Keeping Up With Data — Week 34 Reading List

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readAug 27, 2021

This week I had an opportunity to talk on a webinar about data adoption from the front line to the C-suite organised by my friends from Keboola. We spoke a lot about the importance of using data for solving the right business problems, building a digital twin of the business, ensuring the quality and reliability of data, driving data fluency and designing self-service analytics around the business users. There was also a demo of ThoughtSpot platform and it looks absolutely amazing!

Apart from preparing for the webinar and catching up on writing some longer data blog pieces (stay tuned!), I’ve read some nice articles too.

  • The 4 Kinds of “Data Moats” Your Company Can Build: Warren Buffet has popularised the importance of economic moat around a company. There are four main ways data can play a role in building a business moat — a strong competitive advantage: data can be used to provide a great operational advantage (like they did for Gojek near real-time management); it can be used to drive strategic decisions (like opening thousands of LPG stations); it can drive a core product advantage (think Netflix insight into our movie preferences); and it can drive new opportunities (like Netflix using the data to create their own content). (Prukalpa @ TDS)
  • How does Airbnb track and measure growth marketing? Marketing — traditionally a very creative field — offers a fantastic playing ground for data professionals. There is so many opportunities for data-driven decision making and automation leveraging data and ML pipelines. But the key is to have high-quality, reliable data. When you are tracking 100M+marketing ads like Airbnb, you need to have a very good system for collecting and processing the data. Read more about C-parameters, C-tracking and a suite of tools responsible for ingesting and processing the data before ending up in an offline data layer. (Jing Guo @ The Airbnb Tech Blog)
  • 5 things I’ve learned from building analytics stacks at J.P. Morgan and Fivetran: It’s always great to be able to capitalise on the experience of others. Building an analytics function is not an easy task, but advice given in the article can make it much easier. If only someone told me ten years ago! Point number 4 — running a data team as an R&D team — struck a chord with me especially strongly. Combining a product approach to data, engineering principles for operations and customer-centric mentality is the winning formula. (Veronica M. Zhai @ TDS)

Last weekend I challenged myself to do a long run with a serious runner preparing for a sub 3-hour marathon. I haven’t been running much (read: at all) in the last couple of years, so the 27km we did in the rolling terrain has been with me till mid this week. Waking up before 4am to drive to the Alps for a 100km cold and rainy bike ride on Sunday probably hasn’t helped much. I’ll definitely keep it low key this weekend!

Thanks for reading!

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

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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)