Building and Scaling Data Science Capabilities
Building and scaling data science capability is an imperative for enterprises and startups aiming to adopt a data-driven lens for their business. However, crafting a successful data-science strategy is not straightforward and requires answering the following questions:
- Strategy & Tactics: What part of the business should I target first for adoption? Should I take a jump-start approach or a bootstrap approach?
- Process & Systems: How should I set up an initial process for data science? How to integrate data-driven processes with existing business systems?
- Structure & Roles: Should I adopt a functional or a business-focused data science structure? What specialized roles should I be hiring for Data engineering, ML expert, Visualisation expert, and /or Data Analyst?
- Tools & Stack: Should I build a vertical or horizontal data science stack? How do I integrate data science models with existing applications?
- Engineering & Technical: What are the pitfalls to watch out for? How to avoid pre-mature over-engineering of data science? How to manage the ongoing technical debt for data science?
- Skills & Competencies: How do I up-skill and build differentiated data-science competency across the organization?
The speakers draw upon their experiences in setting up and advising data science teams at enterprises and startups to share best practices on how to craft a successful data strategy and then go on to execute it. The will use case-studies to discuss what worked and failure points to watch out for.
- Learn best practices on how to craft a successful data strategy and then go on to execute it
Enterprises and Startups wanting to build Data Science Culture