The next frontier in data is words not numbers
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Data analytics' big opportunity is analyzing words rather than numbers, says David Creelaman
HR Insights
HR Technology
Talent Analytics
Uncategorized
David Creelman
Rather predictably, perhaps, people analytics has tended to focus almost exclusively on numbers. One exception is sentiment analysis but thatâs relatively rare.
However, as historian, Yuval Noah Harari, often says, itâs âlanguageâ that is the operating system of civilization and no doubt it is the operating system of organizations as well.
So, if language really is the operating system, shouldnât we be analyzing words more than numbers?
I think we should.
Consider the analysis of meetings.
Sure, we could analyze some numbers â such as how many people were in the meeting, how long it lasted, and who spoke the most.
But compared to what a human might glean from actually listening to the words, and I think the insights are potentially far greater.
Words are getting easier to analyze
Until recently, the ability of a people analytics team to analyze words was very limited.
Now though, with large language models, a new world of opportunity has opened up.
The biggest limitation of analytics may not be the language analytics tools themselves but our imagination on how best to use them.
An example of language analysis
Letâs imagine our concern is whether a project team is likely to hit its milestones.
If itâs not, then the manager may want to intervene. Here are some things we might ask the large language model to look for in the meeting:
Was it clear who was meant to do what by when?
Did people address contentious issues or dance around them?
Was there too much disagreement? (or too much agreement?)
Did some team members appear to have âchecked outâ and were not committed to the project?
You can see how radically different this kind of analysis is from the number-focused analysis we are used to.
Instead of gathering numbers and trying to glean insights from them, we just ask the large language model some direct questions.
We use the large language model much like we would use a human observer who was giving us feedback about a team meeting.
How to get started
Since meetings are such a common occurrence and are full of language, then analyzing meeting transcripts would be a good place to start in shifting people analytics from focusing only on numbers to focusing on numbers and words.
The people analytics team could work with a manager who is responsible for several teams and experiment with different prompts to see what a large language model could glean about the state of the project from the meeting transcript.
It might not amount to much, but it might provide important insights, you wonât know until you start experimenting.
Postscript: Did I lack imagination?
I suggested several specific prompts that you might use to see if anything was going off the rails in a project team meeting.
However, large language models are so smart that you could potentially ask something along the lines of â Make a list of common indicators a team is going off track. Review this transcript of a team meeting and report on those indicators.â
Large language models truly are astonishing tools.
Itâs time that people analytics teams reoriented their efforts to take advantage of this new world of analysis that focuses on words not numbers.
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Data analytics' big opportunity is analyzing words rather than numbers, says David Creelaman. HR Insights. HR Technology. Talent Analytics. Uncategorized. David Creelman. Rather predictably, perhaps, people analytics has tended to focus almost exclusively on numbers. One exception is sentiment analysis but thatâs relatively rare. However, as historian, Yuval Noah Harari, often says, itâs âlanguageâ that is the operating system of civilization and no doubt it is the operating system of organizations as well. So, if language really is the operating system, shouldnât we be analyzing words more than numbers? I think we should. Consider the analysis of meetings. Sure, we could analyze some numbers â such as how many people were in the meeting, how long it lasted, and who spoke the most. But compared to what a human might glean from actually listening to the words, and I think the insights are potentially far greater. Words are getting easier to analyze. Until recently, the ability of a people analytics team to analyze words was very limited. Now though, with large language models, a new world of opportunity has opened up. The biggest limitation of analytics may not be the language analytics tools themselves but our imagination on how best to use them. An example of language analysis. Letâs imagine our concern is whether a project team is likely to hit its milestones. If itâs not, then the manager may want to intervene. Here are some things we might ask the large language model to look for in the meeting: Was it clear who was meant to do what by when? Did people address contentious issues or dance around them? Was there too much disagreement? (or too much agreement?) Did some team members appear to have âchecked outâ and were not committed to the project? You can see how radically different this kind of analysis is from the number-focused analysis we are used to. Instead of gathering numbers and trying to glean insights from them, we just ask the large language model some direct questions. We use the large language model much like we would use a human observer who was giving us feedback about a team meeting. How to get started. Since meetings are such a common occurrence and are full of language, then analyzing meeting transcripts would be a good place to start in shifting people analytics from focusing only on numbers to focusing on numbers and words. The people analytics team could work with a manager who is responsible for several teams and experiment with different prompts to see what a large language model could glean about the state of the project from the meeting transcript. It might not amount to much, but it might provide important insights, you wonât know until you start experimenting. Postscript: Did I lack imagination? I suggested several specific prompts that you might use to see if anything was going off the rails in a project team meeting. However, large language models are so smart that you could potentially ask something along the lines of â Make a list of common indicators a team is going off track. Review this transcript of a team meeting and report on those indicators.â Large language models truly are astonishing tools. Itâs time that people analytics teams reoriented their efforts to take advantage of this new world of analysis that focuses on words not numbers. Collision 2023 proves why HR events still have the power to surprise. David Creelman. | Aug 9, 2023. Transforming recruitment with AI-backed diversity tools. Peter Crush. | Jul 31, 2023. Top AI tools that improve the employee experience (and make your job easier) Jordyn Greenberg. | Jul 27, 2023. Analytics teams need to put AI top of their to-do lists. David Creelman. | Jul 24, 2023. Can generative AI correct recruitmentâs course? Shannon Pritchett. | Jul 13, 2023.