Datawarehouse & bi introduction

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Data warehouse and Business Intelligence Introduction

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Datawarehouse & bi introduction

  1. 1. Dataware Housing & Business Intelligence An Overview By Shivmohan Purohit
  2. 2. Agenda <ul><li>Introduction </li></ul><ul><li>Data Warehousing </li></ul><ul><li>Online Analytical Processing </li></ul><ul><li>Data Mining </li></ul><ul><li>Q & A </li></ul>
  3. 3. What a firm/ Organization want to know…. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
  4. 4. What is a Data Warehouse? <ul><li>A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. </li></ul>
  5. 5. What is Data Warehousing? <ul><li>A process of transforming data into information and making it available to users in a timely enough manner to make a difference </li></ul>Data Information
  6. 6. Data Warehousing -- It is a process <ul><li>Technique for assembling and managing data from various sources for the purpose of answering business questions. Thus making decisions that were not previous possible </li></ul><ul><li>A decision support database maintained separately from the organization’s operational database </li></ul>
  7. 7. Data Warehousing <ul><li>A data warehouse is a </li></ul><ul><ul><li>subject-oriented </li></ul></ul><ul><ul><li>integrated </li></ul></ul><ul><ul><li>time-varying </li></ul></ul><ul><ul><li>non-volatile </li></ul></ul><ul><li>collection of data that is used primarily in organizational decision making. </li></ul>
  8. 8. <ul><li>A data warehouse is organized around the major subjects of the organization such as customer, supplier, product, sales, etc.., </li></ul><ul><li>Data warehouse provides a simple and concise view around a particular subject by excluding data that are not useful to the decision support process. </li></ul>Data Warehousing
  9. 9. Type of DW Users Explorers : Seek out the unknown and previously unsuspected rewards hiding in the detailed data Farmers : Harvest information from known access paths Tourists: Browse information
  10. 10. Application-Orientation vs. Subject-Orientation Application-Orientation Operational Database Loans Credit Card Trust Savings Subject-Orientation Data Warehouse Customer Vendor Product Activity
  11. 11. Functioning of Data warehousing Data Source Cleaning Transformation Data Warehouse New Update
  12. 12. Data Warehouse Architecture Data Warehouse Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository Relational Databases Legacy Data Purchased Data ERP Systems
  13. 13. Star Schema <ul><li>A single fact table and for each dimension one dimension table </li></ul><ul><li>Does not capture hierarchies directly </li></ul>T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname, ...
  14. 14. Snowflake schema <ul><li>Represent dimensional hierarchy directly by normalizing tables. </li></ul><ul><li>Easy to maintain and saves storage </li></ul>T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname, ... r e g i o n
  15. 15. OLAP(Online analytical processing) <ul><li>A data warehouse stores data , but OLAP transform the data warehouse data into specific meaningful information. </li></ul><ul><li>Therefore OLAP provides a user friendly environment for interactive data analysis. </li></ul>
  16. 16. OLAP OPERATION on the Multidimensional data <ul><ul><ul><ul><li>Roll-up(GROUP) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Drill down(Less) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Slice and Dice(Pie) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Pivot(rotate) </li></ul></ul></ul></ul>
  17. 17. Multi-dimensional Data <ul><li>“ Hey…I sold $100M worth of goods” </li></ul>Dimensions: Product, Region, Time Hierarchical summarization paths Product Region Time Industry Country Year Category Region Quarter Product City Month Week Office Day Month 1 2 3 4 7 6 5 Product Toothpaste Juice Cola Milk Cream Soap Region W S N
  18. 18. “ Slicing and Dicing” Product Sales Channel Regions Retail Direct Special Household Telecomm Video Audio India Far East Europe The Telecomm Slice
  19. 19. Roll-up and Drill Down <ul><li>Sales Channel </li></ul><ul><li>Region </li></ul><ul><li>Country </li></ul><ul><li>State </li></ul><ul><li>Location Address </li></ul><ul><li>Sales Representative </li></ul>Roll Up Higher Level of Aggregation Low-level Details Drill-Down
  20. 20. Nature of OLAP Analysis <ul><li>Aggregation -- (total sales, percent-to-total) </li></ul><ul><li>Comparison -- Budget vs. Expenses </li></ul><ul><li>Ranking -- Top 10, quartile analysis </li></ul><ul><li>Access to detailed and aggregate data </li></ul><ul><li>Complex criteria specification </li></ul><ul><li>Visualization </li></ul>
  21. 21. Data Mining <ul><li>Data mining is sorting through data to identify patterns and establish relationships. </li></ul>
  22. 22. Data Mining (cont.)
  23. 23. Data Mining works with Warehouse Data <ul><li>Data Warehousing provides the Enterprise with a memory </li></ul><ul><li>Data Mining provides the Enterprise with intelligence </li></ul>
  24. 24. Data Mining Process Cleaning and Integration Databases Data Warehouse Flat Files Patterns Knowledge Selection and transformation Data Mining
  25. 25. Thanks Shivmohan Purohit Q &A Discussion

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