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Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtworks
The document discusses the integration of big data, agile analytics, and advanced analytical techniques to enhance business intelligence and decision-making. It highlights methodologies for data processing and analysis, emphasizing the importance of continuous learning, data convergence, and the agility of analytics in retaining high-value customers. Key concepts include machine learning, data science, and the ethical considerations of analytics in business environments.
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Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtworks
- 1. BIG DATA AGILE ANALYTICS Ken Collier,PhD Director, Agile Analytics @theagilist #thoughtworks 1
- 2. Value Complexity What happened? Descriptive Analytics Why didit happen? Diagnostic Analytics What will happen? Predictive Analytics How can we make it happen? Prescriptive Analytics
- 3. Value Complexity What happened? Descriptive Analytics Why didit happen? Diagnostic Analytics What will happen? Predictive Analytics How can we make it happen? Prescriptive Analytics 3 Traditional Business Intelligence Advanced Analytics
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- 6. Big Data AnalyticsPipeline Modeling Data Operational Data External Data Data Integration Reporting Engine Dimension Mapping Clean Data Report Report Report Dimensional Data Data Sampling Feature Selection Data Partitioning Test Data Training Data Analytical Modeling Candidate Model Model Validation Accepted Model
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- 9. Discover & Explore Analyze &Act Data Convergence Analytical Divergence Discover Harvest Filter Integrate Augment Analyze Act Analytical Opportunities How Advanced Analytics Works If we knew X, we could do Y
- 10. Typical Timeline 3-6 months2 months 2-4 months 10 Data Convergence Analytical Divergence Discover Harvest Filter Integrate Augment Analyze Act Analytical Opportunities Traditional Analytics If we knew X, we could do Y
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- 13. Analytical Divergence Analytical Opportunities Ifwe knew X, we could do Y Data Convergence Discover Harvest Filter Integrate Augment Analyze Act Repeat this cycle solving small problems every few days LEARN MEASURE BUILD Agility in Analytics
- 14. Retain high value customers Highvalue business goal Like this example…
- 15. What’s the smallest, simplest thingwe can do? Retain high value customers Like this example… Common features of defectors?
- 16. Is it useful& actionable? Retain high value customers Like this example… Common features of defectors?
- 17. Repeat!Retain high value customers Likethis example… Common features of defectors? Shopping behaviors of defectors?
- 18. Retain high value customers Likethis example… Common features of defectors? What leads to customers leaving? Shopping behaviors of defectors? What do defectors say about us? Customers’ sentiment before defecting? What encourages customers to stay? Do incentives reduce defection rates?
- 19. Problem solved or continue? What leadsto customers leaving? Like this example… Common features of defectors? Shopping behaviors of defectors? What do defectors say about us? Customers’ sentiment before defecting? What encourages customers to stay? Do incentives reduce defection rates?
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- 21. THE “DATA SCIENTIST” MachineLearning Statistical Modeling Artificial Neural Networks Decision Tree Learning Support Vector Machines Clustering …and many more… Bayesian Classification Monte Carlo Simulation Logistic Regression K-Nearest Neighbor …and many more… Domain Knowledge Data Semantics Business Understanding Business Communication Programming Skills Functional Programming Data “Wrangling” Map/Reduce, SQL, & NoSQL
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- 28. Advanced Analytics Data Science Visual Storytelling Machine Learning Statistics Agile Analytics Big Data Solutions Thinking Ethics Agile DeliveryLean Learning Impact Volume Velocity Variety NoSQL Complexity Polyglot Persistence Continuous Integration Collaboration Evolve Continuous Delivery Hypothesis Build Learn Measure Data Reduction Insight Knowledge Action Disruption Businessvs. IT Focus vs. Platform Monitor & Measure
- 31. Advanced Analytics Data Science Visual Storytelling Machine Learning Statistics Agile Analytics Big Data Solutions Thinking Ethics Agile DeliveryLean Learning Impact Volume Velocity Variety NoSQL Complexity Polyglot Persistence Continuous Integration Collaboration Evolve Continuous Delivery Hypothesis Build Learn Measure Data Reduction Insight Knowledge Action Disruption Businessvs. IT Focus vs. Platform Monitor & Measure Privacy Controls Radical Transparency Data Democracy Open Data
- 34. Advanced Analytics Data Science Visual Storytelling Machine Learning Statistics Agile Analytics Big Data Solutions Thinking Ethics Agile DeliveryLean Learning Impact Volume Velocity Variety NoSQL Complexity Polyglot Persistence Continuous Integration Collaboration Evolve Continuous Delivery Hypothesis Build Learn Measure Data Reduction Insight Knowledge Action Disruption Businessvs. IT Focus vs. Platform Monitor & Measure Privacy Controls Radical Transparency Data Democracy Open Data
- 35. Ken Collier, Director,Agile Analytics kcollier@thoughtworks.com Value Creation Cool New Technologies + Sophisticated Analytics + Lean Learning Principals + Fast Agile Delivery =