Detecting Strategic Isomorphism: A Big Data NLP Approach to Moat Erosion

A big data NLP approach that detects strategic isomorphism to reveal moat erosion.

A big data NLP approach that detects strategic isomorphism to reveal moat erosion. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to turn raw data into decisions: collecting and cleaning data, exploring it, modelling it and presenting the findings in a dashboard or report.

The main parts are data collection and cleaning, exploratory analysis and feature building, modelling and validation, and an interactive dashboard of the results. The reference build uses Apache Spark, NLTK, Python, Pandas, and Power BI, with a clear project structure, setup instructions and sample data so that it can be run and extended.