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Business Intelligence By Vmoulakakis Office2010


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Business Intelligence By Vmoulakakis Office2010

  1. 1. Business Intelligence & Performance Management<br />Center of Excellence for IT<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  2. 2. IT-enabled business decision making based on simple to complex data analysis processes<br />Database development and administration<br />Data mining<br />Performance Management (B.Scorecards.)<br />Data queries and report writing<br />Data analytics and simulations<br />Benchmarking of business performance<br />Dashboards<br />Decision support systems<br />What is Business Intelligence (BI)?<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  3. 3. Make more informed business decisions:<br />Competitive and location analysis<br />Customer behavior analysis<br />Targeted marketing and sales strategies<br />Business scenarios and forecasting<br />Business service management<br />Business planning and operation optimization<br />Financial management and compliance<br />Why BI?<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  4. 4. <ul><li>Through 2012, more than 35 % of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets
  5. 5. By 2012, business units will control at least 40% of the total budget for BI
  6. 6. By 2010, 20% of organizations will have an industry-specific analytic application delivered via software as a service (SaaS) as a standard component of their BI portfolio
  7. 7. In 2009, collaborative decision making will emerge as a new product category that combines social software with BI Platform capabilities
  8. 8. By 2012, one-third of analytic applications applied to business processes will be delivered throughcoarse-grained application mashups</li></ul>Gartner Research, Jan 2009,<br />Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  9. 9. Database systems and database integration<br />Data warehousing, data stores and data marts<br />Enterprise resource planning (ERP) systems<br />Query and report writing technologies<br />Data mining and analytics tools<br />Decision support systems<br />Customer relation management software<br />Product lifecycle and supply chain management systems<br />IT Technologies Supporting BI<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  10. 10. Leveraging new Web 2.0 technologies to:<br />Enhance the presentation layer and data visualization<br />Provide information on-demand and greater customization<br />Increase ability to create corporate and public data mashups<br /> Allow interactive user-directed analysis and report writing<br />Moving the Control of BI into the Hands of the Users: BI 2.0<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  11. 11. <ul><li>Database theory and practice
  12. 12. Data mining and relational report writing
  13. 13. Enterprise data and information flow
  14. 14. Information management and regulatory compliance
  15. 15. Analytical processing and decision making
  16. 16. Data presentation and visualization
  17. 17. BI technologies and systems
  18. 18. Value chain and customer service management
  19. 19. Business process analysis and design
  20. 20. Transaction processing systems
  21. 21. Management information systems</li></ul>BI Skill and Knowledge Clusters<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  22. 22. Knowledge of database systems and data warehousing technologies<br />Ability to manage database system integration, implementation and testing<br />Ability to manage relational databases and create complex reports<br />Knowledge and ability to implement data and information policies, security requirements, and state and federal regulations<br />Critical Information Technology Knowledge and Skills<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  23. 23. <ul><li>Understanding of the flow of information throughout the organization
  24. 24. Ability to effectively communicate with and get support from technology and business specialists
  25. 25. Ability to understand the use of data and information in each organizational units
  26. 26. Ability to present data in a user-centric framework
  27. 27. Ability to understand the decision making process and to focus on business objectives
  28. 28. Ability to train business users in information management and interpretation</li></ul>Critical Business and Customer Skills and Knowledge<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  29. 29. <ul><li>Basics of data warehousing design and management
  30. 30. Data warehouse architectures
  31. 31. Data marts and data stores
  32. 32. Data structures and data flow
  33. 33. Dimensional modeling
  34. 34. Extract, clean, conform and deliver
  35. 35. Server management tools to package, backup and restore
  36. 36. Database server activity monitoring and performance optimization</li></ul>Data Warehousing<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  37. 37. For rapid analysis and display of large amounts of data:<br />On-Line Analytical Processing (OLAP)<br />Multidimensional/ hyper cubes<br />OLAP operations: Slice, Dice, Drill Down/Up, Roll-up, Pivot<br />OLAP vendors and products<br />Multidimensional Analysis<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  38. 38. Data Reporting: the extraction of predictive information from large databases.<br />Data quality<br />AD HOC Reporting<br />Executive Book report<br />Delivery routing<br />Online Reporting<br />Consolidation reporting<br />Data Reporting <br />1/2011<br />Vassilis Moulakakis, CIO<br />
  39. 39. <ul><li>Data representations
  40. 40. Information graphics
  41. 41. Data representation techniques and tools
  42. 42. Visual representation – trends and best practices
  43. 43. Interactivity in data representation
  44. 44. Tools and applications
  45. 45. The user perspective on information presentation</li></ul><br />Data Visualization<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  46. 46. Data mining: the extraction of predictive information from large databases.<br />Data trend, connection and behavior pattern analysis<br />Data quality<br />Data mining tools<br />Predictive and business analytics<br />Descriptive and decision models<br />Statistical techniques and algorithms<br />Data Mining<br />1/2011<br />Vassilis Moulakakis, CIO<br />
  47. 47. Chief Information Officer role<br /><ul><li>IT dept. ready for deploying business systems
  48. 48. BI project lifecycle and management
  49. 49. Collaborate with Business/Sale analysts and business executives
  50. 50. Capturing and documenting the business requirements for BI solution
  51. 51. Translating business requirements into technical requirements
  52. 52. Key Performance Indicators (KPIs), actions
  53. 53. Data-based decision making
  54. 54. Effective communication and consultation with business/sales analysts and business users</li></ul>1/2011<br />Vassilis Moulakakis, CIO<br />
  55. 55. <ul><li>BusinessIntelligenceDeveloper is responsible for designing and developing Business Intelligence solutions for the enterprise. The Developer works on-site at the corporate head quarters. Key functions include designing, developing, testing, debugging, and documenting extract, transform, load (ETL) data processes and data analysis reporting for enterprise-wide data warehouse implementations. Responsibilities include: working closely with business and technical teams to understand, document, design and code ETL processes; working closely with business teams to understand, document and design and code data analysis and reporting needs; translating source mapping documents and reporting requirements into dimensional data models; designing, developing, testing, optimizing and deploying server integration packages and stored procedures to perform all ETL related functions; develop data cubes, reports, data extracts, dashboards or scorecards based on business requirements.</li></ul>Role: Business Intelligence Developer within IT <br />1/2011<br />Vassilis Moulakakis, CIO<br />
  56. 56. Resources<br /><br /><br /><br />,8,0,94346249,778755856,1299095460,CIO+AND+BUSINESS+INTELLIGENCE,32740080,6662417885<br /><br /><br /><br /><br />1/2011<br />Vassilis Moulakakis, CIO<br />
  57. 57. Definitions<br /><ul><li>Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
  58. 58. Dashboards: Typically, information is presented to the manager via a graphics display called a Dashboard. A BIS (Business Intelligence System) Dashboard serves the same function as a car’s dashboard. Specifically, it reports key organizational performance data and options on a near real time and integrated basis. Dashboard based business intelligence systems do provide managers with access to powerful analytical systems and tools in a user friendly environment.
  59. 59. Enterprise resource planning (ERP) is a company-wide computer software system used to manage and coordinate all the resources, information, and functions of a business from shared data stores.
  60. 60. Online analytical processing, or OLAP is an approach to quickly answer multi-dimensional analytical queries. OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.  The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing)
  61. 61. Multidimensional/ hyper cubes: A group of data cells arranged by the dimensions of the data. For example, a spreadsheet exemplifies a two-dimensional array with the data cells arranged in rows and columns, each being a dimension. A three-dimensional array can be visualized as a cube with each dimension forming a side of the cube, including any slice parallel with that side. Higher dimensional arrays have no physical metaphor, but they organize the data in the way users think of their enterprise. Typical enterprise dimensions are time, measures, products, geographical regions, sales channels, etc. Synonyms: Multi-dimensional Structure, Cube, Hypercube
  62. 62. OLAP operations: Slice, Dice, Drill Down/Up, Roll-up, Pivot
  63. 63. See this site for all these definitions: AND DICE </li></ul>1/2011<br />Vassilis Moulakakis, CIO<br />