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Anne-Sophie Roessler, International Business Developer, Dataiku - "3 ways to Fail your Data Lab Implementation"

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Anne-Sophie Roessler, International Business Developer, Dataiku - "3 ways to Fail your Data Lab Implementation"

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Anne-Sophie Roessler, International Business Developer at Dataiku presented "3 ways to Fail your Data Lab Implementation" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.

Anne-Sophie Roessler, International Business Developer at Dataiku presented "3 ways to Fail your Data Lab Implementation" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.

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Anne-Sophie Roessler, International Business Developer, Dataiku - "3 ways to Fail your Data Lab Implementation"

  1. 1. Three ways to Fail your Data Lab Implementation
  2. 2. Dataiku DSS
  3. 3. DataLabs 10 M€ in 2014121 499 M€ in2014 3 029 M€ in2015 5 454 M€ in2014816 M€ in201410 M€ in 2008 Marketing/ Web ü Behavioral segmentation ü Churn prediction ü Sales forecast ü Dynamic Pricing Industrie& Infrastructure ü Predictive maintenance ü Logistic Optimization ü Smart Cities Bank & Insurance ü Fraud detection ü Riskanticipation ü Lifetime moment detection
  4. 4. Why a data Lab? • 1 single Workflow : from a segmentated workflow to a transversal one • Several use cases: Ability to adress many different data centric topics within a single unit • Multiple competences: Business focused approached mixing many different competences • End to end projects : combining data from different sources to handle several aspects on a single topic
  5. 5. Deployment ofthe predictions Dataiku DSSfor fraud prediction Client service Sensor data Garage data Administration • 1 Project Owner (IT) • 1 Project Manager (Business) • 1 Data scientist in house • 3 data scientist sfrom 3 different firms • 3 consultants from 3 different firms • 1 architect (external) Accepted file INVESTIGATE ! Thetransactions areblocked dependingontheir gap with the business rules and behavioral patterns
  6. 6. Welcometo Technoslavia! 6
  7. 7. Focuson the framework,not on the input Data Acquisition & Understanding Data Preparation Model Creation Evaluation Deployment Scored dataset Scored dataset Iteration 1 Iteration 2 Iteration n ✓ Read and import raw data ✓ Detect schemas and structure ✓ Analyze distributions ✓ Assess quality: outliers, missing values... ✓ Performance metrics ✓ Robustness & generalization (cross validation) ✓ Insights (eg variable importance) ✓ Create derived and aggregated variables → Analytical dataset → Report ✓ Feature selection ✓ Compare algorithms ✓ Scoring engine ✓ Publish predictions ✓ Monitor performance ✓ API Business Understanding Adapted from the CRISP-DM methodology Dataset 1 Dataset 2 Dataset n
  8. 8. People and Governance ? PolyglottVS dictator Problems : • Collaboration between technical and non technical profiles inside a single project • Nécessary collaboration between business and tech teams to adress transversal projects accurately Focus : • Promote diversity • …within a workflow centric environment
  9. 9. End to end, from prototyping into production Do it you way …
  10. 10. …and scale!
  11. 11. DataLab Organisation Data Lab Lab Environment MultydisciplinaryTeam: Direction/ Project Management Business Analysts Data Miners / Data Scientists Production Environment Business needs Internal Data sources External datasources Missions : Priorisationof the business needs Prototyping /Agile solution engineering Support for Apps deployment Business Applications Marketing CampaignAutomation Reporting webanalytics Data as A Service Platform Conceptionof“DATAPRODUCTS” Integration of DataProducts OptimisationEngine Real Time Scoring Data Flow Insights & Services Processing chain API Deployment
  12. 12. Thank you !

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