Become BI Architect with 1KEY Agile BI Suite - OLAP

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Celebrating MIS Champions - Become BI Architect with 1KEY Agile BI Suite - OLAP

Celebrating MIS Champions - Become BI Architect with 1KEY Agile BI Suite - OLAP

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  • Hello to everybody and Welcome to Maia developer events.. my name is ekta pardhi in this session we are going to target 1KEY OLAP and related technonology .i believe most of the people knows what is BI ,data warehouse and OLAP concept ? BI is a decision supporting application for decision makers. But Why business need to be intelligence? Let a take a example, of mobile company (everybody is having mobile or not just joking I know everybody is having) market, many service provider are available But we want best and better one .. Which gives us proper network ,service.. But Why we want best one ? In business also we want always growth. It seeks to help you to analyze and make better business decisions, to improve sales or customer satisfaction .It presents the information you need.. when you need it .


  • 1. What is Business Intelligence? Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. Ekta Pardhi
  • 2.
    • What is OLTP ?
    • Traditional RDBMS are used for OLTP
    • On-Line Transaction Processing
      • used for daily processing
      • detailed, up to date data
      • read/update a few records
      • isolation, recovery and integrity are critical
  • 3. Data, Data everywhere yet ...
    • 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 from one form to other
  • 4.
    • What is OLAP(On-line Analytical Processing) ?
    • Online Analytical Processing
      • dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data
      • normally implemented using specialized multi-dimensional DBMS
        • a method of visualizing and manipulating data with many inter-relationships
      • Support common analytical operations such as
        • consolidation
        • drill-down
        • slicing and dicing
  • 5. Online Analytical Processing (OLAP ) It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. Time Product Region SQL 2000 Oracle Access SQL 2005 1KEY OLAP
  • 6. OLAP Applications - multi-dimensional views of data
    • Core requirement of building a ‘realistic’ business model .
    • Provides basis for analytical processing through flexible access to corporate data.
    • The underlying database design that provides the multi-dimensional view of data should treat all dimensions equally.
  • 7. Dimensions: Product, Region, Time Hierarchical summarization paths Product Region Time Industry Country Year Category Region Quarter Product City Month Week Office Day Conceptual Data Model Multi-dimensional view of Data
    • Each dimension is described by a set of attributes; the attributes of a dimension may be related via hierarchy of relationships.
    Month 1 2 3 4 7 6 5 Product Toothpaste Juice Cola Milk Cream Soap Region W S N
  • 8.
    • OLAP Applications - support for complex calculations
    • Must provide a range of powerful computational methods such as that required by sales forecasting, which uses trend algorithms such as moving averages and percentage growth.
    • Mechanisms for implementing computational methods should be clear and non-procedural .
  • 9.
    • OLAP Applications – time intelligence
    • Key feature of almost any analytical application as performance is almost always judged over time.
    • Time hierarchy is not always used in same manner as other hierarchies.
    • Concepts such as year-to-date and period-over-period comparisons should be easily defined.
  • 10.
    • What is Data warehouse?
    • A DW is a subject-oriented, integrated, time-varying, non-volatile collection of data that is used primarily in organizational decision making
    Ekta Pardhi
  • 11. Graphically Data Warehouse Subject Oriented Credit Risk Interest Rate Risk Forex Risk ALM Subject Area OLTP Systems Integrated Credit Risk Code: XXXX Interest Rate Risk Code: CXXYY Forex Risk Code: XX/XX.XX Common Code for various source codes ALM Subject Area Non Volatile OLTP U S E R S Data Warehouse U S E R S Read Write Read Time variant Dec 98 1995 1996 1997 1998 OLTP OLAP
  • 12.
    • Data Warehouse is not …
    • a product, an environment
    • a system, an architecture
    • an end to itself but is at the heart of the Business Intelligence (BI) infrastructure of the organization
  • 13. OLTP/OLAP
    • OLTP (on-line transaction processing)
      • Major task of traditional relational DBMS
      • Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
    • OLAP (on-line analytical processing)
      • Major task of data warehouse system
      • Data analysis and decision making
    • Distinct features (OLTP vs. OLAP)
      • User and system orientation: customer vs. market
      • Data contents: current, detailed vs. historical, consolidated
      • Database design: ER + application vs. star + subject
      • View: current, local vs. evolutionary, integrated
      • Access patterns: update vs. read-only but complex queries
  • 14. OLTP vs. OLAP
  • 15. Examples of OLAP applications in various functional areas
  • 16. Nature of OLAP Analysis
    • Aggregation -- (total sales, percent-to-total)
    • Comparison -- Budget vs. Expenses
    • Ranking -- Top 10, quartile analysis
    • Access to detailed and aggregate data
    • Complex criteria specification
    • Visualization
  • 17.
    • 1KEY OLAP Related Terms
    • Dimension
    • Attribute
    • Hierarchy
    • Fact table
    • Measures
    • Model(cube)
    • Calculated member
  • 18.
    • Dimension
    • A dimension table contains the specific name of each member of the dimension and Dimensions determine the contextual background for the facts
  • 19. Dimension
  • 20.
    • Attribute
    • Information about a specific dimension member
  • 21. Attribute
  • 22.
    • Fact table
    • Fact table contains values for one or more measures at the lowest level of detail for one or more dimensions
  • 23. Fact Table
  • 24.
    • Measures
    • A summarizable numerical value used to monitor business activity
  • 25. Measure
  • 26.
    • Calculated member
    • A mechanism for aggregating measures using formulas more complex than those stored in a cube
  • 27.
    • Hierarchies
    • Think of the points of the data cube as partitioned along each dimension (at some level of granularity)
    • E.g., time dimension: partition according to days, weeks, months,…
    • E.g., cars dimension: partition by model, by color, both model and color, …
    • E.g., dealers dimension: partition by dealer, by city, by state, ...
  • 28. Aggregation Hierarchies
  • 29.
    • Model(cube)
    • A collection of one or more related measure groups and their associated dimensions
  • 30. T i m e p r o d c u s t p r o m o f a c t date, custno, prodno, cityname, sales
  • 31. Region Scheme Time East West North South 1997 1998 1999 2000 Housing Car Edu. Industry Cell 200 Measure Dimension Components of a Data Cube (3D)
  • 32. 1KEY OLAP console
  • 33.
    • DW Modeling
    • Modeling data warehouses: dimensions & measures
      • Star schema : A fact table in the middle connected to a set of dimension tables
        • A star schema is a set of tables comprised of a single, central fact table surrounded by de-normalized dimensions. Each dimension is represented in a single table. Star schema implement dimensional data structures with de- normalized dimensions.
      • Snowflake schema : A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake
        • A snowflake schema is a set of tables comprised of a single, central fact table surrounded by normalized dimension hierarchies. Each dimension level is represented in a table. Snowflake schema implement dimensional data structures with fully normalized dimensions.
  • 34. Star Schema
    • A single fact table and for each dimension one dimension table
    • Does not capture hierarchies directly
    f a c t T i m e p r o d c u s t p r o m o f a c t date, custno, prodno, cityname, sales
  • 35. Snowflake Schema
    • Represent dimensional hierarchy directly by normalizing tables.
    • Easy to maintain and saves storage
    f a c t T i m e p r o d c u s t p r o m o f a c t date, custno, prodno, cityname, sales r e g i o n
  • 36. Components of a star schema Excellent for ad-hoc queries, but bad for online transaction processing
  • 37. Star schema example
  • 38. Star Schema – Example 1 OrderNo SalespersonID CustomerNo ProdNo DateKey CityName Quantity TotalPrice Fact Table CityName State Country City DateKey Date Month Year Date ProdNo ProdName ProdDescr Category CategoryDescr UnitPrice QOH Product OrderNo OrderDate Order SalespersonID SalespersonName City Quota Salesperson CustomerNo CustomerName CustomerAddress City Customer
  • 39. Snowflake – Example 1
  • 40. Star Schema – Example 2 Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city province_or_street country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
  • 41. Snowflake Schema –Example Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city_key location item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city province_or_street country city
  • 42.
    • Features of a OLAP Reporting Solution
    • Must connect with an OLAP data source
    • Must allow the user to drill down into the data with flexibility and ease
    • Must provide a user-friendly visual interface that includes charts, because reporting is most effective when it is visual.
    • Must allow the user to save and retrieve custom reports.
  • 43. Some variants
  • 44.
    • Metadata
    • Metadata explains what data exists, where it is located and how to access it. The metadata is a core of a data logistics system, the infrastructure for DW and ultimately the intelligence system.
  • 45.
    • Data Mart
    • A Data mart is a collection of data specifically designed for the use of a department.
    • Data mart is nothing but a smaller, more focused data warehouse.
    • In many organizations it is useful to create data marts for specific business units that have very specific data analysis needs.
    • The data mart reflects the peculiar needs of the department.
    • A typical example of a departmental data mart in a Bank would be that of a Human Resource Department.
  • 46. Enterprise Data Warehouse vs. Data Mart
  • 47. DW and Data Mart: some other issues Characteristics Data Mart Enterprise DW Time to build Months Years Complexity to build Low to medium High Shared Shared in Dept. Shared across Update Frequently Less Frequently Type of user Subject focused Corporate and top executives
  • 48.
    • Implementation and Success Criteria
    • Do not try to implement the entire data warehouse at once
    • The project should break up the functionality to be delivered in different phases
    • You will not only deliver something tangible for your users, but you may also flush out issues that can be quickly corrected
    • Users are constantly knocking on your door
    • The buzz in the hallways mentions the data warehouse, or meetings make reference to it as the source of data
    • The data warehouse and OLAP becomes the heartbeat of the business, where decisions are made from the data intelligence it provides
  • 49. Strengths of OLAP
    • It is a powerful visualization tool
    • It provides fast, interactive response times
    • It is good for analyzing time series
    • It can be useful to find some clusters and outliners
  • 50.
    • Data Warehousing and OLAP Key Benefits
    • Centralized Data Repository
    • Single Version of the Truth
    • Clean, Accurate Data
    • Organized, Timely Availability
    • Historical Detail
    • Shared and Accessible
    • Ad hoc Discovery & Analysis
  • 51.
    • Walkthrough MAIA OLAP
  • 52.  
  • 53.  
  • 54.  
  • 55.  
  • 56.  
  • 57.  
  • 58.  
  • 59.  
  • 60.  
  • 61.
    • 1KEYOLAP Project Management
    • Data warehouse projects are significantly more dynamic and complicated than traditional systems projects – more change to:
      • Scope
      • Tool evaluation and performance
      • Cross-departmental issues, requirements
      • Cross-departmental data integration and usage
      • Staffing resources
      • Cultural issues
    • DW Project Manager must be more actively involved in all facets of project, understand all facets and issues – not all project managers will succeed as DW project managers
  • 62.
    • Thank You!
    • Ekta Pardhi
    • [email_address]