Keeping Up With Data #81


My personal observation from the last couple of months is that more and more companies are shifting the philosophy behind their approach to data. And apparently Doug Laney has seen the same based on his quote in the Forbes article by Randy Bean: “Many senior executives talk about information as one of their most important assets, but few behave as if it is.” It is the case with many new things — first we need a shift in mindsets, then we must nail the execution. Data is no different in that.

Today’s reading list comprises a mix of data philosophy and data execution.

  • The CDO/CIO Dynamic: The Business-Of-Data Meets The Technology-Of-Data: Data is not a technical problem any more. As the companies go through digitalisation and integration of various systems and processes, the opportunities for using data as an economic asset arise. There are two fundamental dynamics to that — business of data and technology of data. With the CIOs being key in delivering the digital transformation, the new role of CDO/CDAO can be very complementary. Finding opportunities to get value out of data is not threatening to CIOs, it’s benefiting them as it provides additional returns on their massive transformational investments. (Forbes)
  • An Interactive Guide to Hypothesis Testing in Python: “Hypothesis testing is an essential part in inferential statistics where we use observed data in a sample to draw conclusions about unobserved data — often the population.” It is also an important component of data-driven decision making. It starts with choosing the null (and alternative) hypothesis, selecting the appropriate statistical test (with T-test, ANOVA, and Chi-Squared being the most common ones), calculating p-value, and determining the statistical significance. It takes a bit of an effort, compared to just eyeballing the numbers or charts, but it’s worth doing especially for high-stakes decisions. (TDS)
  • Modern data management, the hidden brain of AI: Modern AI algorithms are inspired by the functioning of a human brain, with data being the memory and experience in the analogy. And thus even data management is getting a lot of ideas from how humans collect, process, and store information. But perhaps there is more to copy! People are often unconscious of the nature of their own behaviour and act according to social motivations. Something we could perhaps describe as ‘culture’. And this culture component is critical in data management too. We need ‘social motivations’ for our approach to data. We need a shift in mindset to e.g., measure data in economic terms and not accounting terms. (MIT Technology Review)

Busy week with long days and sleepless nights (to support our teething son) should be followed by a nice weekend. With the in-laws visiting us, I might get a chance to squeeze in a longer bike ride!

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

Thanks for reading!

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

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

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Adam Votava

Adam Votava

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

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