Keeping Up With Data #109

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readNov 18, 2022

Despite 20 years of investment dashboard adoption falls flat. This is what we read in The decade of data e-book by ThoughtSpot. It also brings the chart above showing the adoption stalling at around 30%.

ThoughtSpot’s answer to the problem has been letting the users ask the questions, not just view dashboards created for them. I like the approach because it puts the spotlight on two aspects of business intelligence (and data analytics more broadly).

First one is having a robust, credible, and representative data (model) well reflecting the business. And the second is about the ability to ask (data) the right questions.

Neither is easy, but both are critical for adoption. And in all honesty, I think they are far more important that whether you are googling the data or going through dashboards created for you.

This week’s reading list looks at BI, self destructive dashboards, and AI.

  • The Real Numbers Behind Failed Dashboard Adoption: Despite heavy investment into BI the adoption of dashboards by business users is desperately low. This article states that “70% of the development work that is used to build dashboards is wasted when the dashboard is not fully adopted by the business users that it is intended for.” I am a fan of the “google-like” search bar of ThoughtSpot, but having worked with companies from traditional industries with large workforce going through data transformations I also believe that in certain situations the question should be dictated (promoted) by the organisation, not left up to the individuals. Either way, ensuring higher adoption by having robust data models, solid understanding of the business, and willingness to go extra mile for the business users is worth the effort. (Great Data Minds)
  • What if every dashboard self destructed: The issue with low adoption of BI is not only the effort to build a dashboard is lost. It also creates unnecessary maintenance costs. And let’s not even talk about the ambiguity it creates for business users who are left guessing if the dashboard they are using is still working (and will continue to do so). Could all this be solved by making every dashboard self destructed? Is it even technically feasible? And is there any value in having the dead dashboards lying around? (Counting Stuff)
  • AI: The tech of 2023 is AI. It comes after wearables, mobile, 3D printing, blockchain, Web 3.0, the metaverse, and many others. Like with all the new things in tech, it comes with increased number of startups and venture funding. But with AI we also see accelerating progress and adoption. It is not only curating our search results, recommending movies and songs, or driving our cars. It is now even discovering new drugs. AI is already part of our lives, it is getting better, and the funding is increasing. Will it live up to the hype? Or even exceed it? (No Mercy / No Malice)

Enjoy the weekend!

In case you missed the last week’s issue of Keeping up with data

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Adam Votava

Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)