Keeping Up With Data — Week 33 Reading List


The image above is from — a website based on Wikipedia data and visualised in an appealing, interactive way. If you find yourself occasionally browsing through Wikipedia without any particular goal — like I do — you will enjoy the histography website — like I have.

I’m still staying on the topics I’ve been reading on in the last couple of weeks — data literacy and self-service. These are accompanied by data mastering in this week’s reading list.

  • The Growing Importance of Data and AI Literacy — Part 1: Data literacy is a big topic for organisations as it is key to unlocking the business potential of the data. However, it is not an important topic only to organisations, as we are reminded by Bill Schmarzo. What if we look at the customer data from the angle of the customer — i.e., us? How do we navigate through the complex and sophisticated ways organisations are capturing our data for their own monetisation purposes? Microeconomics teaches us that when presented with facts, people with different preferences can make different decisions. No matter how much we value convenience or privacy, we should be aware of what data about us are being collected. (Data Science Central)
  • Can you achieve self-service analytics amid low data literacy? As companies are democratising data and encouraging data-driven decision making, two topics are often raised. Data literacy and self-service analytics. If none exists (which is usually the case), which should come first? Both. Is the answer of another data guru — Cindi Howson. She uses a nice analogy of ‘teaching a child to read without giving them any books on which to build their skills’. There is one more takeaway from the article: when she speaks about data literacy, Cindi brings a term ‘data fluency’, which is about thinking in data terms. I like this much more. Nobody wants to be illiterate, which makes the topic of data literacy slightly intimidating. Data fluency — the ability to think in data terms — is imho a much better term for using data in the business context. (ThoughtSpot Blog)
  • One Key Mantra for Chief Data Officers: “Source Data is Not the Master”: Data mastering is often a topic for data leaders — especially in larger organisations. What we need to accept is that a single or few data sources will never be enough (in fact, more is better) and none of them will (ever) be perfect. But these imperfections in (our many) data sources don’t rule out succeeding a data mastering project. And they shouldn’t prevent us from acting. The suggested approach lies in identifying and solving problems at a consumption level and then working upstream. This is (a) more practical and (b) targeted approach. And remember — “the source is not the master”. (Tamr Blog)

Big sport plans for the weekend, which in my case usually involves a very early wake up, so enough reading for me now. I’ll be speaking at a webinar about data adoption next Wednesday. Very interesting topics, so I invite you to join!

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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