Research design

Bridging the Gap: Building Data Skills | Characteristic

The UK and the research sector are facing a shortage of high-level data skills at a time when the demand for such skills is increasing. How to bridge the digital skills gap? By Liam Kay.

We are living in a period of immense flows on the job market. People are changing jobs with a frequency rarely seen before, from July to September 2021 the total number of job moves increased to a record high of 979,000, mostly due to quits rather than layoffs. More than 47 million people in the United States also quit their jobs last year, according to US government statistics. The pandemic has caused disaster in many industries. Most importantly, artificial intelligence (AI) and data are transforming the world around us, with data skills becoming increasingly necessary for businesses.

Yet there is a data skills gap in the UK. Last year, a study by the Department for Digital, Culture, Media and Sport on quantifying the data skills gap found that there were 178,000 vacancies for specific specialist roles. data and those that require “hard” data skills, where the majority of the work is data-centric. and requires more advanced knowledge of the data. Nearly half (46%) of companies said they had difficulty recruiting for roles requiring data skills. The results are based on surveys of 1,045 companies, 5,000 workers and 1,000 students in higher education or in training.

Part of the problem is that the AI ​​itself is developing rapidly. Analyse of McKinsey estimates that AI could boost the UK economy by 22% by 2030, and government figures estimate that the UK is third in the world for private investment in AI companies in 2020, behind United States and China.

“AI is subject to the same change as IT was 30 years ago – it will, in my view, become a horizontal skill,” says Kian Katanforoosh, CEO of “An example is that 30 years ago not everyone knew how to code – it was a very specialized skill. Today it is difficult to find a mechanical engineer, an electrical engineer or an analyst who can’t code, because it’s become a horizontal skill.

The shortage of skilled labor increases competition. Ryan Howard, data science consultant and developer of an AI platform for qualitative research called “Big Qual”, says a big part of the problem is that other industries are paying more than the knowledge sector . “Our variety of work and real growth opportunities would make our industry an easy sell, if it weren’t so far off,” he says. “We pay 20% less than other industries, at all levels, for the same basic skill set. Additionally, we expect increased specialization in survey sampling and analysis, higher levels of business acumen and agility to tackle new problems every few days.

“Until this disparity is corrected, there is nothing to be done. We can talk all we want, settle down mentorships and training academies; people just don’t hang around long enough.

Katanforoosh says solutions to the skills gap must be business-specific, rather than looking for a silver bullet to address theskill which can be replicated in many organizations. “What we see in our engagements with businesses is that there is a one-size-fits-all solution for improvement it doesn’t work,” he said. “Employees don’t know where to direct their efforts, they have little time to learn and it’s essential that we guide them – they need mentorship delivered at scale. Companies need to adopt a “build” approach rather than a “buy” approach to skills.

He adds: “AI projects will not be able to develop without the required skills. You need people who understand how models derive, how data derives, how it will be maintained and monitored, and who makes sure it operates ethically and responsibly. If there are not enough people with these skills, it will cost businesses.

Develop skills
The trick is to create specialties in the organization, as well as development more broadly across the business, says Daniel Singer, managing director of analytics at Kantar UK and Ireland. “Companies should not try to improve skills every person in their ideas and marketing teams to make everyone a data scientist – having a broad base of expertise spread across the company is often not as effective as creating focused deep teams working together he says.

“Instead, companies should focus on identifying their goals, determining the talent they need to achieve them, and then hiring appropriately or partnering with a consulting firm that has those areas in mind. spirit. Once on board, it’s important for fellow analysts to work collaboratively with marketing and sales teams so that the use of data is deliberately integrated and supports a broader strategy, rather than staying in a silo.

Howard says the consequences for industry of not filling the data gap could be profound, especially for research methodology. “It’s not really about data and analytics pipelines – those skills can be replaced overnight, no problem,” he explains. “The danger is that the same people who deal with these things long enough naturally become our repository for research design, methods and quality control. These skills are almost impossible to replace, their absence leaves a void.

“We desperately need those who will and can check under the hood, and if we can’t recruit or train them, this story ends with us as glorified opinion. hawkers. Let’s be clear, no one is going to pay us for this.

Stian West LakeDirector General of the Royal Statistical Society (RSS), argues that companies need to have statistical and data science leadership in their organization. organizations whose RSS, BCSThe Chartered Institute for IT and the Alan Turing Institute have launched an Alliance for Data Science Professionals to help govern the data science industry and will seek to create a single, searchable public register of certified data professionals.

“You want data awareness and comfort in using and thinking about data so that it’s not just in a specialized function, but something that’s infused throughout the organization, especially in leadership roles, West Lake said. “It’s a mix of making sure people with those skills move into leadership roles and making sure people already in those leadership roles feel comfortable operating in a rich environment. data. The end state is where the organization lives and breathes data-driven decision making.

Singer adds that many companies recognize that data analytics can play a vital role in shaping their understanding of human behavior, but they often lack people who are proficient in different methods and can identify the right solution to a problem. . “Without this, there is a risk that insight teams will waste resources or miss their goal by using the wrong techniques for the job at hand,” he explains. “There is no one-size-fits-all ‘analytical solution’ that applies to all challenges.”