Keeping Up With Data — Week 37 Reading List

Source: https://www.datasciencecentral.com/profiles/blogs/schmarzo-s-economic-digital-asset-valuation-theorem-formulas

This week has been a beautiful demonstration of the sheer variety of the data world! There is such a broad spectrum of puzzles one can face in a data career — how to strategically leverage data to create business value? What data stack to go for? Who to partner with? How to compel people about the value of data? I must admit, I absolutely love this unpredictable nature of data-related challenges.

While contemplating on some of the questions mentioned above, I enjoyed reading the following articles this week.

  • Fast-Track Data Monetization With Strategic Data Assets: Data is not a regular asset. It can be reused and recombined. The easier that happens, the more liquid the data is. This property of data offers a fantastic opportunity for strategic data assets — data that holds potential for future value creation and appropriation. “As more of a company’s strategic data assets become more highly liquid, data is made increasingly available for conversion to value, accelerating the company’s data monetization,” says Barbara H. Wixom et al. Which ultimately leads to non-linear value creation. Which reminded me of Bill Schmarzo’s slide above. (MIT Sloan)
  • Debunking Data Myths And Misconceptions With Dun & Bradstreet’s Chief Data Scientist, Anthony Scriffignano: While it’s easy to get charmed by the almost magical powers of data, Anthony Scriffignano reminds us of dangers when data isn’t reflecting reality or is incomplete. We can’t rely on data to automatically give us all the answers. “Even with vast amounts of data available and limitless potential contained within, it is always important to remember to stop and think. Data has value, but to get context one must look beyond the data itself.” (Forbes)
  • We the purple people: The term analytics engineering has been used in the last two years. As with any new entry on the list of data professions, it has led to many debates. Is it a more technical role? Or rather business-oriented? Or is it both? Can it be both? Or maybe even more? It seems that people sitting somewhere between the business domain experts ❤️, and the technical experts 💙 can be hugely valuable. The future is not people with either business knowledge, or with technical skills, it’s people with both — the purple people 💜. I’m often using a bridge analogy to describe this convergence. But the ‘purple people’ offers one extra dimension — the future tools are also purple. (dbt blog)

That’s it for the data. Now time to plan a nice little family trip for the weekend!

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

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Adam Votava

Adam Votava

Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | Interim CDO at DataDiligence.com