Typical days in data science: data preparation

Reading time: 2 min

It is cool to be a data scientist.

It is not clear from a short introduction that this attractive looking job is actually donkey work for the most part because of data preparation.

As one survey – published in Forbes – shows, 76% of data scientists enjoy these kind of tasks the least, however they spend around 80% of their time with them. It’s interesting that this rate was similar 10 years ago when we wrote about this in our book released back then.

While spending that much time with data cleansing we create special, unique expressions to describe it: data digging, massaging, play-doh-ing, plucking… Still I know only a handful of people who left their professions, got bored or started to hate working with data because of this. We love cooking so we do the vegetable pealing as well. Besides, the quality of data cleansing does matter a lot. The final result often depends on this phase – the withered parts have to be cut out while the delicious bites are being processed.

Meanwhile the technology that supports data analysis is developing in a dizzying pace. There are a lot of developments that target data cleansing to make it less time consuming to give data experts more time to spend with the actual analysis. Venture capital flows to startups who concentrate on Big Data interpretation, but naturally the giants of the data industry also work on their own solutions.

So can we hope that the 80% data cleansing – 20% analysis ratio of working time is going to pass away? I doubt it and I do not expect any significant changes within the next years.

Tools that support data cleansing are going to get better and better. However this will result in involving data sources that we would not even think about using today. Faster road vehicles did not only result in spending less time with travel, but ended up allowing us to reach farther destinations as well.

There is one more area that has the promise of doing the data cleansing phase: there are initiatives to use artificial intelligence (AI) for data interpretation. Of course, AI sets foot in more and more fields, for example within a few years there will be less need for drivers.

There will always be chefs even when machines help them with vegetable pealing. And there will be data analysts as well, with more and more useful tools to support their work.

Hiflylabs creates business value from data. The core of the team has been working together for 15 years, currently with more than 50 passionate employees.

The picture is owned by Cathy Scola. (www.flickr.com)