Webinar: Analytics with NoSQL: Why, for What, and When?

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The Big Data era has made managing and preparing data for analytics increasingly complicated. Notwithstanding all the attention and new tools available, preparing and sourcing data for analytics still requires 80% of the time, which is more than anyone finds acceptable.

NoSQL databases — including document databases and key value stores — and Hadoop are becoming prevalent in industrial IT infrastructures. The flexibility they exhibit in data modeling and computing models are changing the foundations of analytical projects and requiring many new skills and workflow changes for data scientists. They are also making impressive promises.

In this webinar, we will review how and when NoSQL technology can help your organization derive value from its data with analytics. We will also discuss how Fortune 500 companies are extracting value from their data efficiently to drive revenue and decrease cost. Learn how:

A large manufacturer uses sensor data to provide new analytics apps for its customers and to inform internal product development
A top 5 investment bank performs risk analytics based on trading data in real time
A leading retail company optimizes prices by aggregating competitive data from a variety of structured and unstructured sources

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Webinar: Analytics with NoSQL: Why, for What, and When?

  1. 1. 11Analytics with NoSQL: why? for what? and when?Edouard Servan-Schreiber, Ph.D.Director for Solution Architecture10gen
  2. 2. 2What is Analytics?2• Alerting– Let me know when a cell tower has failed• Getting insights - Strategic Analytics– Churn rates, Customer segment distribution• Transforming, Enriching, Aggregating– Identifying faces in videos and images– Identifying voices in recordings• Operating smarter– Having a pre-approved offer for a customer who calls after he expressedinterest on the web• Analytics-driven actions in real-time– Smart modeling integrating real time context– This customer has lower status but suffered multiple delays in pastmonth, and should have priority over this higher status customer rightnow on this flight
  3. 3. 3Why is this hard?• Lots of data– but few eyes and slow brains• Lots of data– just as many formats• Lots of data– many owners with unaligned interests and concerns• Can you get your analysis in a useful timeframe?• Can you make improvements in a useful timeframe?• When you get new data, how fast can you do something with it?• The more DATA you have, the easier it is to get lost in it...• Data is useful only if it allows you to CHANGE the way you run your activity– this is a surprisingly useful litmus test• Any change requires measurement to make sure it helps– this is a remarkably effective test to identify analytical organizations3
  4. 4. 4Seven vital success areasCRISP-DM methodologyData4DataMany Data Sources and SchemasHard to IntegrateKeeps evolvingActing on “real time”dataIs particularly hard
  5. 5. 5Collaborative Filtering“Those who saw this also liked this….”• Real time continuous updates of the user-product matrixto make up-to-date predictions5
  6. 6. 6Credit Card FraudComplex Event Processing• Each transaction must be approved in amatter of seconds. Each step, the relevantauthority must decide in real-time whetherthe transaction is suspicious enough towarrant an alert, refuting the transaction6
  7. 7. Approaches to Model Scoring
  8. 8. 88Once you have built insights, the hard part is turning those insights intomoney making actions through a multitude of field systemsActions are taken in field systems….DWHSensor StoreOrder StoreInventory MgmtWarranty MgmtCustomer PortalAnalytical StoreData is built here and action is taken hereLong running batchanalysisDevelopment ofStats ModelsIntegration ofEnterprise Data
  9. 9. 99• Once you have built insights, the hard part is turning those insightsinto money making actions through a multitude of field systemsActions are taken in field systems….DWHSensor StoreOrder StoreInventory MgmtWarranty MgmtCustomer PortalAnalytical StoreData is built here and action is taken hereBIGETLMess
  10. 10. 1010Once you have built insights, the hard part is turning those insights intomoney making actions through a multitude of field systemsActions are taken in field systems….DWHSensor StoreOrder StoreInventory MgmtWarranty MgmtCustomer PortalAnalytical StoreOperational Pre-aggregationBIG MoveableNormalETLMess
  11. 11. 1111MongoDB Strategic AdvantagesHorizontally Scalable-ShardingAgileFlexibleHigh PerformanceStrong ConsistencyApplicationHighlyAvailable-Replica Sets{ author: “roger”,date: new Date(),text: “Spirited Away”,tags: [“Tezuka”, “Manga”]}+AggregationFramework+MapReduceFramework
  12. 12. 1212Document-oriented data model (JSON-Style){_id : ObjectId("4c4ba5c0672c685e5e8aabf3"),model: ”101 jet engine",date : ISODate(“24-07-2010”),purchaser: “Emirates”,aircraft: {type: “Boeing 747-400”,first_flight: ISODate(“01-11-2010”)registration: 3467892}manufacturing_plant: 8374parts : [{ partid: 132467589648762348765,description: “blade”,source: “some vendor”,.....},{ partid: 9584352845569846,description: “injector”,source: “some vendor”,.....},sensor_list: [sensorid1, sensoriid2, sensorid3,....]}www.bsonspec.org
  13. 13. 13Use Cases• Retail:– Price Optimization• Utilities and Manufacturing:– Using smart meter data, optimizing the flow ofelectrical power to maximize yield and usage– Sensor data from vehicles to build truck fleet analyticsin real time• Telco:– Geo-based advertising, delivering relevant ads basedon interest and locality– Smart call routing taking into account saturated celltowers and customer value13
  14. 14. 14Use Cases• Gov: City of Chicago (WindyGrid)– Based on reports of maintenance needs (e.g.broken streetlights), dispatching police intargeted ways to reduce crime• Financial Services: MetLife (The Wall)– Moving from a policy centric view to acustomer centric view, enabling informedupsell and cross sell offers based on historicalanalysis and recent activity14
  15. 15. 15How does MongoDB help for these?• Agility to compute and aggregate in place– All• Agility to add new data to existing schema– Price Optimization• High scalable performance to ingestoperational data– Sensor data• High scalable performance to serveoperational analytics– Metlife, Telco 15
  16. 16. 16NoSQL and Analytics16Tech Dev TimeExeclatencyExecPowerDataTransferFunctionalDepthHadoop * * ***** ** *****MongoDB ***** ***** *** ***** **CassandrawithHadoop* * ***** ***** *****DWH *** ***** ***** ** ****SAS ***** ***** ** * *****
  17. 17. 17Conclusions• Analytics are no longer just batch• Analytics requires integrating the real timecontext• Big Data is putting pressure to processdata where it lands• New sources and forms of data are makingit difficult to stick to RDBMS rigidity• MongoDB can help you17

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