How Data Science Builds Better Products - Data Science Pop-up Seattle
•
3 likes•3,785 views
The document discusses how data science can help build better products. It explains that products are initially built to quickly test ideas through lightweight and imperfect means. Data science helps understand customer value and enables continuous learning through a process of analyzing data, making discoveries, and pivoting the product based on what is learned. This contrasts with the traditional approach where functionality is locked in place. The document advocates for an adaptive software environment that allows for rapid changes based on new insights. It provides tips for building successful data products through iterative improvements informed by data.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to How Data Science Builds Better Products - Data Science Pop-up Seattle
Similar to How Data Science Builds Better Products - Data Science Pop-up Seattle (20)
More from Domino Data Lab
More from Domino Data Lab (20)
Recently uploaded
Recently uploaded (20)
How Data Science Builds Better Products - Data Science Pop-up Seattle
- 1. #datapopupseattle How Data Science Builds Better Products Sean McClure, Ph.D Data Scientist, Senior Consultant, ThoughtWorks WorldOfDataSci Thoughtworks
- 2. #datapopupseattle UNSTRUCTURED Data Science POP-UP in Seattle www.dominodatalab.com D Produced by Domino Data Lab Domino’s enterprise data science platform is used by leading analytical organizations to increase productivity, enable collaboration, and publish models into production faster.
- 3. How Data Science Builds Better Products Sean McClure Data Scientist, ThoughtWorks
- 4. Products are Built to Quickly Test Ideas lightweight end-to-end imperfect Why Do We Build Products? data value product early stages
- 5. learning is the key to building better products world -> data -> discovery -> pivot product product product discovery
- 6. TRADITIONAL “DISCOVERY” the right decisions
- 7. the right decisions the right decisionsTRADITIONAL “DISCOVERY”
- 8. the right decisionsTRADITIONAL “DISCOVERY”
- 9. the right decisionsTRADITIONAL “DISCOVERY”
- 10. the right decisionsTRADITIONAL “DISCOVERY”
- 11. the right decisions ? unrealistic TRADITIONAL “DISCOVERY”
- 12. data science the right decisions + understands strategyunderstands data BETTER DISCOVERY
- 13. Count-controlled loops Condition-controlled loops Collection-controlled loops Infinite loops Restart loop Generators Early exit from loops Loop variants and invariants Loop system cross-references Structured non-local control flow Conditions Exceptions Loops Flow Control structures If-then-(else) Case and switch Coroutines Continuations STANDARD SOFTWARE What’s Wrong With the Usual Approach? All functionality is locked in place
- 17. Learning algorithms Model Validation Model Performance Data visualization Operationalizing Models Scientific computing libraries Data cleansing Data preparation Probability and statistics Loops Flow Control structures If-then-(else) Case and switch Coroutines Continuations Count-controlled loops Condition-controlled loops Collection-controlled loops Infinite loops Restart loop Generators Early exit from loops Loop variants and invariants Loop system cross-references Structured non-local control flow Conditions Exceptions ADAPTIVE SOFTWARE What is the New Approach? unlocked
- 21. “rapid and flexible response to change” “continuous improvement” post-developmentdevelopment ability to pivot ability to pivot
- 22. How Do You Put the Brain in the Box?
- 23. Successful Data Products • establish early benchmarks • understand true validation • build sophistication via iteration • provide APIs to model results • get continuous exposure to domain experience • design product experiments Need to utilize technology choices that allow for building data products successfully
- 24. Search Engine Marketing - Recommendation • Increasing CTR? • Decreasing CPC? • Call volume trends • Percentage of Good Call trends. • Page Position • Visits vs Cost Per Visit • Impressions vs CTR graph. • Breakdown of CVT types • Click-to-call • Daily Budget Spend • Top 5 KWs vs Previous Good Cycle • Budget distribution • Impressions per publisher • Revenue per publisher • Page position per publisher • Review for Negative KWs • Review for Partner site issues • Review for OAT • Check Category page • Impression Share • Are the ads approved and running? • Below 1st Page Bid KWs • Quality Score • Is it loading? • Are all numbers replacing correctly? • Out of Area Traffic • High Spend – Low Revenue. • Super Low CTRs making decisions
- 25. Data Product Hadoop Cluster Databases DB Data Producer Queue Reporting Data Operational Data rl_op rl_ keyword rl_ report HDFS Flume Data Core CPI Data Mart Campaign Creative Publishers Proxy Logs Call Logs CPI Admin Console Others Others Sqoop CPI Space Raw Nor mali zed Core Jobs CPI Jobs Search Engine Marketing - Recommendation
- 28. #datapopupseattle Thank You To Our Sponsors
- 29. THANK YOU