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Skills Enhancement Initiatives: Democratizing Data Analytics

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Companies that want to develop their analytical skills often overlook an essential resource: their knowledge workers. However, improving the skills of these professionals will be enough to significantly offset the shortage of data scientists.

A shortage of qualified personnel is a recurring problem for companies seeking to acquire and transfer skills that will allow them to remain competitive on an international level. As skill gaps close on one side, new ones emerge on the other.

For some time now, companies have faced a strong demand for talent in IT and in particular Data scientists. This trend is likely to continue for some time now. In fact, according to the “What Jobs Are in 2030?” From France’s strategy, it is estimated that by 2030, there will be 115,000 job opportunities More IT engineers, an increase of 26% compared to 2019, which shows the growing interest in these technology skills.

Today, this skills gap is getting wider. Companies around the world are in dire need of data professionals at all levels, not just highly specialized data scientists.

In most cases, they are looking for data scientists to lead their analytics initiatives, but there is one element missing. They can also generate more effective insights by improving the skills of existing employees (those without knowledge of data science) and giving them the ability to find solutions to their own problems. In this regard, access to low-code applications, no-code applications, and appropriate training is essential.

Take advantage of hidden expertise in services

Companies around the world are trying to narrow down their roster of employees with analytical skills, but they often overlook an important resource: their own. Knowledge workers. These professionals have unique skills: they possess the specialization needed to address issues within their specific business context. Their expertise remains essential for generating relevant ideas.

At the same time, valuing and capitalizing on their expertise encourages a culture of cohesion, as all employees discuss the issue of data analysis. It is important to consider knowledge workers from different departments as experts in their own right. They are the people who can detect and interpret changes at the highest level, while data scientists monitor global trends.

Turning information into insights requires both Behavioral skills, or “soft skills,” and specific expertise, which are assets that knowledge workers possess. Three behavioral skills are particularly important in moving from data-driven decision-making to a data-driven approach: collaboration, curiosity, and communication.

In general, data analysis should be seen as a team discipline, with employees from diverse backgrounds collaborating to bring different perspectives together. In the end, it is the diversity of opinions that produces the most relevant ideas. Data analytics is also about examination, exploration, and experimentation. It is an investigative task that requires experimentation, testing different ways of doing things and remaining open to new approaches to deepening one’s knowledge. Finally, effective listening and communication are key to finding solutions to these problems.

Research trends for leaders

Given the above, how can leaders increase their investment in employee training? First of all, it should be noted that data scientist experience is not necessary for most of the day-to-day analytical work within the Services. The important thing is to spread the training where the benefits will be most evident. So companies should carefully study which departments can use analytical skills. At the same time, they need to ensure that all training resources are available on a self-service basis, with detailed learning paths and references, with the ultimate goal of providing certification.

From acquiring basic data skills to data science, anything is possible. Not to mention that tools like self-service analytics platforms are a way for companies to encourage data literacy. So they have to think about the training and tools that will be most useful to support the employees on a day to day basis.

There are a number of benefits to having as many employees as possible, from different work areas, and the opportunity to work on data: Data can easily be used as a resource. When relying on data, other departments, just like data scientists, have all the capabilities to make decisions independently. Companies should not just wait for a shortage of qualified data scientists. By choosing to integrate analytics expertise into their workforce, they can bridge the gap in data analysis skills. It’s for sure.

Suggested panel by: Libby Duane Adams, Co-Founder and Chief Advocacy Officer at Alteryx

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