Keeping Up With Data #54

Source: https://www.datasciencecentral.com/profiles/blogs/reframing-the-cxo-conversation-from-data-monetization-to-value

Keeping Up With Data has turned one last week and with that I decided to shift away from sequence labelled by week numbers. Apart from this cosmetic change, Bill Schmarzo ran a LinkedIn Poll asking for a better term than ‘data monetisation’ (which often evokes selling the data). After collecting over 1,200 votes for one of the four options posed, he went for a fifth one — ‘data-driven economic value creation’. ‘Value creation’ is a common term in private equity used to describe ways to generate attractive investors returns. It’s great to see terms linked to data converging to terminology used when making businesses more valuable.

I’ve been reading a lot about data engineering lately. And that obviously impacts this week’s reading list: Data engineering failures, challenges on modern data stack in real life, and experimentation as a company strategy.

  • Data engineering failure — Why is it almost impossible to meet deadlines? Data engineering isn’t without operational challenges. It is not uncommon for data engineers to struggle with setting and, critically, meeting deadlines. Why? Because data engineers are bad at prioritisation, their projects runs on different schedule, and data engineering is still a very young discipline. But fear not, Christophe is here with possible solutions to these problems. Data engineers shouldn’t be afraid of failure. Working on challenging projects is the fastest way to master the craft. Per aspera ad astra! (blef.fr)
  • An introduction to Monzo’s data stack: Everyone is talking about the modern data stack. How does that look like in a bank dealing with massive volumes of transactional data? What challenges come with having 4700 models in a dbt project with ~600k lines of SQL? Monzo is heavily influenced by thought leadership of Tristan Handy, Benn Stancil, or Zhamak Dehghani. And their choices on the data platform and structure of the data team are guided by the principles of centralised data management, and decentralised value creation. It’s absolutely amazing how publicly open they are about the data-related challenges they are facing. I think it has a life saving potential for many. (Monzo)
  • Experimentation as a company strategy: During my career, I’ve worked in, for, or with companies large and small, conservative and innovative, fast and slow. I’ve been on ‘tankers’ and on ‘speedboats’ too. I’ve even seen speedboats turn tankers and tankers becoming speedboats. The analogy might indicate the large companies are tankers, small are speedboats. But in fact, the size of the company isn’t the only factor influencing its ability to move fast. Experimentation culture — where decisions are made at the right level, supported by high quality data, and made by empowered people supported by the right tools — are the signs of a speedboat. Don’t forget that data-driven decision making is superior to HiPPO. (Mikkel does data)

I have big plans for the weekend — a first multi-day trip for our family of four. Hope the fall in Lugano will be as magical as two years ago. It will be strange not to take my bike with me, but a trail run in the morning should be a solid substitution!

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 DataDiligence.com

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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

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