Blueprint for integrating big data analytics and bi
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The document discusses integrating big data analytics and business intelligence (BI). It outlines 6 cornerstones for making Hadoop deployments successful: employing all data, using all analytic assets, providing self-service access, building collaboration, being open and extensible, and populating best-of-breed reporting tools. Big data analytics can optimize businesses by asking any question of large, diverse data sources, while traditional BI focuses on reporting structured data to answer specific questions. The best approach combines the strengths of both.
Blueprint for integrating big data analytics and bi
- 1. Big Data Insight Blueprint for Integrating Big Data Analytics and BI Abe Taha, VP Engineering abetaha@karmasphere.com www.karmasphere.com
- 2. Big Data Insight >⯠Agenda ĂŒïŒâŻ Where does Big Data Analytics fit in the BI ecosystem ĂŒïŒâŻ How does Big Data Analytics complement the type of analysis we do today using BI ĂŒïŒâŻ What are clients doing with Big Data Analytics that they couldnât do with BI ĂŒïŒâŻ What do we need to think about to make Hadoop deployments successful 2 Karmasphere Proprietary and Confidential. Do Not Copy. Do Not Distribute
- 3. Big Data Insight >⯠Hadoop not standing alone
- 4. Big Data Insight >⯠Parallel and Complementary Stacks
- 5. Big Data Insight >⯠The Best of Both Worlds = Big Data Analytics + Traditional BI Traditional BI Big Data Analytics Purpose Reporting on business Optimizing the business Paradigm Ask a specific question Ask any question Format Look at structured data Look at all data Setup Pre-engineered On-the-fly Data locations Siloed One place Agility Weeks to months Almost Immediate 5 Karmasphere Proprietary and Confidential. Do Not Copy. Do Not Distribute
- 6. Big Data Insight >⯠Big Data Analytics on Hadoop Use Cases Product Optimization âąâŻ Insight to usage patterns, bug paths, quality outages âąâŻ Outline new features, improve product roadmap and process âąâŻ Enhance customer service, quality and product âstickinessâ Unified Customer View âąâŻ Insight to correlations across product lines and interaction channels âąâŻ Personalize oïŹers, services and customer experience âąâŻ Reduce churn and increase customer satisfaction Marketing Performance âąâŻ Insight to market program attribution and ROI âąâŻ Increase customer targeting through micro-segmentation âąâŻ Optimize online ads and cross channel programs 6 © Karmasphere 2012
- 7. Big Data Insight >⯠What Hadoop Adopters Are Saying âThe kind of new stuïŹ we want to do canât get done with BIâ Large Hi Tech Chip Manufacturer 7 Karmasphere Proprietary and Confidential. Do Not Copy. Do Not Distribute
- 8. Big Data Insight >⯠How to make Hadoop successful with BI 1.⯠Employ All Data 2.⯠Use All Analytic Assets 3.⯠Provide Self-Service Access for All Users 4.⯠Build a Collaborative Environment 5.⯠Be Open and Extensible 6.⯠Populate Best-of-Breed Reporting Tools
- 9. Big Data Insight >⯠Cornerstone 1: Employ All Data ĂŒïŒâŻ Leave No Data Behind âąâŻ Raw unstructured â Web logs, machine / sensor data, mobile social, video, etc. âąâŻ Structured data â traditional RDMBS, EDWâs âąâŻ Streaming vs. batch oriented âąâŻ Data governance and quality
- 10. Big Data Insight >⯠Cornerstone 2: Use All Analytic Assets ĂŒïŒâŻ Employ All Analytic Assets âąâŻ Traditional models and assets âąâŻ Standard Hadoop components including UDFs and SerDes âąâŻ Custom algorithms âąâŻ Models created in other systems such as SAS/R
- 11. Big Data Insight >⯠Cornerstone 3: Provide Self-Service Access for All Users ĂŒïŒâŻ Self-Service âąâŻ BYOD: Bring Your Own Data âąâŻ Ingest custom functions and algorithms âąâŻ Intuitive, no special skill sets required ĂŒïŒâŻ Empower All Users and Skill Sets âąâŻ Business User âąâŻ Easy-to-use ad-hoc analysis, web-based forms âąâŻ Drag and drop âąâŻ Data Analysts âąâŻ Common skills: SQL âąâŻ Powerful iterative analysis âąâŻ Analytical models and algorithms âąâŻ Customers and Partners for ecosystem
- 12. Big Data Insight >⯠Cornerstone 4: Build a Collaborative Environment ĂŒïŒâŻ Collaborative âąâŻ Project-based environment âąâŻ Leverage cross-functional skills âąâŻ Security and isolation ĂŒïŒâŻ Social âąâŻ Share data and insights across teams âąâŻ Metadata, Queries, Results and Visualizations âąâŻ View colleagueâs activities âąâŻ Usage feedback and metrics
- 13. Big Data Insight >⯠Cornerstone 5: Be Open and Extensible ĂŒïŒâŻ Open âąâŻ Active community, rapid innovation âąâŻ Vendor commitment âąâŻ Standards based âąâŻ Portable - No vendor lock-in âąâŻ Expose standard APIâs and interfaces ĂŒïŒâŻ Extensible âąâŻ Add custom functions âąâŻ Reuse existing analytic models âąâŻ Add additional data sources by defining custom parsers
- 14. Big Data Insight >⯠Cornerstone 6: Populate Best-of-Breed Reporting Tools ĂŒïŒâŻ Best-Of-Breed Reporting tools âąâŻ Ingest data from existing BI systems and ad hoc data including Spreadsheet data âąâŻ Automate delivery of insights âąâŻ Push insights to RDBMS, EDWâs and MPP âąâŻ Expose standards APIs for programmability
- 15. Big Data Insight >⯠How would an architecture look 15 Karmasphere Proprietary and Confidential. Do Not Copy. Do Not Distribute
- 16. Big Data Insight >⯠Summary 1.⯠Implement Big Data Analytics and BI co-existence Hadoop at your fingertips 2.⯠Leverage all your assets 3.⯠Use and build on open and extensible solutions across your company⊠4.⯠Build social and collaborative in early  Private and Confidential
- 17. Big Data Insight >⯠Summary Get the Best of Both Worlds â Build a Bridge Inside Your Company Big Data Analytics on Hadoop Future, see intent Drives Optimization BI Just getting started Historical Drives reporting Entrenched Be around for a long time