The number of students applying for study programs in Artificial Intelligence (AI), Data Science and Business Analytics is going through the roof. Some educational institutes have even received twice as many applications compared to last year. As a result, universities have instituted a limit on registrations, because they cannot deal with these huge numbers of new students. But at the same time, business demand for these data talents is increasing: IBM and Burning Glass Technologies expects the number to grow by 15% in the US by 2020. We have noticed the same trend from conversations with our clients and by taking a quick look at the number of job openings in the field of data science. Many companies are finding it virtually impossible to recruit data scientists externally, due to the limited supply and the resulting rising salaries. And the talent that is on the market is often stolen away by the many startups and born digital companies. Data science offers many lasting competitive advantages: A study conducted by the Harvard Business Review in 2012 shows that data-driven organizations are 5% more productive and 6% more profitable than their competition. Since then, technology has progressed substantially, so the impact today will be even greater. Waiting for the data science hype to die down is, evidently, not an option.
For many companies, the solution can be found much closer to home than would be expected. We are convinced that there is in fact plenty of talent walking around within companies. Talent with the potential to be re-trained as a data scientist or a related role (such as business/analytics translator, analyst or citizen data scientist). There are people that have a natural talent for quantitative challenges, but due to a variety of reasons haven’t yet had an opportunity to utilize that talent. In order to take advantage of these internal opportunities, there are a wide variety of options for training the current workforce in the latest technologies and quantitative skills. These options include university programs (MBAs, elective courses), executive or professional training courses, software/tool-oriented training courses, online courses (MOOCs), in-house training, self-study, or on-the-job training. But which one do you choose? We believe in a mix of these forms of education framed in a broad program applicable to multiple layers in the organization (from C-level to analysts). That way, we ensure that the skills learned by an individual are fully embedded within the organization.
In addition to avoiding the challenges of external recruitment, training your current workforce offers the following benefits:
- In order for data scientists to conduct effective analyses, knowledge of the domain or market is a crucial skill to have. Their knowledge of the domain allows the organization to quickly recognize valuable opportunities related to data.
- The broad approach ensures that the ‘simpler’ analytics challenges can be solved on an ad-hoc basis by citizen data scientists (business managers and staff conducting the analyses on their own). This frees the expert/senior data scientist to focus on the more challenging and value-adding projects.
- The organization-wide approach ensures that the data-driven enthusiasm spreads more quickly throughout the organization. This creates a better pipeline of business questions for the data scientists to get started with.
- In addition to the benefits for the organization, there are also aspects that are advantageous to the individual as well. Developing skills gives employees more satisfaction in their work. We have even heard of examples of employees who have become so enthusiastic about data science, that they have devoted their weekends to experimenting with tools such as Python at home.
If you think that there are opportunities within your organization to re-train employees as data scientists, in order to optimally benefit from the opportunities presented by data science, then read about the experiences of other companies who have gone before you.