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  • IT-enabled business decision making based on simple to complex data analysis processesDatabase development and administrationData miningPerformance Management (Balanced Scorecards.)Data queries and report writingData analytics and SimulationsBenchmarking of Business PerformanceDashboards
  • Make more informed business decisions:Competitive and location analysisCustomer behavior analysisTargeted marketing and sales strategiesBusiness scenarios and forecastingBusiness service managementBusiness planning and operation optimizationFinancial management and compliance
  • Database systems and database integrationData warehousing, data stores and data martsEnterprise resource planning (ERP) systemsQuery and report writing technologiesData mining and analytics toolsDecision support systemsCustomer relation management softwareProduct lifecycle and supply chain management systems
  • Businessintelligencebyvmoulakakis

    2. 2. WHAT IS BUSINESS INTELLIGENCE (BI)? Database development and administration Performance Management (Balanced Scorecards.) Data mining Data queries and report writing Benchmarking of Business Performance Dashboards Data analytics and Simulations4/3/2013 Vassilis Moulakakis M.Sc. 2
    3. 3. WHY BI? Competitive Customer Targeted and location behavior marketing analysis analysis and sales • Business • Business strategies scenarios and service • Business forecasting management planning • Operation • Financial optimization management and compliance4/3/2013 Vassilis Moulakakis M.Sc. 3
    4. 4. IT TECHNOLOGIES SUPPORTING BI Database systems and database integration Product lifecycle Data and supply chain warehousing, management data stores and systems data marts Enterprise Decision support resource planning systems (ERP) systems Query and report Data mining and writing analytics tools technologies Customer relation management software4/3/2013 Vassilis Moulakakis M.Sc. 4
    5. 5. GARTNER REVEALS FIVE BUSINESS INTELLIGENCE PREDICTIONS FOR 2009 AND BEYOND  T h r o u gh 2 0 1 2 , m o r e t h a n 3 5 % o f t h e t o p 5 , 0 0 0 g l o b a l c o m p a ni e s w i l l r e g u l a r l y f a i l t o m a k e i n s i g ht f ul d e c i s i o ns a b o u t s i g n i f i c a nt c h a n g e s i n their business and markets  By 2012, business units w ill control at least 40% of the total budget for BI  B y 2 0 1 0 , 2 0 % o f o r g a n i z a t io ns w i l l h a v e a n i n d u s t r y- s p e c i f ic a n a l yt i c application delivered via software as a service (SaaS) as a standard c o m p o ne nt o f t h e i r B I p o r t f o l io  I n 2 0 0 9 , c o l l a bo r a t i v e d e c i s i on m a k i n g w i l l e m e r g e a s a n e w p r o d u c t c a t e g or y t h a t c o m b i n e s s o c i a l s o f t w a r e w i t h B I P l a t f or m c a p a b i l it i e s  B y 2 0 1 2 , o n e - t h i r d o f a n a l yt i c a p p l i c a t i ons a p p l i e d t o b u s i n e s s p r o c e s s e s w i l l b e d e l i v e r e d t h r o u g h c o a r s e - gr a i ne d a p p l i c a t i on m a s h u p s G a r t n e r R e s e a r c h , J a n 2 0 0 9 , h t t p : / /www. g a r tn e r. c o m/ i t / p a g e . j s p ? i d = 8 5 6 7 1 44/3/2013 Vassilis Moulakakis M.Sc. 5
    6. 6. MOVING THE CONTROL OF BI INTO THE HANDS OF THE USERS: BI 2.0Leveraging 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 writing4/3/2013 Vassilis Moulakakis M.Sc. 6
    7. 7. BI SKILL AND KNOWLEDGE CLUSTERS  Database theory and practice  Data mining and relational report writing  Enterprise data and information flow  Information management and regulatory compliance  Analytical processing and decision making  Data presentation and visualization  BI technologies and systems  Value chain and customer service management  Business process analysis and design  Transaction processing systems  Management information systems4/3/2013 Vassilis Moulakakis M.Sc. 7
    8. 8. CRITICAL INFORMATION TECHNOLOGY KNOWLEDGE AND SKILLS 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 regulations4/3/2013 Vassilis Moulakakis M.Sc. 8
    9. 9. CRITICAL BUSINESS AND CUSTOMER SKILLS AND KNOWLEDGE  Understanding of the flow of information throughout the organization  Ability to effectively communicate with and get support from technology and business specialists  Ability to understand the use of data and information in each organizational units  Ability to present data in a user -centric framework  Ability to understand the decision making process and to focus on business objectives  Ability to train business users in information management and interpretation4/3/2013 Vassilis Moulakakis M.Sc. 9
    10. 10. DATA WAREHOUSING  Basics of data warehousing design and management  Data warehouse architectures  Data marts and data stores  Data structures and data flow  Dimensional modeling  Extract, clean, conform and deliver  Server management tools to package, backup and restore  Database server activity monitoring and performance optimization4/3/2013 Vassilis Moulakakis M.Sc. 10
    11. 11. MULTIDIMENSIONAL ANALYSISFor 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 products4/3/2013 Vassilis Moulakakis M.Sc. 11
    12. 12. DATA REPORTING Data Reporting: the extraction of predictive information from large databases.  Data quality  AD HOC Reporting  Executive Book report  Delivery routing  Online Reporting  Consolidation reporting4/3/2013 Vassilis Moulakakis M.Sc. 12
    13. 13. DATA VISUALIZATION  Data representations  Information graphics  Data representation techniques and tools  Visual representation – trends and best practices  Interactivity in data representation  Tools and applications  The user perspective on information presentation h t t p: / / w w w.smashingma gazine. c om/20 07/0 8/02 /dat a - visualiz at ion - modern - a ppro a c h es/4/3/2013 Vassilis Moulakakis M.Sc. 13
    14. 14. DATA MININGData 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 algorithms4/3/2013 Vassilis Moulakakis M.Sc. 14
    15. 15. BI AND PERFORMANCE MANAGER ROLE  IT dept. ready for deploying business systems  BI project lifecycle and management  Collaborate with Business/Sale analysts and business executives  Capturing and documenting the business requirements for BI solution  Translating business requirements into technical requirements  Key Performance Indicators (KPIs), actions  Data-based decision making  Effective communication and consultation with business/sales analysts and business users4/3/2013 Vassilis Moulakakis M.Sc. 15
    16. 16. ROLE: BUSINESS INTELLIGENCE DEVELOPER WITHIN IT  Business Intelligence Developer - 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.4/3/2013 Vassilis Moulakakis M.Sc. 16
    17. 17. RESOURCES h t t p : // www. c i o . c o m/ a r ti c l e / 6 7 1 5 7 3 /4 _ P e r s o n a s _ o f _ t h e _ N e xt _ G e n e r a t i o n _ C I O ? t a xo n o m yI d =3 1 7 4 h t t p : // www. c i o . c o m/ a r ti c l e / 4 0 2 9 6 /B u s i n e ss _ I n t e l l i g e n c e _ D e f i n i t i o n _ a n d _ S o l u t i o n s h t t p : // www. c i o . c o m/ a r ti c l e / 1 4 8 0 0 0 /1 0 _ K e ys _ t o _ a _ S u c c e s sf u l _ B u si n e s s _ I n t e l l i g e n c e _ S t r a te g y h t t p : // www. s ap. c om/ gr eec e/ c a mp a i g n / 2 0 1 0 _ 0 3 _ C RO S S _ B I _ R C / i n dex. epx? U R L_I D = C R M - G R 11 - O N L - S R C _ A A A _ 0 1 & c a mp a i g n c o d e = CR M - G R 11 - O N L - S R C _ A A A _ 0 1 & d n a = 11 7 8 1 2 , 8 , 0 , 9 4 3 4 6 2 4 9 , 7 7 8 7 5 5 8 5 6 , 1 2 9 9 0 9 5 4 6 0 , CI O + A N D +B U S I N E S S + I N TE L L I G E N C E , 3 2 7 4 0 0 8 0 , 6 6 6 2 4 1 7 8 8 5 h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= yf Q d a H u t a 5 Q & f e a t u r e = r e l a t e d h t t p : // www. q l i k vi e w. c o m/ u s / e xp l o r e / e xp e r i e n c e / p r o d u c t - t o u r h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= AW xg S X X B b B A & f e a t u r e = r e l a t e d h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= g q e f _ F - f X G 4 & f e a t u r e =r e l a t e d 4/3/2013 Vassilis Moulakakis M.Sc. 17
    18. 18. DEFINITIONS  Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the am ount of data doublin g ever y thr ee year s data m ining is bec om ing an inc r eas in g l y 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.  Dash b o ard s : T ypic a l l y , inf or m ation is pr es ented to the m anager via a gr aphic s dis pla y c alled a Das hboar d. A BIS ( Bus ines s Intell i ge nc e Sys tem ) Das hboar d s er ves the s am e f unc tion as a c ar ’s dashboard. Specifically, it reports key organizational performance data and options on a near r eal tim e and integr ate d bas is . Das hboar d bas ed bus ines s intel l ig enc e s ys tem s do pr ovide m anager s with ac c es s to power f u l anal yt i c a l s ys tem s and tools in a us er f r iendl y envir onm en t.  En t erp rise reso u rce p lan n in g ( ERP) is a c om pany- w id e c om puter s of twar e s ys tem us ed to manage and coordinate all the resources, information, and functions of a business from shared data stores.  O n l i n e a n a l yt i c a l p ro c e s s i n g , o r O L AP is a n a p p r o a c h t o q u ic k l y a n s we r m u lt i - d im e n s i o n a l analytical queries. OLAP is part of the broader category of business intelligence, which also enc om pas s es r elat ion a l r epor ti ng and data m ining. T he typ i c a l applic at i ons of O LAP ar e 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)  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 dim ens iona l ar r a ys have no phys i c a l m etaphor , but they or gani ze the data in the wa y us er s think of their enter pr is e . T ypi c a l enter pr is e dim ens ions ar e tim e, m eas ur es , pr oduc ts , geogr aphic a l r egions , s ales c hannels , etc . Synon y m s : Multi - d i m ens io na l Str uc tur e, Cube, Hyper c ub e  O L AP o p e r a t i o n s : Sl i c e , D i c e , D r i l l D o wn / U p , R o l l - u p , Pi vo t  See this site for all these definitions: AND DICE4/3/2013 Vassilis Moulakakis M.Sc. 18