Data science has perhaps become something of a victim of its own success. As big data has proliferated, and more data is available, companies have enthusiastically bought into the idea that they can use analytics to get better insights from all this data. With data storage and cleaning becoming both easier and cheaper, and reliable self-service data visualisation tools readily available, it sounds like an obvious option.

But not everyone can spin data straw into gold insights. Those with the necessary skills, data scientists, have suddenly become hot property.

A challenging gap

The overall gap between supply and demand for data scientists has previously been estimated at well over 100,000, although recent estimates are a bit thin on the ground. There is, however, no doubt that data scientists command a premium in the job market, which implies that there is a shortage. There is also no question that more than 75% of companies have reported in several surveys that their major problem is recruitment of data scientists, because of a global shortage.

Overall figures, however, mask even more worrying local variations in supply. In the US, for example, starting salaries for data scientists are just over $100,000: very high, by programming standards. But in metropolitan areas such as New York, a data scientist can command up to $200,000, suggesting that there is an even bigger shortage in those areas. Fast growth markets like Turkey and the Middle East are also seeing even more acute challenges in attracting and retaining talent.

Identifying opportunities for all

One possible solution to this problem is to increase the number of women working in data science. This may be a surprising option, and some may even say it is unrealistic. This is especially true of those who are used to considering coding and programming as a very male-dominated environment, and are familiar with the fact that fewer women than ever before are now graduating in computer science.

Women, however, now make up 40% of statistics graduates, which is good news. Much of data science is about statistics. Increasingly, too, as data analytics and visualisation tools improve, the skills in demand for data science will not be coding, but interpreting and translating data into insights for business users. In other words, not computer skills, but statistical analysis and communication. These are much less male-dominated areas of expertise in both perception and reality.

The recent Women in Data Science conference at Stanford was a good example of this trend. Supported by a number of businesses including SAS, the conference also spawned satellite events around the world, with thousands following the live feed. It trended on Twitter all day. Despite the perceived male bias in data science, the conference had no problem attracting plenty of female speakers and delegates. It seems that the time is indeed more than ripe to bring more women into data science.

The idea of doing so is not just an opportunity for women, however. It is also an opportunity for businesses. In the most obvious way, it doubles the potential workforce of data scientists, which is very good news in a shortage sector. But it also, and more subtly, spreads the reach of data science to new sectors, particularly those often dominated by women. These sectors may have struggled to access data science in the past because of this, and include healthcare, education, and hospitality. All these sectors could benefit hugely from the arrival of data science principles and operations.

Expanding the workforce is not rocket science

As the Stanford conference showed, there are, in fact, many women already working in data science. Universities in the US and UK report unprecedented demand for data science courses, both masters and doctoral, suggesting that supply is making an effort to catch up with demand.

Embedding data science across all companies and sectors  that could benefit is not going to be straightforward. For every male-dominated tech company that needs to welcome women to enable it to address workforce shortages, there will be others that need to welcome data science.

Ultimately, it is up to organisations to ensure that they create an environment where diverse contributors can thrive. Teams and organisations need people with different skills and knowledge if they are to survive. Only by building diverse teams that welcome contributions from every possible source will organisations be able to tap into the full potential of their workforce now and in the future.