Hirschmann, a legendary economist and considered by Gladwell and many others to be one of the greatest thinkers of the 2

Specified ranges require a computed number to be within a pre-determined interval, such as “values should be zero for 1–3% of records” or “the mean of a column should be between 13 and 16”. These can be used when you know in advance that a key metric or data statistic should never drift outside of a range.

My dad was in the military. I can’t count the number of times he said these words to me when I was a kid. Regardless of the warnings, like a lot of people, I still spend an extreme amount of time in my head either ruminating over past decisions or worrying about future ones.

Let’s consider an example. The European CDC provides COVID-19 data hosted in BigQuery here. In addition to statistics like cases and deaths broken out by country and date, this dataset also tracks intensive care patients.

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eed one thing — a solid plan. And this article provides it for you, irrelevant of your background. It boils down to three essential points. Learning these will help you get a job as a data scientist even if you don’t have a college degree:

Of course, there are still very good reasons to use static tests. They are powerful when you know exactly how your data should behave, and want to be alerted even if the data varies only slightly from this expectation.

But relying only on static tests leads either to poor test coverage — where the majority of important data is not well tested, or to a high maintenance burden that will prevent a testing strategy from being sustainable.

The Stoics centred their lives around being good people and living good lives — lives dictated by virtues and living life to the fullest. Stoicism can provide a blueprint for us, to help us to lead good and happy lives. The Stoics had practices in which they engaged daily, from their waking moments until going to sleep, that provided the structure necessary for a day lived well. We can use these practices, thousands of years later, to help us to live well in a busy, distracted, and stressed society. Following these practices daily will help us to develop and grow — to be better people and to achieve more success.

In this post, we outline a framework for data testing, from static tests that can be written in SQL, to dynamic tests that require statistics or machine learning. Then we compare both approaches with an example from COVID-19 data in the EU.

Predicted ranges are just like specified ranges, except the range is predicted by a time series model. The user can control how much uncertainty should be in the predicted interval, such as “the mean is within a 95% predicted confidence interval”. These are more powerful tests that can find any significant change in key metrics or summary statistics.

When asked by podcast host Cathy Heller for suggestions for how to stop overthinking so much, Malcolm immediately shared a piece of advice he learned from one of his heroes: Albert O. Hirschmann.

When testing data, our first instinct is to reach for perfection. Can’t we write down a clear set of rules that govern exactly how our data should behave, just like we do when testing software?

Dynamic testing strategies such as predicted ranges or unsupervised detection have some significant advantages. They are easier to set up and easier to maintain over time. They can also be used to test any data for any condition, regardless of the current quality of the data.

As your organization grows your business decisions, processes, products and code can all change your data in unexpected ways. And your data is truly at the mercy of many external factors. From how users behave, to what events occur, to the combined actions of competitors, suppliers or market forces.

Fixed rules make a statement in absolute terms about a dataset, such as “this column is never NULL” or “this string always matches a pattern.” These tests are great when your data must be perfect in some clear and known way.

Unsupervised detection is the most sophisticated approach, where anomalous changes are found in an important dataset. All that is required is that the user specify what data is important. Such tests can identify unexpected changes that you hadn’t thought to test for. Stay tuned for future posts on unsupervised detection.