IoT: Preparing our future workforce

Data scientists possess the key skills required to interpret key data. It’s simple - in the same way that it is impossible to drive a car without any fuel, a business cannot utilise its data without the ability to tap into its true value.

Data scientists are in high demand

According to Gartner, by 2020, more than half of major new business processes and systems will incorporate some element of IoT. Businesses will need to be equipped with the right skill sets to implement these projects successfully.

With the current shortage of data scientists, AI is one way that we can begin to automate a more mature and integrated IoT. We need to be offering higher-level training in technologies including AI and IoT to bridge the gap between the shortage of skills and the current need.

The benefit of AI is that it will help companies to scale their projects, while allowing staff to focus their time on jobs that technology can’t do. Meanwhile AI can focus on the mundane, repetitive tasks that employees have less time to do. AI then becomes an enabler for IoT projects.

Reports continue to tell us that insufficient staffing and a lack of expertise are the two things hampering the IoT market. Research from Immersat Research Programme found that 33 % of organisations would benefit from additional skills, whilst 47% believe that they lack the right skills entirely.

According to the report, the three main skills that organisations are lacking in are data security, data science and technical support. The solution isn’t to simply hire more data scientists. We need to understand the importance of other technologies such as AI and machine learning in enabling these IoT projects so that we can train the future workforce.

A more collaborative economy

The current view that the data scientist is the only person able to solve IoT problems is the wrong attitude. The solution is to ensure that employees within an organisation are able to understand their IoT data.

One way to do this is by training millennials. Used to being constantly connected, they are perfectly placed to drive further connectivity. You’ll hear this described as entering the sharing economy. We need to equip employees with the necessary skills in AI, ML and Deep Learning (DL). In doing so, businesses will be able to apply analytics to streaming data for deeper insights. This will enable more predictive decisions to be made and falls into sync with what a data scientist would be doing.

Therefore, we need to focus on implementing more training in tools that can help to automate and enhance roles by:

1. Implement more training - To bridge the current skills gap, we need to focus on offering more training courses in technologies that can act as an enabler, including AI, ML and DL. By allowing more employees to specialise in these skills, businesses will be able to benefit from greater analytics for more predictive decision-making. With more specific and targeted training courses, there will be greater opportunity to upskill the workforce.
2. STEM isn’t the only answer - This 20^th century thinking that STEM is the answer is not the only way to do it. We need to be focused on engineering new businesses and bringing new approaches to the market. A data scientist can be important in designing future business models. So, let’s offer more training in design thinking. It’s not just about the scientific skills a data scientist pursues. The skills to enable a company’s strategy are equally as important.
3. There is no one, single required skill - As the workforce of the future prepare to work in a more connected workplace, there is no single required skill. Ultimately, IoT and digital transformation are linked. Therefore, whilst a data scientist used to be the secret ingredient to create a successful IoT strategy, it’s not the only ingredient needed anymore. The data scientist is replaceable if we focus on building a workforce that possess skills in AI, DL and ML. If we can keep pace with these new technologies, we can ensure we are equipped with the necessary skills to automate our projects more widely.

The workforce of the future must be able to work with AI, DL, ML and data analytics technologies. Only then, will we be able to unlock the true value of our data to drive our IoT projects forward. This is why we need to act now.