big data and bi best practices slidedeck

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Post on 19-Jan-2015




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DESCRIPTION Watch Glen Rabie, CEO of Yellowfin, and Fred Gallahger, GM of Actian Vectorwise take you through 7 of the Best Practices for Big Data and BI.


  • 1. Big Data and BIBest Practices

2. Your presenters Year| LastYellowfin CEO, Glen RabieGeneral Manager, Actian Vectorwise,Fred Gallagher 3. About Actian and YellowfinMaking Business Intelligence easy Taking Action on Big DataHistory of 100GB TPC-H Performance Benchmarks Composite Queries Per Hour (Non-Clustered)500,000.00400,000.00 [email protected],000.00200,000.00100,000.00-Non-Vectorwise Vectorwise 4. Data isthe newoilDavid McCandlesData Journalist 5. The rise and rise of Big Data 6. There has always been Big Data Its just that now we can actually capture and mine it effectively.Canadian Tar Fields 7. Not all Big Data is created EqualPlanet Google and friends are the outliers Large Telco The Norm . 8. Do you have a Big Data problem? 9. Big Data for Everyone Big data is not just for data scientists and bespokeprojects Its for decision makers and data consumers It needs to be anchored in the real worldAnalystConsumers 10. Who is benefitting from Big Data? 11. Who is benefitting from Big Data? 12. Why bother with Big Data?of organizations collect60% more data than they caneffectively use(MIT Sloan Management Review) 13. Why bother with Big Data?of organizations see70% Big Data as a bigbusiness opportunity(Harris Interactive)of organizations investing70% in Big Data initiativesexpect ROI within 1 year(Harris Interactive) 14. Why bother with Big Data?of organizations that84% actively leverage Big Datasay they can now makebetter decisions(Avanade) 15. Best Practices in Big DataFred Gallagher, Actian Corporation 16. What is Big Data? 17. Best Practice #1 Focus on what you want to achieve 18. Its all about driving value 19. Big Data Levers1. Personalization2. Social3. Search4. Find opportunities5. Actionable Insights 20. Best Practice #2 Identify the data you havevsThe data you need 21. Does your data match what you wantto achieve? 22. What data do you need? 23. Best Practice #3Use the right Big Data tool for the job 24. Big Data and Hadoop 25. Big Data Eco-systemSocialMediaAnalytic HadoopDatabases Storage BIG Search DATANewSQLas-a-serviceNoSQLDocumentOperationalBigTable Database Key ValueGraph 26. Best Practice #4 Use a fast database 27. Slow Query Performance is the#1 issue in BIBI Survey 10: Why BI Projects Fail?1. Query Performance Too SlowTDWI Best Practices Report45% Poor Query Response the top problem that will eventuallydrive users to replace their current data warehouse platform.Gartner Magic Quadrant Data Warehousing70% of data warehouses experience performanceconstrained issues of various types 28. User ExpectationsWeb-BasedBusiness IntelligenceUsers expect results inless than 10 secondsMobile BIUsers expect results in less than 3 seconds 29. Use a fast databaseTraditional Database Analytical Database Clustered Database 30. Consider the hidden costsSpend Less on HardwareGet faster results on smallerhardware configurations.Spend Less TimeDatabase Tuning Faster deployment and BI projects. No more aggregates,cubes, complex schemas, etc 31. Best Practice #5Plan for a mixedarchitecture 32. Hadoop and BI architectureHadoopTransactionalFast Database BI ToolExternal 33. Best Practice #6Ensure mass distribution ofyour data 34. Big Data for EveryoneVisualizations AlertsAccess Anywhere 35. Best Practice #7Tailor data delivery toeach audience 36. Give your audience what they wantDemographics Interactive ReportsStatisticsKPIs MapsCollaboration 37. Visualization is powerful Looks like Pac-man Does not look like Pac-man16941 Looks like Pac-man Does not look like Pac-man 38. Big Data Visualization Tips More data requires more focus Interactivity is essential Select the right metrics Provide context Support and prompt action 39. Demonstration 40. Big Data and BI Best Practices1. Focus on what you want to achieve2. Identify the data you have vs The data you need3. Use the right Big Data tool for the job4. Use a fast database5. Plan for a mixed architecture6. Ensure mass distribution of your data7. Tailor data delivery to each audience 41. ConclusionQuestions 42. | Last Year More[email protected]@ActianCorpYellowfin LinkedIn User Group Vectorwise LinkedIn User Group