Data Struggling
Management

4 reasons for Agile in Analytics

Working on several projects as a Data Science consultant, I’ve realized the need to spread the word about project planning in this field. This is neither an Agile apology nor an open letter criticizing project managers (PMs) who prefer other methodologies. It’s more of 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, in my experience, they’ve been the most commonly used in big organizations.

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

Related posts

A summary of how AI has progressed in the last 5 years and current challenges (by ChatGPT)

cetrulin
2 years ago

Scraping stock prices using Alpha Vantage and Google Finance

cetrulin
8 years ago

Flattening complex XML structures into Hive tables using Spark DFs

cetrulin
8 years ago
Exit mobile version