EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS

EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS

Marcia Kaufman, Principal Analyst, Hurwitz & Associates Dan Kirsch, Senior Analyst, Hurwitz & Associates

Steve Stover, Sr. Director, Product Management, Predixion Software

About Us

Hurwitz & Associates -- a strategy consulting, market research and analyst firm focusing on how technology solutions solve real world customer problems.

Marcia Kaufman, COO & Principal Analyst

Coauthor of 6 books including “Big Data for Dummies”

and “Cognitive Computing and Big Data Analytics”

Dan Kirsch, Principal Analyst

Coauthor of “Hybrid Cloud for Dummies” and industry analyst

Agenda

 Preparing for Business Change

 Hurwitz & Associates 2014 Victory Index for Advanced Analytics

 Research Findings & Top Trends in Advanced Analytics

 Predixion Victory Index Results

 Customer Use Case

Are you Prepared for Business Change?

Finding Opportunity in Sensor and Device Data

 Can you combine sensor data with internal and third party data to predict machine failures and improve performance?

 Are you reducing fraud by looking for patterns between point of sale (POS) systems, enterprise data sources and demographic data?

 Can you improve logistics by analyzing sensor data on shipping equipment in combination with weather, traffic and enterprise data sources?

Hurwitz Victory Index: Advanced Analytics

 Assessed advanced analytics offerings of 10 vendors

 Surveyed over 450 end users on business and technical value

 Performed in-depth customer interviews

 Researched customer use cases and best practices

 Identified key market trends

 Rated advanced analytics

vendors: Vision, Viability,

Validity, & Value

Defining Advanced Analytics

 Statistical or data mining solution

 Algorithms and techniques used on structured or unstructured data to predict outcomes

 Most commonly used techniques

 Decision trees

 Linear Regression

 Time series models

 Cluster analysis

 Logistic regression

 Neural networks

 New generation of analytics solutions can hide complexity and add automation

Key Findings: End User Survey

 Users finding and

analyzing patterns in unstructured big data sources (i.e. machine sensor, social media)

 Users highly satisfied with vendor

 Challenges: lack of analytical skills,

integration with other

software, support for in-

database capabilities

Top Trends: Advanced Analytics Victory Index

 Analytics platforms becoming more accessible to business users

 Visualizations have become a key way to explore data and interpret analytics results

 Data sources larger and more diverse increasing

demand for more computational power and in-database capabilities

 Increasing demand for pre-packaged analytics solutions

 Real-time analysis of sensor and machine data used to anticipate problems and take corrective action

Defining the Internet of Things (IoT)

 Network of physical objects

 Instrumented with sensors and/or software

 When connected, these “things” achieve greater value by communicating with each other or with other data sources

 Challenges of IoT data from devices and sensors:

 Complex in terms of type (lack of standards)

 Expanding exponentially

 Flowing at a high rate of speed

Using Analytics to Leverage IoT Data

 Monitor temperature, pressure, moisture

 Develop predictive models

 Detect and analyze hidden patterns and anomalies in sensor and machine data

 Common IoT use cases

 Predictive maintenance

 Customized healthcare recommendations

 Streamlined logistics

 More responsive IT security

 Efficient delivery of public utilities

Go to Market Strength – Predixion has made rapid progress

since its founding. The company built upon its initial success with healthcare to support other industries and use cases including fraud detection, preventative maintenance, and marketing

optimization amongst others.

Customer Experience Strength – Customers like Predixion’s fast time to benefit as well as the integration with R. Customers felt that Predixion’s platform was much more approachable than traditional analytics offerings.

Predixion – A Challenger in the Hurwitz Victory Index

Customer Example: Predictive Maintenance

 Predictive Maintenance with the Internet of Things– Intervening to Prevent Oil Rig Failure

 Challenge:

 A large energy company depends on expensive oil rig equipment to produce revenue

 Equipment failure means that operations must stop

 Unnecessary maintenance also takes equipment out of use

 Solution:

 The company used Predixion to build predictive models using sensor data and oil rig maintenance logs – solution created in 3 weeks

 Sensors on oil rig components monitor pressure, temperature, vibration

 Thousands of scoring predictions are made per second on oil rig components

 Field maintenance and operations teams leverage analytics in real time

 Key Benefit:

 Identify an oil rig failure 14 days before it was set to occur and take

What Users Said About Predixion

 “Predixion has been able to differentiate itself by making an accessible tool with a fast learning

curve and intuitive data visualization approach.”

Predixion Partner in the Life Sciences Space

 “The Predixion relationship is much more like a partnership than a traditional vendor

relationship.”

Predixion Healthcare Customer

Over 20 years of experience in Big Data and

Analytics, Cloud, and Systems Management markets at market leading companies like Teradata, Red Hat, and Dell.

Led the launch and growth of successful product lines for Cloudera, Oracle, SAP, OpenStack,

Microsoft and VMware.

Steve Stover, Sr. Director of Product Management

• Founded in 2009; HQ in California

• Strategic investors include Software AG, Accenture, GE and EMC

• Expert services team with PhDs in Data Science, Statistics and related fields

PARTNERS CUSTOMERS

About Predixion Software

Predixion named “key player” in Healthcare Analytics Market Forecast (2014)

Predixion included in Big Data 50 – the hottest Big Data startups of 2014

Predixion recognized as a Challenger in Advanced Analytics Market (2014) Predixion debuts on Gartner Magic Quadrant

for Advanced Analytics (2015)

Predixion debuts on Forrester WAVE forBig Data Predictive Analytics (2015)

Industry Recognition

Sources: ¹Goldman Sachs, ²Cisco , ³IDC

Devices connected by 2020¹

Generated by the IoT 2013-2020³ CAGR for M2M market¹

12B-50B

Devices connected by 2020²

44EB-4400EB

Generated by the IoT 2013-2020³

40X 60X

Processing Cost¹ Bandwidth Cost¹

4,400 exabytes = 4,400 billion terabytes

Driving Forces: The IoT Data Deluge

• Optimize key processes:

⁻ Supply chains

⁻ Field operations

⁻ Utilization or consumption

⁻ Customer experience

⁻ Treatment plans

• Preserve the value of equipment by avoiding costly failures

• Automate actions

• Create new opportunities from greater insights into your IoT data

Predict Act

Why Predictive Analytics for IoT data?

 Segmentation

 Offer Recommendation

 Predictive Lead Scoring

 Customer Churn

 Retailer Fraud

 Loyalty Fraud

 Readmissions

 Length of Stay

 Population Health

 Claims Fraud

 Medication Adherence

 Disease Progression

 Remote Patient Monitoring

 Predictive Maintenance

 Field Service Optimization

 Inventory Optimization

 Predictive Maintenance

 Driver Attrition

 Driver Incident Risk

ENERGY, UTILITIES & MANUFACTURING HEALTHCARE & LIFE SCIENCES

RETAIL & MARKETING TRANSPORTATION

Predictive Analytics Use Cases

Challenges with Predictive Analytics for the IoT

• Traditional analytics tools cannot handle the volume or speed of IoT data at the edge

• Many devices and machines are in remote areas with limited or periodic connectivity

• Aggregating or filtering the data and forwarding it creates data blind spots

• Critical use cases require real time actions performed at the edge

Taking Action in the IoT – Who, What, and When

Seconds Hours Days Weeks

Executives

Management

Staff

Systems

O P E R A T I O N A L

• Expand capacity

• Alter product line

• Introduce new service

• Schedule maintenance

• Change policies

• Order inventory

• Implement driver improvement

• Repair now

• Schedule patient visit

• Extend production

T A C T I C A L

S T R A T E G I C

• Orderly shutdown

• Next best offer

Predixion provides real-time, predictive analytics at the decision point to improve outcomes.

ACT

API & SDK SOLUTION ACCELERATOR

PREDICT ANYWHERE ANALYZE & MODEL

DATA FROM ANYWHERE

What We Do

CLOUD

SENSORS SMART DEVICES ON DEVICE, IN REAL TIME

• Alerts on both connected & disconnected devices

• Act before costly failures occur

• Automate corrective actions and efficiency optimizations

AT AGGREGATION POINT

• Ideal when on-device deployment is less feasible

• Avoid time lag, data 'blind spots'

• Quickly and efficiently schedule local services

AT CENTRALIZATION POINT

• Enables a broad across the business

• Uncover new business opportunities

• Create new processes and policies

• view to cost optimize

We are the only predictive analytics solution that can run on the device, on the gateway and in the cloud.

Predictive Analytics for the IoT: Where and When You Need it

MODEL CREATIONDEPLOYMENT

DATA

REAL-TIME OR BATCH DATA

• Speed to value

• Portable analytic workflow

• Easy to embed

• Easy to update

RUNTIME

RESULTS

JAVA Combine

Shape Build

Compare Visualize

Collaborate

THE LAST MILE OF ANALYTICS

Patent-Pending MLSM Provides Value in Model Creation & Deployment

Embed Easily into Existing Applications or Create your Own

Portable Web Applications Interactive Mobile Apps

Embedded CRM

Dashboard Integration

Solution Accelerator Embedded IoT

PREDIXION SOLUTION

• Reduce downtime

• Optimize inventory

• Optimize deployment of your technicians

THE CHALLENGE

DESIRED OUTCOMES

• Train the predictive model with sensor data

• MLSM pushed in real time in CEP engine

• 1000s of predictions scored per second in memory with near zero latency

• Predicts individual part failure on individual trucks in advance

• Pushes alerts into Accenture end- to-end fleet management solution

• Quick and easy to embed in a variety of environments

• Can Predixon predict part failures on a large fleet and embed into an end to end fleet management

solution?

Capabilities Proof Point: Predictive Maintenance on Large Fleets

PREDIXION SOLUTION

• Reduce costly failures

• Optimize part inventory

• Optimize field service dispatch

THE CHALLENGE

DESIRED OUTCOMES

• Train predictive models using sensor data

• MLSM Package pushed in real time in CEP engine

• 1000s of predictions scored per second in memory with near zero latency

• Predicts part failure in advance

• Pushes alerts to user friendly UI for action

• Quick and easy to deploy web- based app with our Solution Accelerator

• Can Predixion detect an oil rig failure up to 14 days in advance by analyzing the streaming data from 100s of sensors in the field?

Capabilities Proof Point: Predictive Maintenance on Oil Rigs

• Rapidly create value from your IoT data

• Only predictive analytics vendor that can run on the device, on the gateways or in the cloud

• Expert Data Scientists to help you gain insight and leverage from your IoT data

• Proven ecosystem of technology and delivery partners

Why Predixion Software?

THANK YOU

www.PredixionSoftware.com