Data scientist sourcing: in or out?

We have mentioned it before, and we will probably have to repeat it for quite a while: there is a severe shortage of data scientists, a shortage which will probably last for several years.

Faced with this problem, many organizations decide to partially or fully outsource their data science activities. Very often this means that they turn to Asian outsourcing firms for their data science needs. Especially in India, many more data scientists graduate every year than anywhere else in the world. Outsourcing your data science projects fully or partially to one of these countries does seem to make a lot of sense, as a means to improve your business based on relevant insights.

But outsourcing is not without risks, which you should consider carefully. First of all, analytics pertain to a company’s front-end strategy, whereas traditional ICT services are usually related to the back-end. Second, and more importantly, outsourcing data does entail a lot of risks around security and intellectual property. Data is often the richest resource a company has, and the only way it can differentiate from competitors. So what happens if your data is outsourced for analysis to an organization that also processes the data of your biggest competitors? How can you make sure your data or insights will not be compromised? This takes a lot of guarantees and waterproof contracts, but what if you end up in an international lawsuit?

These are just a few of the risks involved with outsourcing analytical and data science projects. If you want to read more considerations to take into account before embarking on an outsourcing adventure, you can turn to my blog on datamining apps. It might make you a bit more cautious about the extent and duration when outsourcing data science projects.