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?
- Skills & Competencies: How do I up-skill and build differentiated data-science competency across the organization?
- 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?