EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS

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1 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

2 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 2

3 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 3

4 Are you Prepared for Business Change? 4

5 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? 5

6 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 6

7 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 7

8 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 indatabase capabilities 8

9 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 9

10 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 10

11 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 11

12 Predixion A Challenger in the Hurwitz Victory Index 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. 12

13 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 corrective action 13

14 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 14

15 Steve Stover, Sr. Director of Product Management 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.

16 About Predixion Software 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

17 Industry Recognition Predixion debuts on Gartner Magic Quadrant for Advanced Analytics (2015) Predixion recognized as a Challenger in Advanced Analytics Market (2014) Predixion debuts on Forrester WAVE forbig Data Predictive Analytics (2015) Predixion named key player in Healthcare Analytics Market Forecast (2014) Predixion included in Big Data 50 the hottest Big Data startups of 2014

18 Driving Forces: The IoT Data Deluge 40X 60X Processing CAGR Cost for 1 M2M Bandwidth marketcost B-50B Devices connected by by EB-4400EB Generated by the IoT ,400 exabytes = 4,400 billion terabytes Sources: 1 Goldman Sachs, 2 Cisco, 3 IDC

19 Why Predictive Analytics for IoT data? 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

20 Predictive Analytics Use Cases TRANSPORTATION Predictive Maintenance Driver Attrition Driver Incident Risk RETAIL & MARKETING Segmentation Offer Recommendation Predictive Lead Scoring Customer Churn Retailer Fraud Loyalty Fraud ENERGY, UTILITIES & MANUFACTURING Predictive Maintenance Field Service Optimization Inventory Optimization HEALTHCARE & LIFE SCIENCES Readmissions Length of Stay Population Health Claims Fraud Medication Adherence Disease Progression Remote Patient Monitoring

21 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

22 Taking Action in the IoT Who, What, and When Executives S T R A T E G I C Expand capacity Alter product line Introduce new service Management Staff O P E R A T I O N A L T A C T I C A L Schedule maintenance Change policies Order inventory Implement driver improvement Systems Orderly shutdown Next best offer Repair now Schedule patient visit Extend production Seconds Hours Days Weeks

23 What We Do Predixion provides real-time, predictive analytics at the decision point to improve outcomes. DATA FROM ANYWHERE ANALYZE & MODEL PREDICT ANYWHERE ACT API & SDK SOLUTION ACCELERATOR

24 Predictive Analytics for the IoT: Where and When You Need it We are the only predictive analytics solution that can run on the device, on the gateway and in the cloud. SENSORS CLOUD SMART DEVICES AT CENTRALIZATION POINT Enables a broad across the business Uncover new business opportunities Create new processes and policies view to cost optimize AT AGGREGATION POINT Ideal when on-device deployment is less feasible Avoid time lag, data 'blind spots' Quickly and efficiently schedule local services ON DEVICE, IN REAL TIME Alerts on both connected & disconnected devices Act before costly failures occur Automate corrective actions and efficiency optimizations

25 DEPLOYMENT MODEL CREATION Patent-Pending MLSM Provides Value in Model Creation & Deployment DATA Build Shape Combine Visualize Compare Collaborate Speed to value Portable analytic workflow Easy to embed Easy to update RUNTIME REAL-TIME OR BATCH DATA JAVA RESULTS THE LAST MILE OF ANALYTICS

26 Embed Easily into Existing Applications or Create your Own Portable Web Applications Interactive Mobile Apps Dashboard Integration Embedded CRM Embedded IoT Solution Accelerator

27 Capabilities Proof Point: Predictive Maintenance on Large Fleets THE CHALLENGE Can Predixon predict part failures on a large fleet and embed into an end to end fleet management solution? DESIRED OUTCOMES Reduce downtime Optimize inventory Optimize deployment of your technicians PREDIXION SOLUTION 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 endto-end fleet management solution Quick and easy to embed in a variety of environments

28 Capabilities Proof Point: Predictive Maintenance on Oil Rigs THE CHALLENGE 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? DESIRED OUTCOMES Reduce costly failures Optimize part inventory Optimize field service dispatch PREDIXION SOLUTION 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 webbased app with our Solution Accelerator

29 Why Predixion Software? 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

30 THANK YOU

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