Presentation on theme: "Data Warehousing – An Introductory Perspective"— Presentation transcript:
1 Data Warehousing – An Introductory Perspective Selling your ideas is challenging. First, you must get your listeners to agree with you in principle. Then, you must move them to action. Use the Dale Carnegie Training® Evidence – Action – Benefit formula, and you will deliver a motivational, action-oriented presentation.DWCC BBSR
2 Agenda Why Data Warehouse Definition and Architecture Terminology Open your presentation with an attention-getting incident. Choose an incident your audience relates to. The incidence is the evidence that supports the action and proves the benefit. Beginning with a motivational incident prepares your audience for the action step that follows.
3 The Business Need Business Decisions Are not made by Rolling Dices I think…. errrr,I guess soBusiness DecisionsAre not made byRolling DicesWe Don’t knowWhat we don’t knowNext, state the action step. Make your action step specific, clear and brief. Be sure you can visualize your audience taking the action. If you can’t, they can’t either. Be confident when you state the action step, and you will be more likely to motivate the audience to action.
4 Current Business Environment CompetitiveEver ChangingChaoticGlobalUrgency to make decisionsCompetitive advantages stems from well informed decisionsBased on an understanding of:Your ProductsYour Customers PreferencesThe CompetitionYour own company strengthsTo complete the Dale Carnegie Training® Evidence – Action – Benefit formula, follow the action step with the benefits to the audience. Consider their interests, needs, and preferences. Support the benefits with evidence; i.e., statistics, demonstrations, testimonials, incidents, analogies, and exhibits and you will build credibility.
5 The Value Pyramid Increased revenue Increased productivity Each layer providesValue en route to atargeted businessOutcomeIncreased revenueIncreased productivityReduced costsCompetitive advantageTo close, restate the action step followed by the benefits. Speak with conviction and confidence, and you will sell your ideas.
6 DefinitionsA collection of integrated, subject oriented databases designed to support the DSS function where each unit of data is relevant at some moment of time (Inmon 1991)A copy of transaction data specifically structured to Query and Analysis (Kimball 1996)Data Warehouse is NOT a specific technologyIt is a series of processes, procedures and tools that help the enterprise understand more about itself, its products, its customers and the market it services.It is NOT possible to purchase a Data WarehouseBut, it is possible to build one.
7 sachin_kambhoj:sachin_kambhoj:FEATURESNon Volatile - Used mainly for reporting purpose and it is independent of transactional data.Subject Orientation- All relevant data is stored together. Ex: Sales, Finance, Marketing, Customer data etc.Historical data- Can contain data of several years depending on company requirements.
8 Subject Orientation. Operational Datawarehouse AUTO Customer HEALTH PolicyLIFEPremiumCASUALTYClaimsApplicationsSubjects
9 Goals and Applications Goals of a Data WarehouseProvide reliable, High performance accessConsistent view of Data: Same query, same data. All users should be warned if data load has not come in.Slice and dice capabilityQuality of data is a driver for business re-engineering.Data Warehousing Applications:Customer Profitability AnalysisCustomer satisfaction and retentionBuyer behavior.Pricing, Promotion AnalysisMarket researchInventory optimization
10 OLTP v/s Data Warehouse OLTP system runs the business, Data Warehouses tell you how to run the businessCharacteristicOLTPData WarehouseOrientationTransactionAnalysisData AccessRecord at a timeSet at a timeUpdatesFrequent & UnscheduledPeriodic & ScheduledResponse timeSeconds requiredMinutes acceptableConcurrent usersManyFewAvailabilityGuaranteedAs neededData structuresHighly normalizedOften de-normalizedData natureCurrenthistorical
11 If most of your business needs are To report on data in a single transaction processing systemAll the historical data you need are in the systemData in the system is cleanYour hardware can support reporting against the live system dataThe structure of the system data is relatively simpleYour firm does not have much interest in end user adhoc query/report toolsData warehousing may not be for your business!!
12 Modeling Constructs Entity Relationship Diagram Star schema Snow flake schemaWithin the implementation of a warehouse, several of these constructs may be integrated to form an optimal design
13 Entity Relationship Diagram Based on set theory and SQLHighly normalizedOptimized for update and fast transaction turnaroundNot suited for querying in a data warehouse environmentdiagrams like these are very difficult for users to visualize and memorize.
14 Star Schema Facts are numerical measurements of business with A central fact table surrounded by a number of dimension tables.Dimensions are business entities on which calculations are done. They can be numeric or alphanumeric.Example: Product table comprising brand name,category, packaging type, size.Facts are numerical measurements of business withrespect to dimensions.They are numeric and additive(summable across any combination)e.g. A sales fact table could contain time, product and storekey along with dollars sold, units sold, dollars cost.
15 Snow Flake SchemaNormalized version of the star schema with the addition of normalized dimension tables.Normalization helps to reduce redundancy in the dimension tables, but affects performance and user comprehension.
16 DW Terminology Granularity Granularity (or grain) defines the level of detail stored in the physical warehouseLow granularity indicates lot of detail while high granularity indicates less detail.Example: A commercial airline is building a data warehouse. What will the granularity be?Choice A: Each record represents a flightChoice B: Each record represents the customer on a flightThere is no correct answer. To a large extent, the granularity depends on the business User’s exploitation needs.However, you should be aware that the granularity of data affectsVolumes of Data, Data Maintenance, IndexingLevel of Data ExplorationQuery and Reporting constraints
17 DW Terminology Metadata At all levels of the data warehouse, information is required to support the maintenance and use of the data warehouse.Metadata is data about data.There are two views of MetadataBusiness – are warehouse attributes and properties for use by business usersTechnical – describe data flow from Operational systems into the data warehouseOLAPOnline Analytical processingTool(s) for Analytical Reporting including Graphical capabilities.
18 DW TerminologyOLAP Tools available for exploring the information built in a DW :Multi-dimensional On-line Analytical Processing (MOLAP)The data from data warehouse is queried and dumped periodically on to a server on local network to a data storage called Multi-dimensional Database (MDDB) provided by the OLAP tool. This MDDB forms a Data Mart which is then used for querying and reporting.Relational On-Line Analytical Processing (ROLAP)Refers to the ability to conduct OLAP analysis directly against a relational warehouse without any constraints on the number of dimensions, database size, analytical complexity, or number and type of users.Hybrid On-line Analytical Processing (HOLAP)An environment with a combination of MOLAP and ROLAP data storage. Summarized information is typically stored in an MDDB and detailed data is stored in a Relational environment.
19 TerminologyData Mart- Contains Data about a specific subject. Eg. Official data, Customer data, Campaign data etc.Metadata- Data about data. Describes the data stored in Data warehouse.Data Cubes- Central object of data containing information in a multidimensional structure.Data Cleansing- Regular cleaning of data.ETL- Extraction, Transformation and Loading of Data.Data Mining- A mechanism which uses intelligent algorithms to discover patterns, clusters and models from data.