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Venkatesh Sellappa (Venky)

Solutions Engineer

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The Quant Workbench

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Matlab

SAS

Java/C++Python

R

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Excel Is King

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Trial And Error

Data Ingestion

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The Project

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Security Monitoring

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The Solution ?DS X Workbench

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Use Case

All industries are effected by churn.

Being able to predict churn helps companies take action and keep customers longer.

The more historical data, the better the model

Data collected and labeled over time based on churn.

Using a Random Forest we will predict future churners.

Customer Churn Architecture

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Demo ScenarioAssessing Customer Churn Probability in Real Time

Stored long term data on customer churn behavior

New real time data coming in

Predict a customers churn probability before they churn

Alert the proper departments | manager

Business monitors customer retention outlook & performance

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Demo FlowInsights from Data Science to Production

Data ScientistsWhere is the data I need to answer the business questions?

Business Users Where is the insight & predictions from the data?

HDP Cluster

Kno

x

AdminsHow do I meet SLA, Performance, .., Feature needs?

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What Have We Learnt

It Wasn’t My Fault

Model Data Lifecycle

Collaborative WorkSpace

Bring Your Own Tool

Built-In Security

Real Time Monitoring

vsellappa@hortonworks.com