Data and data scientists are not equal to money david hoyle
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Dr. David Hoyle discusses the complexities of applying data science in businesses, emphasizing that cultural and organizational challenges often outweigh technical ones. He suggests building cross-functional teams and leveraging agile methodologies to align data science projects with direct profit and loss impacts, while also ensuring clear communication throughout the process. The document outlines the importance of understanding business problems, mapping them to appropriate data science methods, and continuously monitoring and improving models.
Data and data scientists are not equal to money david hoyle
- 1. Data + Data Scientists ≠ Money Dr. David Hoyle
- 2. My background PRODUCING DATA SCIENCE • 20 yrs in academia • dunnhumby • dunnhumby APPLYING DATA SCIENCE • Lloyds Banking Group • AutoTrader UK • InfinityWorks The challenges in applying Data Science are very different
- 4. How did we get here? Your Company Inc.
- 5. Cultural and organizational challenges are always harder than technical challenges • Which parts of the business? • How should we organize? • How should we work? • How should we communicate? • What support do we need? • What data should we use? • SVM vs Logistic Regression?
- 6. Where do companies need Data Science?
- 7. Data touchpoints Bad guys Account Manager Marketing Developers OEMs Private seller £, €, $ Finance Car Dealer Consumer External Data Internal Data £ Trade£ Retail
- 8. ‘Why?’ is a powerful Data Science tool How will you consume the outputs? ‘We need a neural network’ ‘We want to predict if users will click this link’ ‘Not clicking indicates low user engagement’ Why? Why? ‘We can alter the content in session if engagement is low’ Can you respond to the neural network output fast enough? ‘Hmmm… No.’
- 9. Build cross-functional teams Data Scientist ≠ Data Engineer Data Science Data Engineering Product
- 10. 10 Get close to the business Analytics Team Product Area C Analyst Product Area A Analyst Product Area B Analyst Business Area 2 Analyst Business Area 1 Analyst Data Scientists + Data Analysts
- 12. All parts of Data Science have outputs INFORMATION PRESENTATION ALGORITHM DEVELOPMENT Easier to communicate outputs Easier to communicate progress Harder to communicate outputs Harder to communicate progress Always communicate what the outputs will be
- 13. Understand business problem Map to appropriate abstraction Mathematical statement of abstraction Identify type of mathematical model required Identify & explore potential data sources Build, validate, & test model, e.g. CRISP-DM Productionize model Deploy production model artefacts Consume model outputs Monitor production model Re-build production model Improve model Data Science Data Engineering Data Science Data Engineering Data Engineering Data Engineering Data Science Data Science Understand & conceptualize the problem Understand resources available & build model Incorporate model into business process Monitor & improve The Data Science innovation lifecycle is longer than you think
- 14. Data & Compute should be close together Operational Operational + Data Warehouse SQL, BI
- 15. Not all data is valuable Either Your data is valuable to you – e.g. helps improve business processes Or Your data is valuable to someone else – e.g. gives a market wide view
- 16. To make Data Science pay you need to 1. Work on the projects with direct P&L impact 2. …..by asking the right business questions up-front 3. …..using teams that have the technical right skills 4. …..and understand the business challenges 5. …..using Agile methodologies where appropriate 6. …..always communicating what you are doing and why 7. …..with the right tools and on the right data