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.

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?

10. Conversion: User journey

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