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Where does Data Democracy begin? [Segment-Synapse, 2019]
- 1. Ajay Gopal Synapse, September 25, 2019 Where Does Data Democracy Begin? Hosts:
- 2. 2 who.i.am • PhD: Univ Chicago | Postdoc: UCLA • Lead Teams Building Core IP & Data/ML Products • Fintech Roots: Credit Risk & Ad Auction Models • Now: Building Food Personalization PlatformAJAY GOPAL Head of Data Science & Eng, Sun Basket tw: @aj2z | gh: ajzz | in/ajzz
- 3. 3 • Personalized Healthy Eating Platform • Chef developed, nutritionist approved, healthy, easy & delivered home • Organic produce & sustainable meats • Initially dinner meal kits, now diversified beyond dinners to other meals & snacks • Series E, > 1K Employees What’s Sun Basket?
- 4. In Your Box Today 4 1 What’s Data Democracy 2 3 What it Means for the Org Implementation - Technologies - People - Processes 4 Results
- 5. 5 “Data Democracy” what does it mean to you?
- 6. 6 https://www.data.gov/meta/five-years-open-data-making-difference/ “Data is a right, not a privilege. Today we stand to make data affordable to all. We stand up for data democracy. Stand with us,” – Cell C Marketing Material, South Africa https://www.fastcompany.co.za/business/cell-c-has-launched-new-lte-a-packages-16526675 Is it about access to more/all data?
- 7. 7 http://news.mit.edu/2019/nonprogrammers-data-science-0115 Making analysis tools free & non-expert friendly? https://eng.collectivehealth.com/democratizing- research-creating-accessible-and-actionable- insights-for-all-4273f84d0bdf “How are we approaching this? Well, first of all: death to decks. I don’t really mean that, but whenever possible, we’ve been trying to distill our findings into one-pagers. These one-pagers are easily digestible and provide a high- level summary of research findings to busy stakeholders. We still provide deeper dives to folks who are more interested in the details, but more often than not, these summaries give people easy access to the intel they need to move a product forward.” Making results easy to understand and freely available?
- 8. 8 ‘It makes no sense that (my) information is used as the raw material to produce billions of dollars of income for massive “data monarchs” yet is of no financial value to me.’ Access to the value in your data
- 9. 9 At its core, it means to empower citizens with the data, tools and the latest methods to independently access the value of their data. Open Government & Public Data Sets Reproducible Analyses & Peer Review Consensus Action Based on Established Facts Information Open Source / Low Cost Analytics & Modeling Tools Analysis Reliable Inference Action / Value
- 10. Employees: “informed, intelligent, independent value generators” every employee has all the data, tools & transparency needed for independent decision making • Highly available, real-time data, with cross-functional fluidity via segment, central data dictionary & feature store • Freely available analysis tools & ml platforms, wide access to results via web enabled event analytics eg mixpanel + bi & ml tools • Inclusive analytics training Online coding, reproducible research, design of experiment Customers: APIs to surface analyzed customer data to empower better decisions Eg. Eat better, activity nudges, improve credit scores 10 Data democracy within an organization
- 11. Before: Central Analysis 11 It’s possible and pays back in multiple ways Data democrazcy Analytics Services Operations Product Kitchen Eng Finance Data Platforms & APIs After: Central Platforms, Radial Analysis Embedded Power Users Viewers Operations Product Kitchen Eng. Finance Customers Challenges: - Analysis sometimes too late for decision - Divergent definitions of business metrics`
- 12. 12 Democratization by design Define, compute, & maintain metrics centrally Move source of truth out of inboxes to platforms Engineer products with measurement in mind Evolve data accountability for day-to-day decisions Reduce data fear, maximize access to self-service Technology Process People
- 15. 15 Solve for customer and operational data centrally CUSTOMER DATA PIPELINE OPERATIONAL DATA PIPELINE 1 2 3 Self-Service Points
- 16. 16 Process Embedding measurement into product
- 17. 17 Event Tracking Development Workflow Segment & Mixpanel have dedicated instances to receive events from each of: - Dev Feature Branch - Staging Branch - Production By Data Science Team
- 19. 19 Central team Dedicated technical experts Radials Embedded domain analysts Global Data Aware • Data Scientists • Data Engineers • Business Analyst • Generalists • Marketing • Finance • Operations • Everyone
- 20. 20 5-7 Central Analysts Avg Analysis Completion time 1-2 weeks 15-20 Internal clients who distribute learnings further Before
- 21. 21 After 4in Central Platform Team 30-40Power users across various teams • 30+ Self-Service Mixpanel Product Analytics • 10+ Self-Service Marketers via CDP 90+% analytics self-served by 150+ data consumers Data Science escalations are high value, team time used to build data APIs & products
- 23. 23 Self-service generates demand for: • Measurement in product spec • High quality data • Data driven accountability “Distributed” analysis explores KPIs to centralize: • Single API & point of maintenance for each production business metric • Coherence in cross-functional analysis (common issue: fiscal CAC vs Mktg CAC)
- 24. 24 Risks to mitigate Duplication Divergence Misinterpretation Some of it is good. It validates reproducibility. Clear prioritization, Analysis owners + Peer review can solve Propagate commonly used KPIs back to central store. Central team guarantees quality, recency and maintenance Online training, Expert Office Hours, Checker Tools Security & Compliance - Multi-level security in pipeline, - Tokenize data so still usable, but not in clear - Serve via token-sensitive auditable data APIs
- 25. 25 So where does data democracy begin? Training all employees to self-serve data Making data central, with guaranteed quality and high fluid Embedding measurement into product design Encouraging self- service and mutual data accountability at home.
- 26. 26 Thank you!