5 Reasons to Consider Training Above Hiring Data Scientists
Airbnb has started its own Data University. Tech giant Google aims to have all of its 25,000 engineers trained in Data Science and Machine Learning. These icons of Silicon Valley are setting a powerful example: Data Science know-how is becoming a core competency.
1# The Battle between Supply and Demand
It is no secret that there is a competitive market for data science professionals. Predictions for the total annual demand for Data Scientists are continuously being adjusted upwards, with no signs of slowing down. IBM currently predicts that the annual demand for Data Scientists, and Data Engineers will exceed 700,000 positions worldwide by 2020 [IBM].
In spite of increasing amounts of graduates from data science related fields, the gap between supply and demand is expected to widen. This results in companies having to pay substantial premiums for the Data Scientists they do succeed in hiring, or simply not having the analytical capabilities they so crave.
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2# Data Science graduates want more
Fortunately, hiring expert Data Scientists is not the only, or even the best strategy. Following in the footsteps of these Bay Area giants and training your current teams might be a better strategy in the long run. Why? Data Science graduates have their hearts set on jobs where they can spend the majority of their time doing Data Science projects and predictive modelling, which is not the reality in every company. When Data Science is not your core business it is safe to say: it will be hard to attract young and ambitious Data Scientists. Even if you succeed in hiring top-notch Data Scientists, it needs no explaining that discontented Data Scientists leaving your organisation with all their know-how is a bad situation to be in.
3# Your people know your business
Training existing teams also has the massive benefit of leveraging the product-specific knowledge that people in your company already possess. When an external Data Scientist enters, the first step is getting to know the nature of the process and its data. As can be expected, this is a very time-consuming step. By training your staff, the quality of this preprocessing step can be greatly improved, as well as greatly reduced in time.
4# Become more attractive as a company
Besides being cost-effective, offering training to existing and new employees can be used to attract and retain important talent. Many top profiles know that these skills are highly desirable now and in the future, and are actively looking for places where they can obtain and evolve these skills. Within this context, an organisation that is seen to be investing in its teams is immediately a substantially more attractive employer.
5# Get more out of external data science contractors
As always, the reality is somewhat more complex, and it is unreasonable to expect that anyone can become an expert Data Scientist in just a few days or weeks of training. Hence, it might be required to enrich teams with expert Data Scientists for advanced use cases - possibly from external contractors. In spite of this, training can make sure that your teams have the right skills to identify opportunities, do a lot of the heavy lifting by bringing structure to your data, and leveraging their product knowledge to improve the quality of projects. By working alongside hired experts these people also guarantee that the know-how created in the project is retained within the organisation, and does not leave alongside external consultants. These hybrid teams will result in an organisation that is both cost-effective and agile, two traits that are invaluable in today’s markets.
