Myths and Principles of Data Usability

Big Data, predictive analytics, real time analytics, machine learning, are today’s top buzzwords. They show up in job descriptions, tech articles, industry panel interviews and they inevitably snake their way into product roadmaps. It’s easy to believe that the answer to any problem is simply to get more data, but in order to set up a succesful data business you need to first start by making the data more usable.

Here are some thoughts on what data usability means.

Human vs Machine usability

Is data usable by humans not usable for machines and viceversa? The answer is “No”. Machine-usable data is also human-usable data.

In recent years, machine learning made huge progress when dealing with unstructured data, progress that was spurred by the awareness that humans are exceptionally good at making assumptions about the implicit properties of a dataset. A model that wants to learn how to convert Celsius degrees and Fahrenheit in recipes will have to make assumptions about the relationship between the two numbers and understand that while 419F is a perfectly suitable temperature to bake a cake, 419C can almost melt zinc and is hard to reach in a domestic oven. By making data more usable for a machine, we also ensure it becomes more usable for humans.

Explicitness

Like in the example of Fahrenheit and Celsius degrees, units of measure — in general metadata — are key to the usability of a dataset…