Business Intelligence Presentation (1/2)

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Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...

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Business Intelligence Presentation (1/2)

  1. 1. Business Intelligence What is BI? (Part 1 of 2)
  2. 2. How do I make decissions in my business? • How does the company make decissions? Using intuition? You have to make business decisions based on reality (facts and numbers!) • EPM (Enterprise Performance Management) is a management style focused on measuring companies • Several Methodologies / Strategies: • Balanced Scorecard • Six Sigma, ABC - Activity Based Costing, TQM - Total Quality Management
  3. 3. Phases in the Decision Making Process Intelligence Explicit the problem Design Plan for possible solutions Election Evaluate based on the outcome Implementation Actions according to a plan Control Verify expectations and effects
  4. 4. Types of decisions Structured Semi structured Non structured Strategic Tactic Operational
  5. 5. I need information! • All of these methodologies are based in several numeric indicators • “If you can’t measure it, you can’t manage it” • Indicators come from “day-to-day reality” (main street) • Monthly outcome, Number of complaints filed per product, Number of satisfied customers, Returns • Data exists, but ...
  6. 6. Different Points of View (1) • The CEO says “We need to sell more” • Marketing Mgr thinks “What can we offer to our customers?” • To do this, we need to know: What are the most sold products? What bundles are the most successful? • Who can provide us with this information? Because, we already have this information, don’t we? • IT Mgr, while upgrading platforms and implementing a new CRM system, estimates that the information will be available in 20-30 days...
  7. 7. Different Points of View (2) • Marketing Mgr asks: A month!? Didn’t we have this kind of information in our servers already? • IT Mgr answers:Yes, the data is there, but it doesn’t have the right structure to answer the questions you’re asking • Marketing Mgr keeps thinking that if the data is there, it can’t be so difficult to get the answers they need • IT Mgr keeps thinking that Marketing Mgt always asks for weird things, and with not time at all • And the CEO just wanted to sell more!
  8. 8. Business Problem • Where is the problem? • Marketing Mgr is right: the data IS in the servers • IT Mgr is also right: is not easy to give data the right structure to answers questions • For IT is just enough to deal with data, Marketing needs to extract information from this data.
  9. 9. Data and information are not the same • Companies always maintain several systems to run their everyday business • All of the company workers add and check data from this systems all the time • However sometimes this data, presented in this way, is not enough to make business decisions
  10. 10. Data, Information and Knowledge • Data: entity and transactions stored as structures and codes • Information: is the outcome of processing and extraction of data, with specific domain meaning to those who access it • Knowledge: Information becomes knowledge when is used to make decisions and take actions accordingly
  11. 11. What is Business Intelligence? • Is a set of processes, technologies, applications and practices used to provide information and support the decision making process • It is NOT a standard software product, it is specifically designed by consulting and targeted to a particular business need • There is a series of technology tools that support this objective • To better understand this, a new type of systems categorization appears: OLTP (Transactional) and OLAP (Analytical)
  12. 12. What is BI? SCD MOLAP RDBMS ROLAP ODS Data Warehouse ETL Forecasting Analytics Analytics Data Mining Clustering EIS HOLAP Alerting KPI Reporting OLAP Alerting Time Series Key Performance Indicators Dashboards Sales Intelligence MDM Data Mart Knowledge Discovery Online Analytical Processing Data Integration
  13. 13. Transactional Systems OLTP • Designed to solve everyda work transactions (i.e. sales, customer care, manufacturing) • Points where the data is captured and recorded in the company • Very efficient in the management of specific information • ERP, CRM, RRHH, SCM, Email, Others
  14. 14. Problems and Disadvantages • Relational Databases were designed for everyday work and not for analysis • It is difficult to manage historical information • Data is distributed among multiple systems and databases. How and where do you gather all of the systems data? • To extract information from data you need knowledge of non-trivial skills (programming or SQL language) and is not a dynamic process
  15. 15. Analytical Systems OLAP • OLTP Systems complement • Designed specifically to obtain information, analyze and solve business problems • Specific analytical information is added to the data • They use a different database technology, optimized to extract information. • Analytical systems unify all of the company’s data in one system: the Data Warehouse
  16. 16. What is a Data Warehouse? • Is a digital warehouse with all of the electronic registered data in a company • They store all of the company’s information: daily and historical data • They gather heterogeneous information sources in one centralized space • It is used for reporting, data analysis and exploration, to see and detect changes and tendencies • Only two operations exist: load and query
  17. 17. Data Warehouse or Data Mart? • A Data Warehouse contains all of the company’s information (wide scope, higher risk project) • A Data Mart is targeted to solve one company’s department needs in particular (limited scope, lower risk project) • The Data Warehouse can be built joining multiple specific Data Marts
  18. 18. DWh Objectives • Must allow easy access to the company’s information • Must present this information in a consistent way • Must be adaptable and change-resilient • Must be a safe store, protecting the company’s information assets • Must support the decision making process • Must be accepted by the decision makers to be successful
  19. 19. DWh Challenges • Has to unify the whole company’s data model • Data latency • Historical data storage • Data granularity • Speed and performance in queries • Independent of OLTP system changes
  20. 20. How do we build a DWh? • ETL processes Extract the information from transactional systems, Transform this information and they Load it into the data warehouse • The information is stored in multidimensional databases • Information is ready to be used • The systems to access information are easy to use, you don’t have to work at NASA!
  21. 21. Dimensional Modelling (1) • This is what makes a Data Warehouse a business oriented database • Measures. Business Variables • Numerical values • Sums, consolidations, arithmetic operations • Dimensions • Texts • Filters
  22. 22. Dimensional Modelling (2) Time Date-ID Facts Dimension Year Product-ID Month Date-ID Date-ID SKU Day Product-ID Branch-ID Description Category Total Type Branch-ID # Products Price Country # Tickets State Product Branch City Dimension Dimension
  23. 23. Why is it multidimensional? • A dimension is one of the “edges” of your business • Customers • It is called multidimensional • Invoices because you can see the information from • Orders different “edges” at • Quotes the same time • Time • Activities
  24. 24. ETL Processes Database Data Information OLTP ETL Warehouse Access Systems Transformation Extraction Loading
  25. 25. What’s in a cube? Time ts uc od 967 Jan Feb Mar Apr Pr 540 P4 967 P3 P2 780 P1 Clients Josh Sarah Joe Anna
  26. 26. Data Warehouse Database Technology • MOLAP : Multi Dimensional • ROLAP : Relational • HOLAP : Hybrid
  27. 27. How do I “see” what’s in a DWh? • OLAP Cubes • Reports • Dashboards • KPIs - Key Performance Indicators • Alerts
  28. 28. OLAP Cubes • They let you analyze all of the information available in the Data Warehouse • Each cube stores a set of specific information, and contains different “measures” and “dimensions” • Measures are numbers (i.e. amounts, quantities, percentages) • Dimensions contain attributes to filter and order information • Several visualization tools: Excel, Reports, Web
  29. 29. Some cubes • Sales • Stock • Suppliers • Orders • Accounting... • Human Resources... • Finances...
  30. 30. • Dimensions Sales • Date / Time • Customer • Branch / Store • Product • Discount • Measures • Quantity • Cost • Profit
  31. 31. Stock / Inventory • Dimensions • Date / Time • Store / Branch • Product • Measures • Qty / Price / Cost
  32. 32. Supply • Dimensions • Date / Time • Supplier • Product • Contract / Contract terms • Type of transactions • Measures • Qty / Amount / Cost
  33. 33. Orders • Dimensions • Date / Time • Product • Customer • Salesperson • Terms of sale • Measures • Qty / Amount / Discount
  34. 34. Reports • These are the classic reports we already know • When your reports are built with data from the DWh, you can trust on a reliable data source • Historical data can be accessed too • You can build reports with data coming from different systems in the company • All of the reports are accessed from the same location
  35. 35. Digital Dashboards • Is an information system similar to a car’s dashboard, designed to be easy to read • Easy and visual information presented in a way to help you detect and correct tendencies • Use them to align company strategies among departments and global objectives
  36. 36. Key Performance Indicators (KPI) • They measure specific items and help you organize, define and evaluate your objectives • SMART: Specific, Measurable, Achievable, Relevant, Time-bound • Number of new Customers, Opportunity closing average time, Customer loss index
  37. 37. Conclusions • OLTP systems to support everyday work and give information to the company • OLAP systems to extract and analyze information and to make decisions • Dashboards to concentrate information in a centralized view • OLAP cubes to solve specific questions and freely explore information
  38. 38. Some Software Products you might need • Microsoft SQL Server Analysis Services • Microstrategy • SAS • OpenSource Alternatives (Pentaho)
  39. 39. The End?

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