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Training Taster: Leading the way to become a data-driven organization
The document discusses becoming a data-driven organization. It provides an overview of the value chain of data science and an analytics maturity journey. The value chain of data science shows how data can be measured, optimized, used to generate predictions and insights, and ultimately create value. It emphasizes starting with the desired value and working backwards to the necessary data. The analytics maturity journey outlines four phases - initialization, continuous experimentation, enterprise empowerment, and data democratization - with different focuses at each stage to build analytical capabilities and business adoption of data and analytics. Key roles in a minimal viable data science team are also outlined.
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Training Taster: Leading the way to become a data-driven organization
- 1. Leading the way tobecome a data-driven organization Training Taster Steven van Duin
- 2. Agenda • Value Chainof Data Science • Break • Analytics Maturity Journey
- 3. Your facilitator Steven vanDuin Analytics Educator & Analytics Translator
- 4. Value Chain ofData Science
- 5. Goal of thissection Goal: Provide a framework for thinking about how data science can deliver value Topics: o Ways to get value from your data o Creating smart products and services o The Value Chain of Data Science o Apply the value chain to a case Let me explain how data science can deliver value to your business!
- 6. How to getvalue from your data? There are several options 1. Directly sell data (to customers) 2. Use data to generate insights • Allow better decision making • Sell them to customers 3. Use data to develop AI solutions • Improve the jobs and tasks of employees • Create smarter products for customers
- 7. AI solutions leveragedata science to get as close as possible to influencing the action My keywords for an AI solution are: “learning” and “end-to-end”! Human Decision Action Analytics Discipline Insights Level Descriptive Whathappened? Human Input Reporting Analysis Prescriptive Whatshould be done? Data Science Data Value Diagnostic Why did it happen? Decision Support Automated Data Products Predictive Whatwill happen?
- 8. Content Recommender Dissecting an AIsolution like the Netflix personalized series recommender Suggested series list Series to suggest Expected duration Already Watched Business requirements: • Increase VCR • Apply PG Ratings • Push Netflix Originals User requirements: • Good suggestions • Ease of use • Great experience Duration Prediction Data sources Rank & Filter Measure KPIs (VCR, CTR, ratings, etc)
- 9. The generic architectureof a full fledged AI solution Advised actions Predictive insights Predictive insights Business requirements User requirements Model Decision engine Measurement Data Other Data/insights Model UI
- 10. We can summarizethis conceptually with the “Value Chain of Data Science” Actions Insights Data Measure Optimize Predict Value What an AI solution does… …and its resulting (intermediate) outputs!
- 11. It all startswith data, preferably lots of relevant and high-quality data! Actions Insights Measure Optimize Predict Value Data
- 12. Algorithms need datafor training, validating and testing Actions Insights Measure Optimize Predict Value Data Train Validate Data set split Train model Determine performance Test Determine generalizability Study Sample exam Exam
- 13. Apply algorithms tomake predictions Actions Insights Measure Optimize Predict Value Data
- 14. Use the simplestpossible algorithm to predict, but not too simple Actions Insights Measure Optimize Predict Value Data Your model is too simple to predict reliably Model predicts future observations reliably Your model is great in memorizing, but does not generalize Underfitting Just right Overfitting
- 15. Nowadays getting allkinds of predictive insights is “easy” Actions Insights Measure Optimize Predict Value Data Regression (numeric target variable) Classification (categorical target variable) Clustering (groups of observations) E.g. number of products, minutes of delay, number of visitors, entity salience score, … E.g. churn yes/no, risk rating high/medium/low, car brand, … Groups with similar behaviour (e.g. website visits, customers, …), Groups with suspicious behaviour (e.g. fraudulent transactions)
- 16. Apply optimization totransform those predictions into prescriptions Descriptive Analytics What happened? Diagnostic Analytics Why did it happen? Predictive Analytics What will happen? Prescriptive Analytics What should I do? Prediction Optimization Information Hindsight Insight Foresight Actions Insights Measure Optimize Predict Value Data
- 17. So the insightsbecome actionable Actions Insights Measure Optimize Predict Value Data
- 18. Make sure youstore the taken actions and relevant metrics Actions Insights Measure Optimize Predict Value Data Model metrics Busines s Metrics Measures related to business value and KPI’s, e.g. revenue, churn rate, CPA, service level. Usage Metrics Measure related to the model and optimization performance, e.g. RMSE, MAPE, accuracy, precision Measures related end-user usage and satisfaction, e.g. % of followed action, # of calls made, feedback on UI/UX
- 19. Make it allthe way to the end, to finally create some value Actions Insights Measure Optimize Predict Value Data
- 20. Example case: Reducethe waste of fresh sandwiches sold during flights Actions Insights Data Measure Optimize Predict Value
- 21. Example case: beercolor prediction Actions Insights Data Measure Optimize Predict Value
- 22. Forward navigate thevalue chain of data science to discover relevant use cases Actions Insights Data Measure Optimize Predict Value
- 23. To discover viableand feasible use cases, work backwards from value to data Optimizing what actions? Using what insights? Learned from what data? What is the opportunity? Measure Optimize Predict Ask more business-oriented questions to uncover good ideas: • What’s your strategic focus for the coming year? • Where do you see opportunities for Advanced Analytics? • How do these opportunities relate to (upcoming) projects? • What are the business gain/pains that are being addressed? • Who stand to benefit from these projects, and how?
- 24. Many organizations startat the data and only go halfway Business challenges: • Optimize end user journey • Facilitate taking of actions • Measure outcome and value • Achieve business objectives Technical challenges: • Collect and clean the data • Build a predictive model • Generate and collect insights • Optimize and make actionable Actions Insights Data Measure Optimize Predict Value
- 25. Many fail becausethey focus on details, in isolation Value Action Model Data Insights Not this… ...but this! Value Action Insights Data
- 26. Key learnings: o Manyways to generate value with data o Use the Value Chain of Data Science to build analytics solutions o Start from the value, arrive at the data o Iterate over the whole value chain o Start small, create proof, then scale out
- 27. Quiz time! 1. Takeout your phone 2. Go to kahoot.it 3. Enter the pin that will be shown on the screen 4. Type in your nickname 5. The questions appear on the screen. Answer these with your phone (multiple choice) 6. The fastest correct answer gets most points
- 28.
- 29. What does ittake to become a Data & AI-driven organization? _ WHY Why do we need to become a Data & AI-driven organization? _ WHAT What do we need to mature our Data & AI capability? _ HOW How do we deploy our Data & AI capability to developAI products?
- 30. A restaurant analogy:kitchen & service area as one team Service Area aka your Data Science team aka the “business” Kitchen
- 31. Running a restaurantvs. writing a recipes book One of the main reason why businesses fail, is hiring Analytics researchers vs. people that can apply analytics to solve real-life problems As a restaurant, you need more than chefs. And you need the service area and kitchen to be aligned. VS
- 32. Data & AImaturity can be measured along two axes Analytical Capability Business Adoption • People & Skills • Tools & Technology • Data • Executive Support • Funding • Implementation
- 33. A typical Data& AI maturity journey consists of 4 phases Continuous Experimentation Enterprise Empowerment Initialization Business Adoption Analytical Capability Initialization • Find and initiate firstuse cases: identify opportunities, boot up data, people & tools Continuous Experimentation • Expand team and infrastructureas you are gradually implementing moredata & AI products. Enterprise Empowerment • Grow data & AI practice across allbusiness units and put business in driver’s seat; buy-in required! Data& AI Democratization • Data & AI literacy in genes of company; anyone has skills required to make data (& AI) driven decisions Data & AI Democratization
- 34. Each maturity phasehas a different focus Initialization • Find and initiate firstuse cases: identify opportunities, boot up data, people & tools • Proving value • “Can wedo it?”, “How hard is it?”, “What are the opportunities?” • Expand team and infrastructureas you are gradually implementing moreAI products • Capability building • “How to organize?”, “What skills and tech is needed?”, “How do I repeat and scale?” • Grow Data & AI practice across all business units and put business in driver’s seat • Business Adoption • “How to involvethe business?”, “Who is paying for new and matured use cases?” • Data & AI literacy in genes of company; anyonehas skills required to make Data (&AI) driven decisions • Enabling everyone • “How to supportData & AI driven decision making across the organization?”, “How to measureeffects?” Focus Questions State Enterprise Empowerment Continuous Experimentation Data & AI Democratization
- 35. Measure your Data& AI maturity to spot improvement opportunities Metrics Axes Score Curious about your AI maturity score? Check our AI maturity self-assessment: https://xebia.ai/ai_maturity_scan Team L&D Knowledge sharing Categories
- 36. Become a Data& AI-driven organization WHAT do we need to mature our Analytical Capability? • People & skills • Tools & technology • Data
- 37. 1. Get yourteam right
- 38. A successful teamneeds to get the following things ‘right’: • Project sponsorship • Analytics translation • Data science • Data engineering Tasks related to these topics can be done by different roles/people.
- 39. These roles formminimal viable project team Analytics Sponsor • Create a data & AI strategy and vision • Make budget and resources available • Oversee use case portfolio BIZ IT D&A Analytics Translator • Collect, prioritize, and validateideas • Drive the solution developmentprocess BIZ IT D&A Data Scientist • Explore the dataand train models • Explainfinal solution performance BIZ IT D&A Data Engineer • Extract data from data sources • Embed modelsin runnablesolution BIZ IT D&A
- 40. Different use casesrequire additional skills and roles Software engineering Agile coaching Legal & Compliance UI/UX design Domain expertise Plant Operator
- 42. 2. Grow yourtalent with L&D
- 43. Upskilling your kitchencrew involves training, technology and talent • Develop a talent strategy • Make choices on the AI enabling technology • Based on the talent and technologies you have, close the gaps with training • Tutor-led training • Self-paced training • On-the-job learning • Coaching Technology Proper tooling to develop AI solutions Training Foundation of data experts Talent Mix in seniority, theoretical expertise and business experience
- 44. Example learning journeyfor Data Scientist
- 45. Not only oncompany level, but also at bigger scale Xebia Data empowers medical students with AI
- 46. Training is justthe start though Tutor-led and self- paced learning On the job learning & Coaching Tracking KPIs and celebrating examples 3 1 Academy 2 Use cases Reinforcement 4 Community Making it real for business
- 47. 3. Share knowledgeand successes
- 48. Hackathons & innovationdays Energizing and fun way to try out new tools & technologies. Knowledge sharing helps you to spot new tools & tech, and prevents you from making the same mistake twice Regular project demos Adopt best practices between teams and prevent makes from being made twice. Meetups, conferences The more you share knowledge, the more it grows.
- 49. Maturing your People& Skills Analytical Capability Immature Mature • Mostly reliant on external consultants • Senior talent in-house • No vision & strategy for growing in-house talent • Clear career paths and training curriculum in place • Knowledge sharing is minimal or ad hoc • Constant knowledge sharing; learning is part of culture Examples of maturity indicators
- 50. Key learnings: o Yourminimum viable team of sponsoring, translation, data science and data engineering o Additional expertise will need to aid the team o Provide your technical talents growth perspective and training o Foster knowledge sharing to start a flywheel
- 51. That’s all Folks! At leastfor now…