Big Data Strategy. Use Case Study. Amy O Connor // Field Sales Evangelist

Transcription

1 Big Data Strategy Use Case Study Amy O Connor // Field Sales Evangelist

2 The Importance of a Data Strategy Data is your Most Important Asset Use that Data to achieve your Business Vision 2

3 Data created by People 3

4 Zuck s Law The amount of things each user shares on Facebook has roughly doubled every year. 4

5 Customers are providing greater visibility into their lives Builders Boomers Gen X Gen Y Gen Z 5

6 Data created by Machines The Internet of Things 6

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8 Key Elements of a Big Data Strategy 1. Collect your Existing Data in One Place 2. Create New Data Channels 3. Create Opportunity through data-focused Operational Efficiency Use Cases 4. Drive Innovation and Revenue with Data 5. Collect, Secure, Govern and appropriately Use your Data to achieve your Business Vision 8

9 1. Collect your Existing Data in One Place Provides access to historical and real-time data Supports compliance needs Enables sharing across your business Empowers out-of-box thinking Increases analytical agility The Enterprise Data Hub is Affordable and Attainable. 9

10 1. Collect your Existing Data in One Place Experian clients want single view of customer; requires real-time updates on purchasing behaviors, online browsing patterns, social media activity Legacy systems can only process 50M customer matches/day New Experian Cross-Channel Identity Resolution Engine is a persistent repository of all client touch points Now processing 100M matches/hour (28K customer matches / second) Delivering hourly versus monthly campaign reports to clients 10

11 1. Collect your Existing Data in One Place Monsanto wanted to automate data- driven R&D decisions to reduce time to market for new products. 1,000+ research scientists developing products in silos Data processing bottleneck slows development sharing data between groups typically took 3-4 weeks Brought cross-company data together in an Enterprise Data Hub Time to market for new product went from 5-10 years to months 11

12 1. Collect your Existing Data in One Place Morgan Stanley, a global investment bank, must improve portfolio management capabilities. Legacy systems would not scale to handle new log data. Analysts were only able to use sample data, which reduced accuracy of models. Their Enterprise Data Hub handles PB-scale for every log: web, server, app Analysts can now pattern match for every attribute, producing time-based correlations of market events with web and database logs to consistently deliver results 12

13 2. Create New Data Channels Build new apps, sensors, etc to gather data Innovation from data, photo, video, apps Opportunity to view business new ways Opportunity to make new connections between people and the physical world Bridging experiences across devices The Internet of Things The Enterprise Data Hub can process any type of data predictably. 13

14 2. Create New Data Channels Hospital re-admittance reflects poor providerto-patient communications Kaiser wanted to utilize new at-home devices to deliver health information IT systems can t accommodate 24x7 data streams from devices Kaiser s Enterprise Data Hub combines real-time machine-generated data streams, electronic medical records, social data and other information Kaiser Permanente helps providers recommend at-home action based on realtime data to prevent hospital visits. 14

15 2. Create New Data Channels Opower equips utility meters on millions of homes with sensors Ever-growing utility data streams are captured and analyzed (AMI, smart appliances, interactive user apps, sensors, social media) Opower s Enterprise Data Hub enables time based correlations that are delivered to customers of 75 global utilities via their Social Energy web application. Opower helps 15+ millions homes save hundreds of millions of dollars on energy bills by instrumenting sensors on meters. 15

16 2. Create New Data Channels Large equipment manufacturers are instrumenting sensors across product lines Sensors collect information about usage, maintenance of the equipment, but also of the soil being moved. Detailed equipment usage information facilitates predictive maintenance Sensors detecting information about soil empowers new services such as increasing output per acre of land 16

17 3. Create Opportunity through Operational Efficiency Reduce data processing windows Reduce storage costs Make data available quickly and easily It is all about opportunity cost. 17

18 3. Create Opportunity through Operational Efficiency Allstate, one of the largest insurance companies in the US, has data silos spread across company with 80+ years historical data; only some digitized Incumbent systems could run one state s risk model in one day requiring 50 days to run all 50 state models. Using Allstate s EDH, risk models for all 50 states now run in 16 hours using Hive; a 75X speed-up Allstate can now optimize offers and pricing with a comprehensive view of individual risk on a daily basis. 18

19 3. Create Opportunity through Operational Efficiency Nokia built a Teradata warehouse in 2002 for product BOMs, marketing campaigns, financial reporting In 2010 the company needed to leverage mobile user activity information (e.g.; app downloads, device usage) to develop a more accurate churn management model Log data was processed in Nokia s Cloudera cluster and the results were sent into the Teradata warehouse This integration of the Cloudera cluster with the data warehouse created a seamless working environment for the marketing users who were experienced with the Teradata warehouse. 19

20 4. Drive Innovation and Revenue with Data Add more context to current use cases Build insights into business processes Optimize investments Create new business models Drive new revenue opportunities It is all about innovation and transforming business. 20

21 4. Drive Innovation and Revenue with Data Ill children need special skills, diagnoses, treatment, equipment, support US-based Children s Healthcare instrumented the pediatric health care unit with noise and light sensors Bedside data feeds collect light and noise, which is now correlatated with quality of care, patient outcomes in neonatal ICU Cloudera s Enterprise Data Hub is the foundation for this new system. 21

22 4. Drive Innovation and Revenue with Data MasterCard has multiple petabytes of data that can only be stored in a highly secure, Payment Card Industry (PCI) compliant environment. MasterCard has multiple monetization use cases that require that environment and data Cloudera worked with MasterCard to create the world s first PCI-compliant Hadoop system MasterCard now has mutliple use cases running in parallel, including merchant fraud detection, consumer behavioral modeling, security analytics 22

23 With Big Data comes Big Responsibility Data is your Most Important Asset Collect, Secure, Govern and appropriately Use that Data to achieve your Business Vision 23

24 Thank You