Analytic next gen usecases - presented for ISB, Hyderabad
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How the big data platforms are empowering to 1) build market mix modeling and digital attribution and help make marketing budget decision 2) financial institutions to tackle new generation usecases.
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Analytic next gen usecases - presented for ISB, Hyderabad
- 1. Big data analytics empowering the Next Generation use-cases SANDEEP AKINAPELLI
- 2. Agenda Overview of analytics Use case 1: media mix marketing/multi touch attribution Use case 2: Banking & more applications Final thoughts
- 3. Types of analytics Descriptive Analytics Predictive Analytics Prescriptive analytics
- 4. Why should you do data science? • Axiom: More Data = Better Models -> Better Business Decisions • but requires new platforms • Business has ever-shorter expectations of “Time to Insights” • Across industries markets expect a “Tailored Experience” • customers expect companies to know them better • Competitive pressure • If you don’t do it your competitor will • Data sets to accomplish the above are getting bigger and more complicated • traditional BI / Analytics falls short
- 5. Trend Time = money Data = money Big Data = money Big unknowns = money ! . . Big connectivity = money!
- 6. Use case 1: media mix marketing
- 7. Media mix marketing problem
- 8. Traditional multi touch models Derive the relationship based on the past data Fit and train a model Use the model to predict/simulate Not evidence based. Correlation doesn’t mean casuation Does not drilling down to next levels
- 9. What makes you click on a Digital Ad?
- 11. User journey
- 12. User journey
- 13. User journey
- 14. User journey
- 15. User journey
- 16. Next gen multi touch models visualize Analyze/model transform Ingest Call center data Sales/marketing data
- 17. Problems at a hierarchy Chief marketing officer Print media manager Digital channel manager Campaign managers How should we amend the marketing budget How do I distribute the budget among various sub channels How do I maximize the ROI of my campaigns
- 18. © Cloudera, Inc. All rights reserved. Traditional Analytic Processing Pipeline ETL Data Warehouse Analytic Platform Staging ModelingData GovernanceGovernance Processing Acquisition Aggregate, Snapshot, Roll- up Datamart UDF Views HPC Modeling Aggregate, Snapshot, Roll- up
- 19. © Cloudera, Inc. All rights reserved. Data Science / Machine Learning Workflow Faster from Data ➢ Exploration ➢ Action – in ONE platform Data Engineering Data Science (Exploratory) Production (Operational) Data Wrangling Visualization and Analysis Model Training & Testing Production Data Pipelines Batch Scoring Online Scoring Serving Data GovernanceGovernance Processing Acquisition Reports, Dashboards Data Hub
- 20. Banking & More
- 21. Cloudera at a glance
- 23. Use case 2
- 24. Final thoughts
- 25. Extracting value from data – an untapped opportunity
- 26. Successful Hadoop Deployment Needs Faster & Easier Workload Offload Identify suitable workloads and provide recommendations for predictable offloads Big Data Architect Enable Peak Performance On Hadoop Understand usage to actively manage and optimize data models to enable peak performance Database Admin
- 27. Navigator Optimizer: Offload to Hadoop Predictably Select the right workloads to migrate • Visibility into existing workloads • Cost assessment per workload Understand workload requirements • Determine most common workload patterns • Develop data-driven offload strategy Reduce the offload cost • Complexity assessment with specific recommendations • Focus efforts by identifying duplication
- 28. Q & A