Success Through an Actionable Data Science Stack
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The document discusses the role of a lead data scientist in establishing a data-driven culture using an agile data science stack. It emphasizes the importance of effective communication, stakeholder collaboration, and iterative processes to implement data practices that align with organizational goals. Key takeaways include creating a flexible roadmap, utilizing appropriate tools, and measuring success through relevant metrics.
Success Through an Actionable Data Science Stack
- 1. Backstage to Data Driven Culture Success with an Agile Data Science Stack Big Data LA Day 2016 Pauline Chow
- 2. 2 So, You are the First Data Scientist…?
- 3. WORLDWIDE BUSINESS BUSINESS TO GO CREATIVE SOLUTIONS WORLDWIDE BUSINESS BUSINESS TO GO CREATIVE SOLUTIONS What my Friends Think I Do What my Mom Thinks I Do What Society Thinks I Do What my Boss Think I Do What I Think I Do What I Actually Do Misconceptions about Data Scientists 3
- 4. 4 So, You are the First or Lead Data Scientist…?
- 5. Open Source & New Tools Profits Steady , Adding Products Report to VP Marketing Non Technical Culture First Data Scientist What does the organization do best? How does it relate to data and technology? What is the business core competencies? What are existing tools, processes, and code? Do you have a budget for new tools and resources? What Tools are Available ? This is both a team members and expectations related question. Where is your Team? What is the mood of the organization? How are they solving problems? Why are they adding DS/A into the organization? What is the State of the Organization? Who are the stakeholders? How is data able to contribute to their goals and expectations? Who has the Influence On the Roadmap? Context for Presentation Case Study: Startup in Digital Media 5
- 6. Effectively Implement Solutions Maximize Impact & Commun- ication Set a Blueprint that promotes flexibility, iteration, and scalability. It facilities agile-oriented mindsets for data practices and it crucial for implementation. Build a Roadmap from Blueprint to shape data practices and implement goals from stakeholders, company, as well as strong DS/A foundations. Develop key qualitative and quantitative milestones. Communicate consistently and frequently to the organization. Influence Expectations Influence from both angles, yours and stakeholders expectations. Find explicit and implicit goals and bridge the gaps that you find. 6 Key Drivers Integrating Data Culture Create an Agile Data Science Stack Non-technical focused
- 7. Actively Listen Implement Explore Collaborate Influence Grow Guiding Verbs for “First” Data Scientist 7 In no particular order
- 8. ACTIVE LISTENING: What Are you Trying to Hear?
- 9. Explicit Goals & Expectations Structured, straight-forward, logical, and safe inquiries Document, share, and openly discuss with team members and stakeholders. Jungwoo Hong @ Unsplash
- 10. Implicit Goals & Expectations Thom @ Unsplash
- 11. IMPLEMENT: HOW TO APPROACH YOUR BLUEPRINT FOR DATA DRIVEN-INFORMED CULTURE?
- 12. Architecture First Process First 12 STACK AGILE APPROACHES Anthony Delanoix @ Unsplash Jeff Sheldon @ Unsplash
- 13. Blueprint approach from infrastructure perspective AGILE BY ARCHITECTURE 13
- 14. Customize as the team grows SaaS & PaaS Integration 14
- 15. IDENTIFY BUILD SYS & MODELS - Select Appropriate Models - Build Models and Pipelines for Scalability - Evaluate and refine Models ACQUIRE DATA - Identify the “right” source - Import data and set up remote / local storage - Determine tools to work with selected sources CREATE PROBLEM STATEMENT - Identify business, data, product objectives - Brainstorm potential solutions - Create questions and identify people/stakeholders to help PARSE & MINE DATA - Determine distribution of data and necessary transformations - Format, clean, splice, etc - Create new derived data PRESENT RESULTS - Summarize Findings - Add Storytelling aspects - Identify next questions and additional analysis - For teams and stakeholders 15 AGILE BY PROCESS Blueprint approach from workflow perspective ACQUIRE PARSE & MINE PRESENTBUILD DEPLOY
- 16. IDENTIFY BUILD SYS & MODELS + DEPLOY Leverage platforms that document models, pipelines, and feature iterations. Collaboration is a plus. - Sklearn pipelines - DS/ML platforms: Yhat, domino labs, anaconda ACQUIRE DATA Curate data from existing sources that is cleaned, reliable, and automated, where ETL can be skipped - Segement.io - Zapier - CrowdFlower - Open Data CREATE PROBLEM STATEMENT Keep most attributes of this section in-house and within your team PARSE & MINE DATA For the data that cannot be automated or acquired cleanly, sklearn pipelines or open source Luigi (Spotify) or airflow (AirBNB) can mitigate this process. PRESENT RESULTS Adopt platforms that allow for iterations and data mining/ parsing process to feed into reports and presentations - Ipython Jupyter Notebooks - Dashboards: Looker, RJMetrics, Tableau 16 SaaS & PaaS Integration Customize as the Process Increases in Complexity ACQUIRE PARSE & MINE PRESENTBUILD DEPLOY
- 17. COLLABORATE: What Metrics to Emphasize for Teamwork?
- 18. Burn Rate Most companies do not widely broadcast but transparency can put decisions into perspective for the organization. Time and urgency can also be of the essence. Customer Acquisition Cost (CAC) Illustrates market competitiveness with your products, services, and market saturation. Social media ad platforms can make up a large portion of these costs.
- 19. Gross Profit & Revenue Actual revenue & profit after expenses, investors, and ongoing costs. If the business model and product are viable then the company will be able to stand on its own without external capital. Active Users Measure the ongoing stickiness of a service or product. Clearly define “active” to not overcompensate first-time, new, and experimental users. Can the company move beyond early adopters and fans?
- 20. Churn Rate & Retention How many people are leaving or become inactive after a certain period of time? When in the customer’s lifetime is churn more likely to occur? The higher the expected churn rate, then the more the company has to spend on acquiring new customers. Cumulative Growth Cumulative growth puts a long term and sustainable perspective to just month over month growth. Short-term growth can unabashedly take over and cause decision makers to lose sight of an organization’s mission and goals.
- 21. Response Time The amount of time teams take to respond and complete tasks, which includes bug fixes, technological improvements, product upgades, and customer service. Responsiveness demonstrates staff and team dedication, effective allocation of resources, operational effectiveness, and no tech debt. Customer LIfetime Value (CLV) Total dollars from a customer during the lifetime relationship with that customer. Intersection of frequency of customer purchases, revenue per customer, acquisition costs. This measure can have predictive qualities
- 22. INFLUENCE How to align and connect goals and expectations?
- 23. "Leadership is the art of giving people a platform for spreading ideas that work." -Seth Godin 23
- 24. Evaluate milestones, iterate and grow Month 12 Blueprint for Agile Data Science and Analytics Stack Day 30 Establish clear measures for success as widespread as possible Day 90 Good first impressions. Listen and Learn! Day 1 Celebrate improvements to workflow, effectiveness, and access Day 60 Democratize data access and streamline measures to external and internal teams Month 6 Communicate, Strategize, Communicate... Connect the Dots 24
- 25. Anything Else Reporting & Urgent Requests Data Acquisition, Cleaning Exploration & Analysis, Reports, & Presentation 20% 80% 80% 20% 25 Allocate Time & Resources Effectively Business as Usual Allocation New Data Science Allocation
- 26. GROW YOUR TEAM When to increase the ability and capabilities of your team?
- 27. Technical Project Manager Data Scientist Data Engineer Data Engineer Analyst Researcher Team Members
- 28. 6 1 2 5Central to the ability to juggle and balance responsibility of being the first/lead data scientist. Agile Data Science & Analytics Stack 3 4 Active Listeni ng Influen ce Collabora te with Metrics Explore Implement Grow Actionable Agile DS/A Stack is Key to Success 28