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Ravishankar P. Hariharan

EMERGING TRENDS & TECHNOLOGY IN BUSINESS INTELLIGENCE MARKET. Ravishankar P. Hariharan. Business Intelligence and Data Warehouse Definitions. Business Intelligence

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Ravishankar P. Hariharan

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  1. EMERGING TRENDS & TECHNOLOGY IN BUSINESS INTELLIGENCE MARKET Ravishankar P. Hariharan

  2. Business Intelligence and Data Warehouse Definitions Business Intelligence Capability of collecting and analyzing internal and external data to generate knowledge and value for the organization. This includes business process decision support at the strategic, tactical, and operational levels. Data Warehouse A database populated with data from business transactional systems optimized for retrieval of information providing value in the areas of business projection, market trend analysis, and cost minimization.

  3. Business IntelligenceToday • Is driven largely by Reporting • Data warehouse as foundation for BI is Established • Knowledge level of customer in BI expectation, is increasing • Tools established in ETL, Reporting, OLAP, Analytics, Data Mining. • BI Reporting is viewed different from ERP [SAP, Oracle] ,Source • system reporting and MIS reporting. • More IT Professionals are joining BI/DW stream .

  4. Still Untapped • Large number of industries are still unaware of BI (Especially in • Domestic Market) • BI being more than reporting is an opportunity • -Upgrading of system • Sophisticated BI delivery system like EIS, Analytics, Sensitivity • Analysis are yet to be scaled and has a huge impact on business • Limited user group implementation . • - Mindset from professionals • - Cultivate both Top down and Bottom up approach.

  5. Characteristics Of BI Market • Closely connected to business • Any change in business affects BI • ROI…. ROI…. ROI…. • Depends on data completeness • (Information across the enterprise) Local Business Environment Global Changes BI System Legal & Laws Structure of Company Composition of Manpower

  6. Top 5 Emerging Trends In BI • 1. Emergence of BI 2.0 and challenges • 2. Market Consolidation of Tools & Technology • Offering BI as service opportunity (SAAS Model) and • challenges • 4. Defining BI Delivery System • Analytics, Data Mart • 5. Quality of Information • Master Data Consolidation • KPI Definition for Management /System • Business Knowledge & Context • Identifying Critical Information

  7. Emerging Trend 1 Business Intelligence 2.0

  8. Characteristics of BI 2.0 Event driven Real time Automated analysis Forward looking Process oriented Scalable

  9. Real Time BI RTBI provides the same functionalities as the traditional business intelligence, but operates on data that is extracted from operational data sources with zero latency, and provides means to propagate actions back into business process in real-time Seamless transition from data into information into action RTBI needs automatic processes and intelligent systems (adding semantic web techniques and advanced analytics)

  10. Contd.. Reduce Cycle time – until real time From (extract, transform, load) ETL-Approach to OLAP and high performance analytics to ? E.g. Realtime Decisioning, In-Memory Analytics on 64Bit-Hardware, Enterprisewide Realtime CPM, BAM/ Realtime-BI, Advanced Analytics.

  11. Advantage & Disadvantages Action in almost real time. Advantage of operational reporting and analytic reporting. There will emerge new architecture and schema for storing / transmitting data. Challenges ? Not so easy. You could end up with messing the entire Information management in the organization. Accuracy could be hit. A wrong decision could emerge. Could just end up replicating operational system reporting. Short-Sighted implementations .

  12. Emerging Trend 2 Consolidation of Tools & Vendors in Market.

  13. Market consolidation Major players in IT IBM, Oracle, MS have been aggressively buying leading BI product firms. SAP bought BO to strengthen BI-front. Cognos bought out by IBM in a Mega deal. Oracle bought Hyperion, Siebel analytics to name few. Microsoft releases BI-Platform. Why ? Huge market potential. Oracle not too successful with custom developed products in BI SAP – BW had strong architecture but weak concepts. MS had been silently away in the Past.

  14. List Of Major BI Tools

  15. Contd..

  16. Leaving 3 categories of vendors (Gartner) Vendor Categories Up-and-Comers Megavendors Pure Plays Open Text Informatica Teradata SAS Microsoft Oracle SAP IBM Targeting specific market segments Innovative solutions Disruptive Technologies

  17. Vendors & Products Vendors Technologies • MDM Siperian, Initiate • Data Quality and Governance Group1, Back Office, Silvercreek, SAP-BOBJ, Informatica, Trillium, IHS-Intermat, Utopia • Enterprise Search Endeca • Real time ETL Radware • Data Archiving SAND • Data Visualization AVS • Query and Usage Management Applfuent • Rules Engines ILOG • DW Appliances Netezza • DW Infrastructure Egenera • Data Mapping and Analysis Exeros • Unstructured Data Itemfield • Opensource Pentaho, Talend • BAM Celequest • EII (Enterprise Inf. Integration) Composite, Metamatrix, Sybase Awaki • Information Mobility Sybase • DW/BI DBMS Sybase IQ • Object Oriented Architecture Objectriver • Predictive Analytics Tech Labs evaluated KXEN, SPSS, and SAS

  18. Emerging Trend 3 Offering BI as Service

  19. BI as Service The Software as a Service (SaaS) Model is the combination of a Business Model and a Software Delivery Model When does it arise for BI? Small to medium customers requiring BI service Reporting service to analytical service Insight services Service require larger framework than the organization can afford Expert availability: -BI Expert, Statisticians, Data mining expert, Subject matter expert

  20. Emerging Trend 4 Defining BI delivery system

  21. BI Delivery system ----- Analytics You have to bring the same rigor you bring to operations and finance to the analysis . - Rupert Bader, Director - Workforce planning at Microsoft (MSFT)

  22. BI Delivery system ----- Analytics Where does analytics stand? Do you want to give analytic solution to all the key customers? Is there a shape and focus to the analytic system? If analytics is Multi-dimensional analysis, Predictive analysis , data mining and so on …. Which is best suited for a particular function? We could possibly classify our offerings.

  23. Defining Analytics • Enterprise analytics • Functional analytics • Workforce analytics • Rapid analytics • Financial analytics • Analytics as a whole solution • Analytics as a spearhead solution

  24. Intelligence Grading. Reports. Standard reports. Drill down reports. Alert. Ad-hoc reports Analytics? Statistical analysis Predictive modelling Forecasting. Optimization.

  25. Organizations have the opportunity to employ analytics in a way that drives better value from their system. Optimization (What’s the best that can happen?) Forecasting / extrapolation (What if these trends continue?) Predictive modeling (What will happen next?) Analytics Statistical analysis (Why is this happening?) Competitive Advantage Ad hoc reports (How many, how often, where?) Reporting Alerts (What actions are needed?) Query / drill down (Where exactly is the problem?) Standard reports reports (What happened?) Sophistication of Intelligence 25

  26. Emerging Trend 5 Focus on Information Quality

  27. Data Transformation Discrete Data (Not Clean) Cleaned Data Integrated Data Net SAP Public Domain Budget Ex: ERP or ODS Ex: Master Data Mgmt

  28. Contd.. Meaningful Data Valuable Info Critical Info KPI Facts & Measures Ex: Building DM/DW Ex: Business Consulting IM Consulting Metric Mgmt Ex: Fast Moving Info Sensitive Info High Impact KPI

  29. Talent Management Initiatives have produced a positive impact on business performance 29

  30. Define your key information level. Facts and Measures Key Performance Indicator Metrics External ratios, Perspective Your Models(EBDITA, DOW JONES, FKTM) Scalable factors.

  31. Information ManagementSpecialty Domains & Core Competencies Information Management Specialty Domains Data Management & Architecture Portal Business Intelligence BI Governance Portal Data Quality Enterprise Search Master Data Management BI & DW Architecture Collaboration Meta Data Management Data Integration Data Movement & Replication Reporting & Analysis Enterprise ContentManagement Strategic Intelligence Document Management Analytics & Discovery Web Content Management Performance Management Transactional Management

  32. Q & A

  33. THANK YOU

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