, Operations, Finance).

• Type of Data marts :– Dependent (replicated) :

• Created from warehouse• Replicated • Functional subset of warehouse

– Independent :• Scaled down, less expensive version of data warehouse• Designed for a department or SBU (Sub Business Unit)• Organization may have multiple data marts• Difficult to integrated

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ADVANTAGES OF DATA MARTS• The cost if low in comparison to an enterprise data

warehouse• The lack time for implementation is significantly

shortes• Controlled locally rather centerly• Less information than the data warehouse• Allow a business unit to build its own decision

support systems without relying on a centralized IS Department

• An independent data mart can serve as a proof of concept prior to investing the resources needed to develop a comprehensive enterprise data warehouse

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DATA MINING• Data mining (DM) is a term used to describe knowledge

discovery in databases. • DM is a process that uses statistical, mathematical,

artificial intelligence and machine learning techiques to extract and identify useful information and subsequent knowledge from large databases.

• Type methods to identify patterns in data : – Simple models (SQL based query, OLAP, Human

judgement)– Intermediate models (regression, decision tree,

clustering)– Complex Models (neural networks, other rule

induction) 5-17

DATA MINING TOOLS AND TECHNIQUES

• Statistical MethodsLinear an non linear regression, point estimation

• Decision TreesUsed to classification and clustering methods

• Case Base ReasoningUsing historical case

• Neural ComputingUtilize many connected noteds

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DATA MINING TOOLS AND TECHNIQUES

• Intelligent agentsapproaches to retrieving information from databases, especially external ones is the use of intelligent agents

• Genetic AlgorithmsWork on the principle of expansion of possible outcomes

• Other ToolsSeveral other tools can be used

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Data Visualization• Data visualization is easier to implement when

the necessary data are in a data warehouse or better yet in a multidimensional server

• Technology that supporting data visualization and interpretation of :– Digital imaging, GIS, GUI, tables,

multidimensions, graphs, VR, 3D, animation– Identify relationships and trends

• Data Manipulation can get with good performance

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Multidimensionality• Summary data can be organized in different

ways for analysis and presentation. An efficient way to do this called Multi Dimensionality

• Data can organizated with standard, not analysis• The Factors considered In multidimensionality :– Dimensions (product, salespeople ,market,

business units)–Measures (sales volume, money, inventory,

profit, forecasting vs actual)– Time(daily, weekly, monthly, quarterly, yearly)

• Significant overhead and storage5-21

Real-Time Analytics The User need specialized methods to store

information in many format with :– Query analysis Real-time– Decision making Real-time– Update data warehouses Real-time• Updates may be made while queries are

active• Not all data updated continuously

– Consolidated in business application analytics

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GEOGRAPHIC INFORMATION SYSTEMS (GIS)

• GIS is a computer based system for capturing, storing, checking, integrating, manipulating, and displaying data with digitized maps :– Orientation geographic– Maps model geographic– Needs a software web access to maps– Using with modeling and simulation

• GIS is used as a geographic spreadsheet that allows manager to model business activities and performance what if analysis

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GIS APPLICATION

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Web Analytics/Intelligence• Web Analytics and web intelligence are the terms

used to describe the application of business analytics / business intelligence of web sites

• Web Analytics– Business application analytics to activities

operations of web sites• Web Intelligence– Business application intelligence analytics to

activities operations of web sites

========= thanks 4 your attention =========5-25