The Horror: Actionable Insights

This image should terrify you.

And yet… (and yet), this is the norm in much of the business world. Best practices and expertise being applied to designs that then go to usability and user testing, but often-times without the structure to truly pull actionable metrics at anywhere close to scale.

Glare is all about breaking out of the horror of digging for the narrative buried in user interviews. Digging up that one deck the team had mentioned or shared a few months ago.

What about you:

  • How does that hat strike you?
  • Do you feel a resentment toward the effort required to gather ‘actionable business insights’?
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“As I grew older, the data itself started eating into my skin, consuming me, making it extremely hard to rip it off and form it into the hideous yet most obvious object of depravity – actionable business insights…"

I would say that we might resent the effort required, but I personally find the actionable business insight to be the most interesting yet valuable piece of the puzzle!

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Also, I kinda want the hat :rofl:

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This hat is basically the corporate version of ‘live, laugh, love’ :smiling_face_with_tear:

My first question: where does all that unstructured data come from? There must have been a reason to start tracking certain data points.

Either way, I feel like @Helge made a great point here about connecting seemingly vague data points to key business context in order to surface actionable insights: Systems Thinking with AI (Glaringly Obvious) - #3 by Helge

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I’d agree with this @MoData - “where does all that unstructured data come from?”

Hidden within this interview Clayton Christensen suggests that (paraphrasing): https://youtu.be/IkBp1ntD3Zc?si=m5KYhunfEemXkCAW

a. when companies are founded their perspetive / data is about the customer opportunity. It’s data about context, what people are trying achieve in their lives etc.. And the company responds to that data developing a product that solves just that. [this data is messy]

b. but then as the company becomes successful the nature of the data that surrounds the executives changes. And now the data is all about products and features [simple, quantitative data that fits into excel spreadsheet cells and can be translated into graphs]. And so management looses their insights about their customers reason for hiring the product. And now they believe that their business is about improving the efficiency with which they make and the effectiveness of their own products and features. And with it the company buries what makes it great.

I like ‘thick data’ (and Snowdens ‘rich data’ and sensemaker). As to me they represent a potential to bridge both worlds.

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