Convergent vs Divergent Data Analytics in Manufacturing - Episode 8 of The TechOps Podcast

Convergent vs Divergent Data Analytics in Manufacturing

In Episode 8 of The TechOps Podcast, hosted by Dan Saavedra, the founder of MergeYourData.com, the spotlight is on the varying perceptions and applications of data analytics in the manufacturing industry.

Key Takeaways:

1. Understanding Convergent and Divergent Thinking

Convergent Thinking: Perceived as leading to a single correct solution, much like solving a mathematical equation.

Divergent Thinking: Opens up avenues to multiple potential solutions, promoting a more explorative approach.

2. Role in Manufacturing

Manufacturing typically leans towards convergent solutions due to its defined nature. However, applying data analytics with a divergent approach can yield a broader spectrum of solutions, especially for business-centric problems.

3. The Iterative Cycle

Incorporating both thinking methods in an iterative manner optimizes the data analytics process. Begin with a convergent mindset to pin down a problem, transition to divergent thinking for solution exploration, and revert to convergent thinking for solution implementation.

4. Business vs. Engineering Problems

Engineering problems have set solutions based on specific variable changes. On the other hand, business issues, covering areas like marketing and efficiency, benefit more from a divergent approach due to their multifaceted nature.

5. Harnessing Divergent Thinking for Maximal Value

Monitoring specific metrics is a convergent activity. However, the crux of data analytics value is realized when applied divergently. This fosters an environment where multiple potential solutions can be identified, leading to informed and diverse decision-making.

Want to take advantage of creative data thinking?