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

Mata Kuliah : CSM 211 , Management Support System Tahun Akademik : 2012/2013 BUSINESS INTELLIGENT : DATA WAREHOUSE , DATA AQUITITION , DATA MINING , BUSINESS ANALYTICS DAN VISUALIZATION Pertemuan-5 5-1

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 5-2

Hasil Pembelajaran The student can shows relationship between Database Management Systems (DBMS) to Decision Support System (DSS) 5-3

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 5-4

Database Management System (DBMS) in Decision Support System/Business Intelligence Software to management database Operating in operation systems to management data (create, update, delete, etc) Need Query and management reporting Data Security Need programming language to construction Decision Support Systems 5-5

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 5-6

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 5-7

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 5-8

Database Structures 5-9

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 5-10

Characterics of Data Warehousing Subject Oriented Integrated Time Variant (time series) Nonvolatile Summarized Non Normalized Sources Metadata 5-11

ARCHITECTURE OF A 3TIER DATA WAREHOUSE 5-12

ARCHITECTURE OF A 2 TIER DATA WAREHOUSE 5-13

Data Warehouse Development The process of migrating data to data warehouse involves : The extraction of data from all relevant sources Data 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 5-14

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 5-15

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 5-16

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 Methods Linear an non linear regression, point estimation Decision Trees Used to classification and clustering methods Case Base Reasoning Using historical case Neural Computing Utilize many connected noteds 5-18

DATA MINING TOOLS AND TECHNIQUES Intelligent agents approaches to retrieving information from databases, especially external ones is the use of intelligent agents Genetic Algorithms Work on the principle of expansion of possible outcomes Other Tools Several other tools can be used 5-19

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 5-20

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 storage 5-21

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 5-22

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 5-23

GIS APPLICATION 5-24

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

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