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Business Intelligence By Vmoulakakis Office2010

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BI AND PERFORMANCE MANAGEMENT

BI AND PERFORMANCE MANAGEMENT

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  • 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
  • 4.
    • 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. 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 mashups
    Gartner 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
  • 11.
    • Database theory and practice
    • 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 systems
    BI 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
  • 23.
    • Understanding of the flow of information throughout the organization
    • 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 interpretation
    Critical Business and Customer Skills and Knowledge
    1/2011
    Vassilis Moulakakis, CIO
  • 29.
    • Basics of data warehousing design and management
    • 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 optimization
    Data 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
  • 39.
    • Data representations
    • 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 presentation
    http://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
  • 47. Chief Information Officer role
    • IT dept. ready for deploying business systems
    • 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 users
    1/2011
    Vassilis Moulakakis, CIO
  • 55.
    • 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.
    Role: Business Intelligence Developer within IT
    1/2011
    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
  • 57. Definitions
    • 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. 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

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