simon elisha @ pivotal

2 © 2014 Pivotal Software, Inc. All rights reserved. 2 © 2014 Pivotal Software, Inc. All rights reserved.

Connecting Data with Customers Bridging the Gap Between Data Science & Customers

Simon Elisha, CTO & Director of Field Engineering, A/NZ @simon_elisha

3 © 2014 Pivotal Software, Inc. All rights reserved.

Welcome to the Age of the Customer

Source: Forrester Research Inc.

4 © 2014 Pivotal Software, Inc. All rights reserved. 4 © 2014 Pivotal Software, Inc. All rights reserved.

Title in Upper & LC Bold Type Subtitle 24 Point Arial Title Case

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Race to Deliver Consumer-like App Experiences

  Enterprise mobile apps must be “consumer-grade”

–  Simple, clean, intuitive UI –  High performance –  Built with modern technology stack


Mobile driving the explosion of data

iPhone 5s sensors 1.  Proximity 2.  Ambient light 3.  Gyroscopic 4.  Accelerometer 5.  Magnetometer 6.  Fingerprint 7.  Front Camera 8.  Rear Camera 9.  GPS 10.  WiFi 11.  Bluetooth 12.  Cellular radio 13.  Microphone

Galaxy S5 sensors 1.  Proximity 2.  Ambient light 3.  Gyroscopic 4.  Accelerometer 5.  Barometer 6.  Magnetometer 7.  Fingerprint 8.  Hall (cover) 9.  Heart rate 10.  Gesture 11.  Front Camera 12.  Rear Camera 13.  GPS 14.  WiFi 15.  Bluetooth 16.  Cellular radio 17.  Microphone

Constant data collection


Big Data Utility Gap

70% of data generated by

customers

80% of data being stored

3% being prepared for

analysis

0.5% being analyzed

<0.5% being operationalized

3 Exabytes per day now

40 Trillion total Gigabytes in 2020

First Movers

Smart Enterprises

~20X $2.9B

~30X$4B

~7X $290B

~20X $120B

Average Enterprises

(Or 162 iPhones of storage for every human)


Connecting Customers with Data = Data Operationalization


The 4 Rules of Data Operationalization

1.  Make it easy to develop models 2.  Make it easy to insert models into applications 3.  Make it easy for models in applications to access

data sources 4.  Make it easy to regularly update the models/

applications


What’s Stopping Most Companies from doing this?

• Data in Silos

• Slow, Reactive Analytics

• Slow IT and App Dev Processes


Slow, Reactive Analytics

•  It’s expensive •  Enhancements and projects take too long •  It drives people to create their own “data hideouts”


Data Lake Architecture Ingestion

Tier Insights

Tier Unified Operations Tier

System monitoring System management

Unified Data Management Tier Data mgmt.

services MDM RDM

Audit and policy mgmt.

Processing Tier

Workflow management

Distillation Tier

HDFS storage Unstructured and structured data

In-memory MPP database

Real-time

Micro batch

Mega batch

SQL NoSQL

SQL MapReduce

Query interfaces

SQL

Sources Action Tier

Real-time ingestion

Micro batch ingestion

Batch ingestion

Real-time insights

Interactive insights

Batch insights


YOUR DATA

DATA SCIENCE

+!= MODELS


Drive Automated

Low Latency Actions

Production Data Feeds

Low Latency Model

Scoring

API Availability or Push to

Apps

Business Logic

Application Response

New Events

(aka, Data) Model Operationalization

(“O16N”)


Data Science Value Chain

Product Engineer

Data Engineer DBA Data

Engineer Data

Engineer Data Scientist

Data Engineer

Application Developer PMO

•  Many parties/skills involved •  Sponsorship from the top or it will not happen •  Ideas/initiatives typically from LoB leadership


High

Future Past TIME

BUSINESS VALUE Business

Intelligence

Predictive Analytics & Data Mining (Data Science) Typical Techniques & Data Types

•  Optimization, predictive modeling, forecasting, statistical analysis

•  Structured/unstructured data, many types of sources, very large data sets

Common Questions

•  What if…..? •  What’s the optimal scenario for our business ? •  What will happen next? What if these trends continue?

Why is this happening?

Business Intelligence Typical Techniques & Data Types

•  Standard and ad hoc reporting, dashboards, alerts, queries, details on demand

•  Structured data, traditional sources, manageable data sets

Common Questions

•  What happened last quarter? •  How many did we sell? •  Where is the problem? In which situations?

Data Science

Low

Data Science Goes Further Than BI


MADlib In-Database Functions

Predictive Modeling Library

Linear Systems •  Sparse and Dense Solvers

Matrix Factorization •  Single Value Decomposition (SVD) •  Low-Rank

Generalized Linear Models •  Linear Regression •  Logistic Regression •  Multinomial Logistic Regression •  Cox Proportional Hazards •  Regression •  Elastic Net Regularization •  Sandwich Estimators (Huber white,

clustered, marginal effects)

Machine Learning Algorithms •  Principal Component Analysis (PCA) •  Association Rules (Affinity Analysis, Market

Basket) •  Topic Modeling (Parallel LDA) •  Decision Trees •  Ensemble Learners (Random Forests) •  Support Vector Machines •  Time series modeling (ARIMA) •  Clustering (K-means) •  Cross Validation, CRF

Descriptive Statistics

Sketch-based Estimators •  CountMin (Cormode-

Muthukrishnan) •  FM (Flajolet-Martin) •  MFV (Most Frequent

Values) Correlation Summary

Support Modules

Array Operations Sparse Vectors Random Sampling Probability Functions


Data Gravity: Send the Model to the Data

Data Load (Limited by workstation)

Model Runs on Database


Improve Company

Performance

Increase Revenue

Increase Demand

Increase Ad Spend Lift

Ad Effectiveness

Analytics

Market Mix Modeling

Short-term Media

TPM Coupon

redemption

Increase Customer Acquisition

& Loyalty

Segmentation & Targeting

Store Clustering Churn

Prevention Loyalty Program

Analytics Customer

Lifetime Value Customer

Satisfaction Customer Care

Analytics

Increase Reach

Brick & Mortar Site selection

analysis Online

Digital Marketing

Social Media eComm

Optimization

Increase Basket Size

Affinity Analysis

Next Best Offer Cross-sell/Upsell

Store Experimentation

Manage Demand

Demand Analysis

Price Optimization

Markdown Planning

Assortment Optimization

Shelf Space Optimization

Build Brand Equity

Focus Group Research

Social Media Analytics

Reduce Costs

Supply Chain Management

Route Optimization Distribution Inventory

Optimization

Theft Prevention

Supply / Demand

Forecasting

Procurement Optimization

Brick & Mortar POS Anomaly

Det.

Image Analytics Online

Transaction Anomaly Detection

Risk Modeling

Risk Modeling Health & Safety

General & Administrative

Workforce Analytics Employee

Churn IT/Security Analytics

Sample Retail Solutions Tree


Storefront Advertisement Targeting Customer

A major consumer service provider

Business Problem

Use digital displays in storefronts to optimize imagery based on the likely preferences of nearby shoppers

Challenges

•  Joining real-time location data with an accurate individual subscriber preference model

Solution

•  Build a per-customer preference model based on his or her service usage history

•  Data preparation to easily detect boundaries of physical points-of-presence

•  Real-time optimization of group preferences and display of imagery most likely to correlate to store entry


Cross-Channel Customer Engagement Customer

A major health insurance company

Business Problem

As each call to the call center represents a significant cost to the company, find out when customers are using the call center instead of the website

Challenges

▪  Unstructured text data requires considerable preprocessing

Solution

▪  Used logistic regression to predict whether a customer would be unable to find their information on the web and need to call in

▪  Created a topic model based on the call logs to learn what these customers were calling about, since these would be the topics they were having trouble finding on the website


Loyalty Program Analysis Customer

POC for a global oil and gas company

Business Problem

Understand consumer behavior and identifying cross-sell/up-sell opportunities from loyalty card program.

Challenges

•  Limited consumer view and metadata

•  Difficult to link separate items purchased together as part of a single transaction

Solution

•  Identified different types of loyalty card holders based on their brand preference and average spend

•  Derived recommendations for the retail shops of the gas station network based on previous user purchases


Real-time alert system for high-risk respiratory patients Customer

Large vertically-integrated healthcare provider

Business Problem

The cost of treating respiratory patients is greatly increased by urgent care visits

Challenges

Identify urgent care risk factors and propose interventions to reduce risk of urgent care visit.

Solution •  Predict risk of acute care encounter using

prescription refill history, air quality data, and socioeconomic indicators.

•  Build a real-time application for alerting patients and physicians to take preventative action


Traditional App Lifecycle: Results in Lost Opportunities

Potential lost revenue opportunity

140 days to land an app

75 steps, 9% automated

Example: Intel’s traditional app lifecycle

Traditional App Lifecycle: Each phase compounds time and complexity, sacrificing agility

Traditional App Deployment: Many manual steps, developers out of the workflow

Example: Rakuten’s traditional app deployment process

Request Servers

Wait for hardware/VM creation

Setup Operating System

Install Software

Setup monitoring

Deploy website

Check / Test

Add to Load Balancer

...

Developer Operators


New Demands Driving Changing Dev & Release Practices   Agile

–  Frequent releases of fewer features, closer team interactions

  DevOps –  Culture of devs and ops working closely together

  Continuous Delivery –  Frequent releases, fewer features, highly

automated processes **Enterprise initiatives in 2014*Source: Xebia Labs Survey Report

DevOps Continuous Delivery Agile

50

40

30

20

0

10

60


Get the Model Into the App!! Remember the Back-

End!


“Jumping the Gap” Operationalizing Data

Data Team Applications Team


Data Science Value Chain

Product Engineer

Data Engineer DBA Data

Engineer Data

Engineer Data Scientist

Data Engineer

Application Developer PMO

Constant feedback & refinement A cycle, not a “one time” event

Multiple Times a Week/Day


Mobile Changes the way you Deploy   Mobile has resulted in exponential workload increases and highly

dynamic loads

  User expectation is no downtime for upgrades / maintenance

  Mobile device / OS speed iteration is faster than ever before

Client / Server OS 5 year cycle

Mainframe OS 10+ year cycle

Mobile OS 1 year cycle


The Power of PaaS (On Premise & Off Premise)

Traditional IT

Storage

Servers

Networking

O/S

Middleware

Virtualization

Data

Applications

Runtime

You

Man

age

IaaS

Storage

Servers

Networking

Middleware

Virtualization

Data

Applications

Runtime Yo

u M

anag

e

IaaS

O/S

PaaS

Storage

Servers

Networking

Middleware

Virtualization

Data

Applications

Runtime

Pivotal CF +

Choice of IaaS

Business Value, Agility & Cost Savings

You

Man

age


Rapid Innovation Requires a Combined Approach

 Dramatically improve developer experience

 Agile teams, rapid iteration  Microservices, incubate open source

advancements (data and apps)

 Continuous delivery, no planned downtime

  Instant scaling of apps and data services

 Automation and deployment consistency at every step

DEVELOPERS OPERATORS


Major sports brand creates immersive fan experience •  In-app data, beacons, and legacy system data revealed new business insights •  Measure linger-time, video watching habits, create custom offers at events •  Elastically scalable to peak traffic of > 20 million notifications per minute


Parting Big Data Thought

 Big Data is not always big – but it can be awkward

3 PB of Data

Single table with 20 Billion Rows

Vs


The 4 Rules of Data Operationalization 1.  Make it easy to develop models

–  Right tools, right data, right people

2.  Make it easy to insert models into applications –  Development process, deployment process, platform

3.  Make it easy for models in applications to access data sources –  Binding to performant data sources, standard interfaces (e.g. SQL)

4.  Make it easy to regularly update the models/applications –  Agile/DevOps/Continuous Delivery

simon elisha @ pivotal

Data & Analytics

pivotal software

right data

parting big data

performant data sources

data sources binding

explosion of data iphone

big data utility gap

pb of data single table