Source – https://www.computerweekly.com/
In today’s business environment, data is key to success. With over 2.5 quintillion bytes of data created each day, data-driven insights are the main driver in every major business decision and are essential to discovering more efficient processes, reduction in risk or new sources of revenue.
However, harnessing the power of data continues to be a challenge, due to the on-going shortage of data science skills in the labour market, as demand for digital skills still far outstrips the supply. A recent UK government report found that nearly half of businesses (46%) have struggled to recruit for roles requiring hard data and analytics skills.
IDC estimates that by 2025 we’ll have created more than 175 zettabytes globally. As the world of business continues evolving, companies are moving fast and need fast solutions – they can no longer tolerate knowledge workers, delivering low strategic output from legacy tools for the enterprise. The sheer abundance of data and its growing complexity means data skilled workers able to harness it for fast and sound decisions will be at the forefront of the job market throughout the next decade.
While not every worker needs to become a data scientist, many businesses are turning to upskilling their employees to overcome this shortage. Building their own internal pool of talented data workers with the skills, desire, knowledge, and analytical expertise to be successful and thrive in an increasingly ‘data-rich’ environment.
Organisations have already started to recognize data literacy as an important skill for their workforce. A recent McKinsey study found that 84% of executive leaders – when increasing their talent pool of data specialists – experienced more success from upskilling their existing workforce, compared to just 16% who succeeded when hiring externally. By providing analytics solutions that upskill information workers into data-literate knowledge workers, these knowledge workers – individually and collectively – can help drive organisational transformation. Employees have the context of the business questions to solve as well as the knowledge of the data assets available that can drive answers through analytics.
Creating a culture of upskilling is by no means an easy feat. Getting employees engaged can be half the battle. It requires building a new culture where data is accessible to workers throughout the organisation, as well as significant investment in new tools and platforms that do not require users to know complex coding languages. Low code and no code solutions provide space for employees who want to upskill, learn and practice to become skilled data workers themselves.
By implementing formal upskilling programmes that focus on key skills and technologies, in addition to providing a learning curriculum that can result in valuable and credible certifications, companies can set themselves and their employees up for success. However, these programmes should not be dry and academic. In fact, the upskilling journey can be a social experience.
For instance, businesses can host “lunch and learn” activities and company-wide “data challenges” that bring people together from across the organisation, introduce staff to data science and make it appealing and accessible. Gamification strategies can also encourage staff to use online learning resources and develop their data skills by using leader boards, points scoring and creating personal challenges and achievements.
The aim is to create an open culture of learning where staff communicate and work together to solve data problems. A company’s existing data scientists should act as coaches to colleagues, encouraging them to think analytically and ask the right questions of datasets. This will help build data skills into every team, so that data analytics becomes an enterprise-wide initiative, rather than siloed into one team of analytics professionals.
The other benefit of this more social approach to data science is how it can impact diversity. Simply put, data science has a diversity problem: as few as 15% of data scientists are women. This lack of diversity is a huge concern, because with a diverse range of approaches and points of view to tackle data challenges and ensure data models and algorithms are free from biases, businesses will see improvement in results. It’s no secret that the more diverse the workforce the richer the business outcomes will be, research by McKinsey has shown that organisations with more ethnic and gender diversity are more likely to outperform. When we value our varied experiences, they impact how we solve problems to get to better answers.
The evolving landscape of the data science and analytics market creates an inherent need for organisations to foster and grow data analytics cultures fuelled by collaboration and diversity, presenting an opportunity for all demographics traditionally underrepresented in the technology workforce, to accelerate their careers by embracing analytic roles. For business leaders, this represents an opportunity to look within for specialists with the right attitude to problem solving, not just technical aptitude, to support and upskill in both data literacy and analytics.
By investing in upskilling, people from any age, gender and background can learn vital data skills and progress their careers. It also enables companies to recruit new individuals who don’t necessarily have an academic background or specific coding skills, which may encourage a more diverse range of applicants. This was the experience of the sports and fitness apparel company Gymshark, which uses Alteryx to empower and upskill its employees.
“We’ve been able to expand faster because we are able to find these individuals easier, rather than having to find people with very specific skillsets,” says Gemma Hulbert, CDO at Gymshark. “New hires are now able to come in and hit the ground running right away with Alteryx, even though they aren’t data analysts. We are able to create apps that empower our employees to be able to learn new skills using the platform.”
Data science doesn’t have to be the preserve of the elite few. Anyone in the workforce with a passion for solving data puzzles is now able to do it, not just a handful of specialists. In the past, employees with vast expertise in their own fields were locked out of data analytics due to the technical knowledge it required.
With the right tools and investment, anyone can learn data skills, and when people are encouraged to be creative and think critically, they are able to ask the right questions and solve all sorts of problems. Thanks to self-service platforms and automation, the power of analytics is no longer restricted to a few gatekeepers, but rather it is available to all. By enabling employees to scale their passion for data science, businesses will accelerate the knowledge workers’ journey to become data-driven, be better able to unlock data-driven insights and tackle the world’s biggest problems with a successful digital transformation journey.