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Leading the Product 2017 - Wendy Glasgow


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Title: Data : Considering more than the obvious
Presented at both Leading the Product 2017 - Melbourne and Sydney

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Leading the Product 2017 - Wendy Glasgow

  1. 1. Leading The Product 2017 Speaker Slides Melbourne and Sydney, Australia Wendy Glasgow Google For more information go to
  2. 2. Confidential + ProprietaryConfidential + Proprietary Data: Considering more than the obvious
  3. 3. Confidential + Proprietary Data to define what we build Data generated from our product Data powering our product 2 31
  4. 4. Confidential + Proprietary
  5. 5. Confidential + Proprietary Data to define what we build 1
  6. 6. Confidential + Proprietary Metrics to define success - moving away from the gut Get them right - we can truly build a great product that grows our business Get them wrong - we can look successful on paper but completely miss the mark
  7. 7. Google Confidential and Proprietary weight target quantity target Russian nail factory workers Early 20th century
  8. 8. Macro: What is my business trying to do? What is my team trying to do?
  9. 9. Build a strong profitable business
  10. 10. Proprietary + Confidential Executives that adhere to metrics that tie directly to business objectives 3xmore likely to hit their goals Source: New Study Reveals Why Integrated Marketing Analytics are Critical to Success, Think with Google, Forrester, March 2016
  11. 11. Confidential + Proprietary Understand the macro before diving into the micro Product Strategy Market Penetration | Brand Positioning | Profit Targets Product Plan Metrics Marketing Str… Sales Cost / Service External DriversEngagement Product Teams Your Product strategy enables focus
  12. 12. Confidential + ProprietaryConfidential and Proprietary Jack Welch, Former CEO of GE “There are only two sources of competitive advantage: The ability to learn more about our customers faster than the competition, and the ability to turn that learning into action faster than the competition.”
  13. 13. Proprietary + Confidential Customers are NOT created equal Focus: Who is your customer?
  14. 14. Confidential + Proprietary Know your ‘best’ customers $ $$$$$ $$$$$ $ Value Spend less Cost to Acquire Ideal Customer Base Cost to Service, Support, Retain
  15. 15. Confidential + Proprietary Widen the scope of considered data Marketing Ad Logs Search DM EDM SMS Competitions Newsletters Product, Websites Stores Engagement Analytics Transactions Customer Services & Support Call centre Customer interactions Finance Transaction Business Costs Operations & Logistics Operational costs Delivery Sales CRM POS Customer Value People & Culture People costs Skills matrix Attrition Tech POS Logs Analytics Relevant Data Data Strategy Data Governance
  16. 16. Confidential + Proprietary Data generated from the product 2
  17. 17. Confidential + Proprietary ● Deal with data early ● Ensure you have a data strategy ● Add a section at the definition stage ● Make it mandatory ● Provide a process, make it consistent ● Over capture and LABEL! You’re focused on getting your product live
  18. 18. Confidential + Proprietary We’re capturing data - what is important to consider? ● Data storage is CHEAP - $0.02 per TB per month in BigQuery! ● Capture everything possible - but make it readable ● Consider the data points you’re capturing ● Make the data meaningful - LABEL ● Link your data - IDs, Labels ● Timestamps are critical
  19. 19. Confidential + Proprietary Tools BigQueryAttribution Data Studio Data Vis Tag Manager Tag Mgt Optimize Testing/Personalisation Google Analytics Analytics
  20. 20. Confidential + Proprietary
  21. 21. Confidential + Proprietary Make friends with statistics - OR - with someone who already is correlation causation Get intimate with your data relevance
  22. 22. Confidential + Proprietary Data as an Asset
  23. 23. Confidential + Proprietary There is no competitive advantage within an organisation! Share your Data There is no competitive advantage within an organisation!
  24. 24. Confidential + Proprietary Data powering our product 3
  25. 25. Confidential + Proprietary Machine Learning is the new ground for gaining competitive edge & creating business value *Source: MIT Survey 2017; n=375 Bain Consulting Study Competitive advantage ranked as top goal of machine-learning projects for 46% of IT leaders & 50% of adopters can quantify ROI 2X more data-driven decisions 5X faster decisions than others 3X faster execution
  26. 26. Confidential + Proprietary Machine Learning Allows You to Solve a Problem Without Codifying the Solution ✓ Recognizes patterns in data ✓ Predictive analytics at scale ✓ Builds ML models seamlessly ✓ Fully managed service ✓ Deep Learning capabilities Google Cloud AI
  27. 27. Confidential + Proprietary First Step in This Journey Begins with Data “Every Company will be a Data Company” *Source: Wired, Bloomberg, Fortune, McKinsey Proprietary + Confidential
  28. 28. Confidential + Proprietary Machine Learning Lifecycle at a Glance How do I collect, store and make data available to the right systems? How do I understand what data is required to solve my business problem? User Data Objective TrainServe How do I get to a working model within the period of time where my objective is still relevant? How do I scale prediction into production systems? How do I keep my model relevant with continuously updated data?
  29. 29. Confidential + Proprietary Flow to build a custom ML model Identify business problem Develop hypothesis Acquire + explore data Build a model Train the model Apply and scale 1 2 3 4 5 6
  30. 30. Confidential + Proprietary Structured Data ● Spreadsheets, Logs, Databases ● Text that includes structure ● Data needs to be separated ● Typical data generated from products Unstructured Data ● Natural Language, Images ● More complex but sometimes these are better understood ● Number of existing ML APIs - Supervised Learning ● Need labels on the data ● Build examples to train the system Unsupervised Learning ● Data is grouped / clustered ● Drawing inferences from data sets
  31. 31. Confidential + Proprietary A feature in ML is very different from a feature in Product In ML, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. Feature Engineering
  32. 32. Confidential + Proprietary A feature is a data point, so what is good? Represent raw data in a form conducive for ML 1. Should be related to the objective 2. Should be known at production-time 3. Has to be numeric with meaningful magnitude 4. Has enough examples (absolute minimum of 5)
  33. 33. Confidential + Proprietary What can I do today to plan for ML 1. Find your Data Strategy and Governance owners – get familiar with it or create it! 2. Identify the decisions your product makes today. 3. Consider suitability for automation with ML. 4. What data do you have today and what do you need to capture? 5. Capture data in line with your strategy and governance guidelines – update them if necessary. 6. Capture LOTS of data, but LABEL it well and consistently!
  34. 34. Confidential + Proprietary Takeaway 1 Takeaway 2 Value is in use of data Think inside, outside & future It’s what we do with the data that matters BUT… early consideration can increase value How does you relate to your surroundings Relevance, correlation and causation
  35. 35. Confidential + Proprietary ● Predictive maintenance or condition monitoring ● Warranty reserve estimation ● Propensity to buy ● Demand forecasting ● Process optimization ● Telematics Manufacturing ● Predictive inventory planning ● Recommendation engines ● Upsell and cross-channel marketing ● Market segmentation and targeting ● Customer ROI and lifetime value Retail ● Alerts and diagnostics from real-time patient data ● Disease identification and risk satisfaction ● Patient triage optimization ● Proactive health management ● Healthcare provider sentiment analysis Healthcare and Life Sciences ● Aircraft scheduling ● Dynamic pricing ● Social media – consumer feedback and interaction analysis ● Customer complaint resolution ● Traffic patterns and congestion management Travel and Hospitality ● Risk analytics and regulation ● Customer Segmentation ● Cross-selling and up-selling ● Sales and marketing campaign management ● Credit worthiness evaluation Financial Services ● Power usage analytics ● Seismic data processing ● Carbon emissions and trading ● Customer-specific pricing ● Smart grid management ● Energy demand and supply optimization Energy, Feedstock and Utilities Cloud Machine Learning Use Cases