BI AND PERFORMANCE MANAGEMENT

1. Business Intelligence & Performance Management Center of Excellence for IT 1/2011 Vassilis Moulakakis, CIO

2. IT-enabled business decision making based on simple to complex data analysis processes Database development and administration Data mining Performance Management (B.Scorecards.) Data queries and report writing Data analytics and simulations Benchmarking of business performance Dashboards Decision support systems What is Business Intelligence (BI)? 1/2011 Vassilis Moulakakis, CIO

3. Make more informed business decisions: Competitive and location analysis Customer behavior analysis Targeted marketing and sales strategies Business scenarios and forecasting Business service management Business planning and operation optimization Financial management and compliance Why BI? 1/2011 Vassilis Moulakakis, CIO

5. By 2012, business units will control at least 40% of the total budget for BI

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. In 2009, collaborative decision making will emerge as a new product category that combines social software with BI Platform capabilities

8. By 2012, one-third of analytic applications applied to business processes will be delivered throughcoarse-grained application mashupsGartner Research, Jan 2009, http://www.gartner.com/it/page.jsp?id=856714 Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond 1/2011 Vassilis Moulakakis, CIO

9. Database systems and database integration Data warehousing, data stores and data marts Enterprise resource planning (ERP) systems Query and report writing technologies Data mining and analytics tools Decision support systems Customer relation management software Product lifecycle and supply chain management systems IT Technologies Supporting BI 1/2011 Vassilis Moulakakis, CIO

10. Leveraging new Web 2.0 technologies to: Enhance the presentation layer and data visualization Provide information on-demand and greater customization Increase ability to create corporate and public data mashups Allow interactive user-directed analysis and report writing Moving the Control of BI into the Hands of the Users: BI 2.0 1/2011 Vassilis Moulakakis, CIO

12. Data mining and relational report writing

13. Enterprise data and information flow

14. Information management and regulatory compliance

15. Analytical processing and decision making

16. Data presentation and visualization

17. BI technologies and systems

18. Value chain and customer service management

19. Business process analysis and design

20. Transaction processing systems

21. Management information systemsBI Skill and Knowledge Clusters 1/2011 Vassilis Moulakakis, CIO

22. Knowledge of database systems and data warehousing technologies Ability to manage database system integration, implementation and testing Ability to manage relational databases and create complex reports Knowledge and ability to implement data and information policies, security requirements, and state and federal regulations Critical Information Technology Knowledge and Skills 1/2011 Vassilis Moulakakis, CIO

24. Ability to effectively communicate with and get support from technology and business specialists

25. Ability to understand the use of data and information in each organizational units

26. Ability to present data in a user-centric framework

27. Ability to understand the decision making process and to focus on business objectives

28. Ability to train business users in information management and interpretationCritical Business and Customer Skills and Knowledge 1/2011 Vassilis Moulakakis, CIO

30. Data warehouse architectures

31. Data marts and data stores

32. Data structures and data flow

33. Dimensional modeling

34. Extract, clean, conform and deliver

35. Server management tools to package, backup and restore

36. Database server activity monitoring and performance optimizationData Warehousing 1/2011 Vassilis Moulakakis, CIO

37. For rapid analysis and display of large amounts of data: On-Line Analytical Processing (OLAP) Multidimensional/ hyper cubes OLAP operations: Slice, Dice, Drill Down/Up, Roll-up, Pivot OLAP vendors and products Multidimensional Analysis 1/2011 Vassilis Moulakakis, CIO

38. Data Reporting: the extraction of predictive information from large databases. Data quality AD HOC Reporting Executive Book report Delivery routing Online Reporting Consolidation reporting Data Reporting 1/2011 Vassilis Moulakakis, CIO

40. Information graphics

41. Data representation techniques and tools

42. Visual representation – trends and best practices

43. Interactivity in data representation

44. Tools and applications

45. The user perspective on information presentationhttp://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ Data Visualization 1/2011 Vassilis Moulakakis, CIO

46. Data mining: the extraction of predictive information from large databases. Data trend, connection and behavior pattern analysis Data quality Data mining tools Predictive and business analytics Descriptive and decision models Statistical techniques and algorithms Data Mining 1/2011 Vassilis Moulakakis, CIO

48. BI project lifecycle and management

49. Collaborate with Business/Sale analysts and business executives

50. Capturing and documenting the business requirements for BI solution

51. Translating business requirements into technical requirements

52. Key Performance Indicators (KPIs), actions

53. Data-based decision making

54. Effective communication and consultation with business/sales analysts and business users1/2011 Vassilis Moulakakis, CIO

56. Resources http://www.cio.com/article/671573/4_Personas_of_the_Next_Generation_CIO?taxonomyId=3174 http://www.cio.com/article/40296/Business_Intelligence_Definition_and_Solutions http://www.cio.com/article/148000/10_Keys_to_a_Successful_Business_Intelligence_Strategy http://www.sap.com/greece/campaign/2010_03_CROSS_BI_RC/index.epx?URL_ID=CRM-GR11-ONL-SRC_AAA_01&campaigncode=CRM-GR11-ONL-SRC_AAA_01&dna=117812,8,0,94346249,778755856,1299095460,CIO+AND+BUSINESS+INTELLIGENCE,32740080,6662417885 http://www.youtube.com/watch?v=yfQdaHuta5Q&feature=related http://www.qlikview.com/us/explore/experience/product-tour http://www.youtube.com/watch?v=AWxgSXXBbBA&feature=related http://www.youtube.com/watch?v=gqef_F-fXG4&feature=related 1/2011 Vassilis Moulakakis, CIO

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. 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. 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. 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. OLAP operations: Slice, Dice, Drill Down/Up, Roll-up, Pivot

63. See this site for all these definitions: http://altaplana.com/olap/glossary.html#SLICE AND DICE 1/2011 Vassilis Moulakakis, CIO