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The next frontier in data is words not numbers

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Summary

This article discusses how data analytics is now beginning to focus on words instead of numbers. It suggests that analyzing words is the true operating system of organizations, and should be given more attention. A large language model can now be used to analyze words, and can be used to ask questions about a project team meeting, such as if it was clear who was meant to do what by when, or if there was too much disagreement. People analytics teams should start experimenting with analyzing meeting transcripts to shift their focus from numbers to words.

Q&As

What is the opportunity for data analytics in analyzing words rather than numbers?
The opportunity for data analytics in analyzing words rather than numbers is to gain insights into the operating system of organizations.

How can large language models be used to analyze team meetings?
Large language models can be used to analyze team meetings by asking direct questions about who was meant to do what by when, whether contentious issues were addressed, if there was too much disagreement or agreement, and if any team members had "checked out" and were not committed to the project.

What kinds of insights can be gained from analyzing words rather than numbers?
Insights that can be gained from analyzing words rather than numbers include understanding the dynamics of the team, identifying potential issues, and recognizing when a team is likely to hit its milestones.

How can organizations use large language models to gain better insights?
Organizations can use large language models to gain better insights by analyzing meeting transcripts and experimenting with different prompts to see what the large language model can glean about the state of the project.

What are the advantages of using large language models to analyze team meetings?
The advantages of using large language models to analyze team meetings include being able to ask direct questions and being able to use the large language model like a human observer to provide feedback.

AI Comments

👍 This article provides great insight into how large language models can provide valuable insights into team meetings. It's also full of useful examples of how to use the models to gain useful insights.

👎 This article largely focuses on the potential of large language models and doesn't provide enough practical advice on how to get started using them.

AI Discussion

Me: It's about how people analytics teams should be focusing more on analyzing words rather than numbers. It also talks about how large language models are opening up a world of opportunity for data analysis.

Friend: Interesting. So what are the implications of this article?

Me: Well, people analytics teams need to shift their focus from number-focused analysis to a mix of number and language analysis. This could help them uncover insights about the state of projects, teams, and organizations that they wouldn't have seen before. Additionally, large language models could help them identify indicators of when teams are going off track. This could help them intervene and get projects back on track quicker.

Action items

Research language models and their capabilities to better understand how they can be used to analyze words.
Experiment with different prompts to see what insights a large language model can provide from meeting transcripts.
Develop a strategy for incorporating language analysis into people analytics initiatives.

Technical terms

Data Analytics: The process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software.
Sentiment Analysis: The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc., is positive, negative, or neutral.
Language Model: A set of statistical techniques used to predict the likelihood of a sequence of words.
Large Language Model: A type of language model that uses a large amount of data to generate more accurate predictions.
AI-Backed Diversity Tools: Tools that use artificial intelligence to help organizations identify and address diversity issues in their recruitment processes.