Data science skills-gap in 2020 due to lack of funding, time & support

, Data science skills-gap in 2020 due to lack of funding, time & support

Research, released at the annual Women In Data UK conference from data analytics consultancy, Mango Solutions, Women in Data and Datatech Analytics, demonstrates a sector in crisis moving into 2020. As data becomes the currency of the modern business, the race to become data-driven has seen organisations investing heavily in core analytics skills – however, a lack of support, funding and time available for upskilling are all cited as challenges within the UK data science community. Indications are that vital steps need to be taken to assess skills gaps and plan to unite individuals to create effective, skilled teams that can rise to the growing data challenge for businesses.

Lack of support and the right tools

The research from this first skills survey, conducted amongst data science professionals in the UK, reveals that over half of respondents plan to move roles within the next year. When asked what was the greatest challenge faced in delivering value, over a quarter (29%) of respondents cited lack of support from managers and leaders, along with nearly half (44%) saying that bureaucracy is the greatest challenge they face in their roles. A lack of access to the right tools was also cited as a frustration, with over a quarter (28%) reporting that this was an issue.

According to the survey results, the average tenure in role across all respondents is two and a half years. However, with 56% of respondents indicating an intention to seek new roles within the next 12 months, it’s likely that this churn rate is on an upward trajectory.

Over 50% of respondents identifying as practitioners reported that they have no internal data science community within which to share an active role. Respondents to the survey who identified as managers stated that operating in siloed teams is the greatest challenge (51%) they have when it comes to delivering value within their organisation.

At the moment no formal accreditation for data science roles in the UK is available. Currently there are many points of entry into the profession, which makes such accreditation difficult. Indeed, when asked, over half of respondents indicated that they do not see the need for accreditation. However, the lack of a standardised set of criteria to form a framework and description for data science roles, including learning and development for skills advancement has the potential to make recruiting for these roles challenging.

Machine learning – top area for upskilling in 2020

Almost half of data scientists who identified as fulfilling a leadership role said that skills shortages are posing the greatest challenge to delivering value within their organisation, with four out of five (86%) of managers reporting that it is difficult to hire talent in the sector.

When asked how they were planning to plug this skills gap, upskilling was the number one strategy being deployed, with over two thirds (69%) of the managers revealing that this is how they plan to address the shortage within their organisations. However, when data scientists were asked what prevents them from learning a new skill, time was cited as the key barrier by 70% of respondents. Additionally, not knowing where to start (32%), and funding (25%), were cited as issues preventing upskilling in the next year.

Of those data scientists who plan to upskill in 2020, the three most popular topics for future development are:
– Machine Learning (57%)
– Big Data analytical technologies (e.g. Spark, Storm, Flink) (49%)
– Big Data technologies (e.g. Hadoop, Mongo DB, others) (44%)

From a managerial perspective, the data science skills most scarce across their organisations are:
– Visualising (43%)
– Programming (35%)
– Technology (35%)
– Communication (34%)

Rich Pugh, Chief Data Scientist and co-founder at Mango said:

“Due to the dynamic and growing nature of data science, creating a data science team with the optimum blend of analytic and “soft” business skills is costly and complex. There is a scarcity of resources and a lack of common understanding around existing analytic skillsets and job descriptions.

“As more organisations embrace data-driven transformation, there has never been a more urgent need to upskill and resource data science teams across a wide range of sectors and departments. Data science should be considered as a team sport, with the combined skills of each member contributing to success. If organisations can’t hire people with all the skills required, I would urge them to look at what skills are in existence internally and create a team of people with complementary skillsets. That way, as a collective team, firms can create a solid foundation for driving data-driven transformation.”

Roisin McCarthy, co-founder of WiD, said:

“We are asking our members, and the wider business community, to help us to demystify perceptions around data science as a way to address the skills gap and appeal to a wider ranging section of professionals. Data-driven organisations have a massive opportunity to attract and recruit the right talent, growing a data science community that is thriving, challenging and lucrative.”