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Data warehouse
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Data warehouse






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    Data warehouse Data warehouse Presentation Transcript

    • CONTENTS Database and Data Warehousing History of data warehousing Evolution in organization use of data warehouses Data Warehouse Architecture Benefits of data warehousing Strategic uses of data warehousing Disadvantages of data warehouses Data mart Data mining Data mining for decision support Text mining OLAP Data warehousing integration Business intelligence
    • Database and Data Ware Housing…. The Difference… DWH Constitute Entire Information Base For All Time.. Database Constitute Real Time Information… DWH Supports DM And Business Intelligence. Database Is Used To Running The Business DWH Is How To Run The Business
    • A producer wants to know….Which are ourlowest/highest margincustomers ?Who are my customersand what productsare they buying?What is the mosteffective distributionchannel?What product prom--otions have the biggestimpact on revenue?What impact willnew products/serviceshave on revenueand margins?Which customersare most likely to goto the competition ?
    • Data, Data everywhereyet ... I can’t find the data I need data is scattered over the network many versions, subtle differences I can’t get the data I need need an expert to get the data I can’t understand the data I found available data poorly documented I can’t use the data I found results are unexpected data needs to be transformed fromone form to other
    • What is a Data Warehouse?A single, complete andconsistent store of dataobtained from a variety ofdifferent sources madeavailable to end users in awhat they can understand anduse in a business context.
    • What is Data Warehousing?A process oftransforming datainto information andmaking it available tousers in a timelyenough manner tomake a differenceDataInformation
    • Data Warehousing -- a process It is a relational or multidimensional databasemanagement system designed to supportmanagement decision making. A data warehousing is a copy of transaction dataspecifically structured for querying and reporting. Technique for assembling and managing data fromvarious sources for the purpose of answeringbusiness questions. Thus making decisions thatwere not previous possible
    • Data warehousing is … Subject Oriented: Data that gives information about a particular subjectinstead of about a companys ongoing operations. Integrated: Data that is gathered into the data warehouse from a variety ofsources and merged into a coherent whole. Time-variant: All data in the data warehouse is identified with a particulartime period. Non-volatile: Data is stable in a data warehouse. More data is added but datais never removed. This enables management to gain a consistent picture of thebusiness. Data warehousing is combining data from multiple and usually varied sourcesinto one comprehensive and easily manipulated database. Common accessing systems of data warehousing include queries, analysis andreporting. Because data warehousing creates one database in the end, the number ofsources can be anything you want it to be, provided that the system can handlethe volume, of course. The final result, however, is homogeneous data, which can be more easilymanipulated.
    • History of data warehousing The concept of data warehousing dates back to the late 1980swhen IBM researchers Barry Devlin and Paul Murphydeveloped the "business data warehouse". 1960s - General Mills and Dartmouth College, in a jointresearch project, develop the terms dimensions and facts. 1970s - ACNielsen and IRI provide dimensional data marts forretail sales. 1983 – Tera data introduces a database management systemspecifically designed for decision support. 1988 - Barry Devlin and Paul Murphy publish the article Anarchitecture for a business and information systems in IBMSystems Journal where they introduce the term "business datawarehouse".
    • OLTPOLTP- ONLINE TRANSACTION PROCESSING Special data organization, access methods andimplementation methods are needed to support datawarehouse queries (typically multidimensionalqueries) OLTP systems are tuned for known transactions andworkloads while workload is not known a priori in adata warehouse e.g., average amount spent on phone calls between 9AM-5PM in Pune during the month of December
    • OLTP vs Data Warehouse OLTP Application Oriented Used to run business Detailed data Current up to date Isolated Data Clerical User Few Records accessed at a time(tens) Read/Update Access No data redundancy Database Size 100MB -100 GB Transaction throughput is theperformance metric Thousands of users Managed in entirety Warehouse (DSS) Subject Oriented Used to analyze business Summarized and refined Snapshot data Integrated Data Knowledge User (Manager) Large volumes accessed at a time (millions) Mostly Read (Batch Update) Redundancy present Database Size 100 GB - few terabytes Query throughput is the performance metric Hundreds of users Managed by subsets
    • To summarize ... OLTP Systems areused to “run” a business The Data Warehouse helpsto “optimize” the business
    • Evolution in organizational use of data warehousesOrganizations generally start off with relatively simple use of datawarehousing. Over time, more sophisticated use of data warehousingevolves. The following general stages of use of the data warehouse can bedistinguished:Off line Operational DatabaseData warehouses in this initial stage are developed by simply copyingthe data off an operational system to another server where theprocessing load of reporting against the copied data does not impactthe operational systems performance.Off line Data WarehouseData warehouses at this stage are updated from data in the operationalsystems on a regular basis and the data warehouse data is stored in adata structure designed to facilitate reporting.Real Time Data WarehouseData warehouses at this stage are updated every time an operationalsystem performs a transaction (e.g. an order or a delivery or a booking.)Integrated Data WarehouseData warehouses at this stage are updated every time an operationalsystem performs a transaction. The data warehouses then generatetransactions that are passed back into the operational systems.
    • Client ClientWarehouseSourceSource SourceQuery & AnalysisIntegrationMetadataData Warehouse Architecture
    •  The data has been selected from various sources and then integrate andstore the data in a single and particular format. Data warehouses contain current detailed data, historical detaileddata, lightly and highly summarized data, and metadata. Current and historical data are voluminous because they are stored atthe highest level of detail. Lightly and highly summarized data are necessary to save processingtime when users request them and are readily accessible. Metadata are “data about data”. It is important fordesigning, constructing, retrieving, and controlling the warehousedata.Technical metadata include where the data come from, how the data werechanged, how the data are organized, how the data are stored, whoowns the data, who is responsible for the data and how to contactthem, who can access the data , and the date of last update.Business metadata include what data are available, where the dataare, what the data mean, how to access the data, predefined reportsand queries, and how current the data are.
    • Business advantages It provides business users with a “customer-centric” view of thecompany’s heterogeneous data by helping to integrate data fromsales, service, manufacturing and distribution, and other customer-related business systems. It provides added value to the company’s customers by allowing themto access better information when data warehousing is coupled withinternet technology. It consolidates data about individual customers and provides arepository of all customer contacts for segmentationmodeling, customer retention planning, and cross sales analysis. It removes barriers among functional areas by offering a way toreconcile views from multiple areas, thus providing a look at activitiesthat cross functional lines. It reports on trends across multidivisional, multinational operatingunits, including trends or relationships in areas such asmerchandising, production planning etc.
    • Strategic uses of data warehousingIndustry Functional areas ofuseStrategic useAirline Operations; marketing Crew assignment, aircraft development, mixof fares, analysis of route profitability,frequent flyer program promotionsBanking Product development;Operations; marketingCustomer service, trend analysis, product andservice promotions, reduction of ISexpensesCredit card Product development;marketingCustomer service, new information service,fraud detectionHealth care Operations Reduction of operational expensesInvestment andInsuranceProduct development;Operations; marketingRisk management, market movementsanalysis, customer tendencies analysis,portfolio managementRetail chain Distribution; marketing Trend analysis, buying pattern analysis,pricing policy, inventory control, salespromotions, optimal distribution channelTelecommunications Product development;Operations; marketingNew product and service promotions,reduction of IS budget, profitabilityanalysisPersonal care Distribution; marketing Distribution decisions, product promotions,sales decisions, pricing policyPublic sector Operations Intelligence gathering
    • Disadvantages of data warehouses Data warehouses are not the optimal environment forunstructured data. Because data must be extracted, transformed and loaded intothe warehouse, there is an element of latency in datawarehouse data. Over their life, data warehouses can have high costs.Maintenance costs are high. Data warehouses can get outdated relatively quickly. There is acost of delivering suboptimal information to the organization. There is often a fine line between data warehouses andoperational systems. Duplicate, expensive functionality may bedeveloped. Or, functionality may be developed in the datawarehouse that, in retrospect, should have been developed inthe operational systems and vice versa.
    • Data Marts A data mart is a scaled down version of a data warehouse that focuses on aparticular subject area. A data mart is a subset of an organizational data store, usually oriented toa specific purpose or major data subject, that may be distributed to supportbusiness needs. Data marts are analytical data stores designed to focus on specific businessfunctions for a specific community within an organization. Usually designed to support the unique business requirements of aspecified department or business process Implemented as the first step in proving the usefulness of the technologiesto solve business problemsReasons for creating a data mart Easy access to frequently needed data Creates collective view by a group of users Improves end-user response time Ease of creation in less time Lower cost than implementing a full Data warehouse Potential users are more clearly defined than in a full Data warehouse
    • From the Data Warehouse to Data MartsDepartmentallyStructuredIndividuallyStructuredData WarehouseOrganizationallyStructuredLessMoreHistoryNormalizedDetailedDataInformation
    • Characteristics of the Departmental Data Mart• Small• Flexible• Customized by Department• OLAP• Source is departmentallystructured data warehouseData martData warehouse
    • Data Mining Data Mining is the process of extracting information from thecompanys various databases and re-organizing it for purposesother than what the databases were originally intended for. It provides a means of extracting previously unknown, predictiveinformation from the base of accessible data in data warehouses. Data mining process is different for different organizationsdepending upon the nature of the data and organization. Data mining tools use sophisticated, automated algorithms todiscover hidden patterns, correlations, and relationships amongorganizational data. Data mining tools are used to predict future trends andbehaviors, allowing businesses to make proactive, knowledgedriven decisions. For ex: for targeted marketing, data mining can use data on pastpromotional mailings to identify the targets most likely tomaximize the return on the company’s investment in futuremailings.
    • Functions Classification: It infers the defining characteristics ofa certain group Clustering: identifies group of items that share aparticular characteristic Association: identifies relationships between eventsthat occur at one time Sequencing: similar to association, except that therelationship exists over a period of time Forecasting: estimates future values based on patternswithin large sets of data
    • Characteristics Data mining tools are needed to extract the buried information “ore”. The “miner” is often an end user, empowered by “data drills” and otherpower query tools to ask ad hoc questions and get answers quickly, withlittle or no programming skill. The data mining environment usually has a client/server architecture. Because of the large amounts of data, it is sometimes necessary to useparallel processing for data mining. Data mining tools are easily combined with spreadsheets and other enduser software development tools, enabling the mined data to beanalyzed and processed quickly and easily. Data mining yields five types of information:associations, sequences, classifications, clusters and forecasting. “Striking it rich” often involves finding unexpected, valuable results.
    • Common data mining applicationsAPPLICATION DESCRIPTIONMarketsegmentationIdentifies the common characteristics of customerswho buys the same products from the companyCustomer churn Predicts which customers are likely to leave yourcompany and go to a competitorFraud detection Identifies which transactions are most likely to befraudulentDirect marketing Identifies which prospects should be included in amailing list to obtain the highest response rateMarket basedanalysisUnderstands what products or services arecommonly purchased togetherTrend analysis Reveals the difference between a typical customerthis month versus last monthScience Simulates nuclear explosions; visualizes quantumphysics
    • Entertainment Models customer flows in theme parks; analyzes safetyof amusement parks ridesInsurance andhealth carePredicts which customers will buy new policies;identifies behavior patterns that increase insurancerisk; spots fraudulent claimsManufacturing Optimizes product design, balancing manufacturabilityand safety; improves shop-floor scheduling andmachine utilizationMedicine Ranks successful therapies for different illnesses;predicts drug efficacy; discovers new drugs andtreatmentsOil and gas Analyzes seismic data for signs of underground deposits; prioritizes drilling locations; simulates undergroundflows to improve recoveryRetailing Discerns buying-behavior patterns; predicts howcustomers will respond to marketing campaigns
    • z Data Warehousing provides theEnterprise with a memoryz Data Mining provides the Enterprisewith intelligenceData Mining works with DataWarehouse
    • Data mining for decision supportTwo capabilities are provided new businessopportunities Automated prediction of trends and behavior: forex, targeted marketing. Automated discovery of previously unknown patterns: forex, detecting fraudulent credit card transactions andidentifying anomalous data representing data entry-keyingerrors.
    • Data mining toolsIT tools and techniques are used by data miners Neural computing: It is a machine learning approach by whichhistorical data can be examined for patterns. Intelligent agents: It is the promising approach to retrieveinformation from the internet or from intranet-based databases. Association analysis: An approach that uses a specialized set ofalgorithms that sort through large data sets and expresses statisticalrules among items.
    • Text mining Text mining is the application of data mining to nonstructured or less structured text files. Operates with less structured information Frequently focused on document format rather thandocument content
    • Text mining helps in…. Find the “hidden” content of documents, includingadditional useful relationships Relate documents across previously unnoticed divisions(e.g.: discover that customers in two different productdivisions have the same characteristics) Group documents by common themes (e.g.: identify all thecustomers of an insurance firms who have similarcomplaints and cancel their policies)
    • To summarize ... OLTP Systems areused to “run” a business The Data Warehousehelps to “optimize” thebusiness
    • OLAP Online Analytical Processing - coined byEF Codd in 1994 paper contracted byArbor Software Generally synonymous with earlier terms such asDecisions Support, Business Intelligence, ExecutiveInformation System OLAP = Multidimensional Database
    • OLAP Online analytical processing refers to such end useractivities as DSS modelling using spreadsheets andgraphics that are done online. OLAP involves many different data items in complexrelationships. Objective of OLAP is to analyze complex relationshipsand look for patterns, trends and exceptions.
    • OLAP Is FASMI Fast Analysis Shared Multidimensional Information
    • Strengths of OLAP It is a powerful visualization paradigm It provides fast, interactive response times It is good for analyzing time series It can be useful to find some clusters and outliers Many vendors offer OLAP tools such asbrio.com, cognus.com, microstrategy.com etc and it ispossible to access an OLAP database from web.
    • Data warehousing integrationDATASOURCES(databases)End Users:Decision making and other tasks:CRM, DSS, EISInformation DataWarehouse (storage)Analytical processing,Data miningData visualizationGenerate knowledgeOrganizationalKnowledge basePurchasedknowledgeDirect useDirect useUseUseSTORAGEstorageUse ofknowledgeDataorganization ;storageuse
    •  Businesses run on information and the knowledgeof how to put that information to use. Knowledge is not readily available, it iscontinuously constructed from data and/orinformation, in a process that may not be simple oreasy. The transformation of data into knowledge may beaccomplished in several waysData collection from various sources stored in simpledatabases
    •  Data can be processed, organized, and stored in a data warehouse andthen analyzed (e.g.) by using analytical processing) by end users fordecision support. Some of the data are converted to information prior to storage in thedata warehouse, and some of the data and/or information can beanalyzed to generate knowledge. For example, by using data mining, aprocess that looks for unknown relationships and patterns in the data,knowledge regarding the impact of advertising on a specific group ofcustomers can be generated. This generated knowledge is stored in an organizational knowledgebase, a repository of accumulated corporate knowledge and ofpurchased knowledge. The knowledge in the knowledge base can be used to support lessexperienced and users, or to support complex decision making.Both the data and the information, at various times during the process, andthe knowledge derived at the end of the process, may need to bepresented to users.
    • Data Warehouse for Decision Support Putting Information technology to help the knowledgeworker make faster and better decisions Used to manage and control business Data is historical or point-in-time Optimized for inquiry rather than update Use of the system is loosely defined and can be ad-hoc Used by managers and end-users to understand thebusiness and make judgments
    • Business intelligence and data warehousing
    • Business Intelligence One ultimate use of the data gathered and processed in thedata life cycle is for business intelligence. Business intelligence generally involves the creation or useof a data warehouse and/or data mart for storage ofdata, and the use of front-end analytical tools such asOracle’s Sales Analyzer and Financial Analyzer or MicroStrategy’s Web. Such tools can be employed by end users to access data, askqueries, request ad hoc (special) reports, examinescenarios, create CRM activities, devise pricingstrategies, and much more.
    • How business intelligence works? The process starts with raw data which are usually kept incorporate data bases. For example, a national retail chainthat sells everything from grills and patio furniture toplastic utensils had data about inventory, customerinformation, data about past promotions, and salesnumbers in various databases. Though all this information may be scattered acrossmultiple systems-and may seem unrelated-businessintelligence software can being it together. This is done byusing a data warehouse. In the data warehouse (or mart) tables can be linked, anddata cubes are formed. For instance, inventoryinformation is linked to sales numbers and customerdatabases, allowing for deep analysis of information.
    •  Using the business intelligence software the user can askqueries, request ad-hoc reports, or conduct any otheranalysis. For example, deep analysis can be carried out byperforming multilayer queries. Because all the databasesare linked, one can search for what products a store has toomuch of, determine which of these products commonlysell with popular items, bases on previous sales. Afterplanning a promotion to move the excess stock along withthe popular products (by bundling them together, forexample), one can dig deeper to see where this promotionwould be most popular (and most profitable). The resultsof the request can be reports, predictions, alerts, and/orgraphical presentations. These can be disseminated todecision makers to help them in their decision-makingtasks.
    • More advanced applications of business intelligenceinclude outputs such as financial modeling budgeting resource allocation and competitive intelligence.