Keeping Up With Data #105

Source: https://petrjanda.substack.com/p/the-art-of-drawing-lines

Disposable technology is one of the concepts I promote regularly. Bill Schmarzo wrote in 2019:

“Technology architecture should serve two basic purposes:

  1. Facilitate the capture, refinement, curation, sharing, management, governance and analysis of the company’s invaluable data assets.
  2. Build a technology architecture that doesn’t get in the way of point #1.”

It’s one of the concepts I’m regularly going back to. Case in point is that the three years old article speaks about technology elements like HDFS on-premise data storage.

The technology and data infrastructure is evolving so fast, that technologies we use today will probably be obsolete in 2–3 years. The key lies in understanding that the business value and differentiation lies in the data companies are collecting, curating, and using to power their business and operational models. Not in the technology used to do so.

Let’s always be guided by the business, not by technology, vendors, or missing use cases. We shall aim to build a robust reflection of the business in data — business data twin — and use it to solve the business’ problems at the given time.

Just like the data contracts in the image above (and an article below) are not structured around technology, but business.

But never give up the ownership of the data and the intelligence built on top of it!

This week’s reading list looks at data ethics, data contracts, and data strategy.

  • Data ethics: What it means and what it takes: “Now more than ever, every company is a data company,” starts the article. With the increasing role of data for companies’ operations and business models, I very much agree. And because I also think that data is a very powerful asset, we should use it wisely. That’s when data ethics come to play. I would have hoped that the amazing economic value hidden in data is motivating enough to get started with data. But if not, the article might do its trick by scaring the executives that they could even be held personally liable if the company isn’t managing and using data correctly. For many reasons I suggest to start with the value creating aspects of data, but the value preserving or defensive elements should never be overlooked. (McKinsey Digital)
  • The art of drawing lines: Data contracts are the talk of the town. As always, Petr complements latest data trends with his SWE background and business acumen. He provides a great explanation of architecture — the art of drawing lines to split the whole into part, where parts can evolve freely without breaking the whole. So when it comes to data contracts, the question is where should these sit? Petr argues that putting them between technological boundaries (between data ingest and data warehouse) might not be the right choice. “When finding the right interfaces, it pays off to think from a business and organizational point of view,” concludes the author. Data contracts might be coded rules for API interfaces, but — just like any other contract — they are negotiated, enforced, and broken by people. (Petr Janda)
  • The 4 Question Data Strategy Framework: Adam is sharing his powerful four-question framework for data strategy. He starts by assessing where the organisation is, then moves on where they want to get, and finally asks how to get there and where to start. It’s always hard to disagree with these high-level frameworks. And I don’t want to! “The approach is meant to be agile, lightweight, and quick — prioritising action and iteration over big up-front planning.” With that in mind I’d suggest to switch the first two steps. Start with the goal, only then analyse where you are. Not all about the current state is typically relevant. (Beyond Data)

Enjoy the weekend!

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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Adam Votava

Adam Votava

2.5K Followers

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