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The Data Strategist's Playbook

Your weekly collection of recipes to help you define a data strategy and add value to your business.

I curate and share content like blog posts, opinion pieces and recordings from conference talks to help you find your way in this constantly changing ecosystem. I'll publish a new issue every Wednesday and promise to keep it short and relevant.

In this newsletter, you'll read about:

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Highlights

Here are the some of the most popular articles from recent episodes:

Data Platforms for 2020 & Beyond

An interesting argument: data lakes are created the FOMO way; useful only to a handful of specialists in your organisation because the data lacks clarity. The author describes why domain-specific data services are the logical successor to data lakes.

Numbers Every Data Engineer Should Know

A list of data pipeline-related metrics that help understand whether or not all components in your data infrastructure are running nicely or should be closely monitored and improved upon.

Why Great Data Engineering Needs Automated Testing

Even though writing tests for ever-changing data isn't as easy and straightforward as for regular code, it shouldn't be an excuse to skip automated testing. Don't aim for 100% coverage; start assuring your riskiest metrics are always correct and work your way to the (data) source.

Building A Data Driven Culture — One Step at a Time

This is interesting: when creating a data-driven culture, pick a problem that is small and easy to solve, make sure the solution is not only presented but implemented, pick a slightly bigger problem and repeat.

Data Quality at Airbnb. Part 1 — Rebuilding at Scale

Rewriting software or rebuilding systems from scratch is unpopular with managers because it's expensive and at first glance the result is identical to what you already have when changes happen under the hood. Airbnb did rebuild their data infrastructure to ensure higher quality data and share their approach.

How should our company structure our data team?

There a few ways to set up data teams with data engineers and data analysts and their colleagues in other teams. In this interesting case study, we learn how travel start-up Snaptravel iterated over classic models like centralised and embedded teams and what their ideal team structure looks like.

Go to the archives to see all previous issues and articles.

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