Data Struggling
Management

4 reasons for Agile in Analytics

Working in several projects as Data Science consultant I’ve realized about the need of spreading the word about project planning in this field. This is neither an Agile apology nor an open letter criticizing project managers (PMs) that prefer other methodologies. It’s more a post trying to help analysts struggling with deadlines when their manager’s Gantt diagram is not really helpful.

There are tons of different project management methodologies (see a nice article here), and different ways to apply Agile (doing or being Agile). However, in this article I’ll focus on a comparative Waterfall vs Agile (doing) approaches as, on my experience, they’ve been the most commonly used in big organizations.

The traditional waterfall methodologies have many advantages such as good traceability of the progress inside the project life cycle or its ease of use for the PM. But although these make life easier for Project Managers, they create many challenges for the actual ‘doers’, especially if the project requires data exploration.

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