Take Advantage of Hidden Data Science Talent in the Workplace

The demand for knowledge of artificial intelligence (AI), data science and business analytics is greater than ever, both at universities and in the business world. At every Dutch university, the  programs on these knowledge fields have set a maximum on student enrolment , and since 2016 the number of AI-related job openings has grown by 207 percent. Therefore companies that need personnel with these skills experience problems. And yet there is hope for these companies, since there is plenty of hidden talent in the workplace.

Robert Monné, Manager at The Analytics Academy

It is a good thing that the importance of AI and data science has gained widespread recognition. It has a positive impact on our society, and it offers competitive advantages for businesses. The research and consultancy bureau Gartner predicted last year that by 2021, around 30 percent of the growth in income for market-specific digital solutions will be due to AI technology. So there is good reason for companies to keep trying to recruit from the limited pool of data scientists. Universities are wrestling with enrolment caps, and Europe will have to make sail to close the AI knowledge gap with the rest of the world.

As a company that needs data science knowledge, are you therefore left to your own devices? Fortunately, no. When we train organizations in the latest data science skills, we always encounter people in-house who have untapped talent for the field. By teaching these talented individuals the right fundamentals by offering training courses, they can develop into a role that can make life easier (and therefore more effective) for the data science (or AI) specialist. This talent can be found among analysts, business translators, or even ‘citizen data scientists’: personnel without a data science background, but who can conduct useful analyses when given the right software and access to data. These analyses generate immediate value in the operations or on the “factory or shop floor”. Gartner expects that these ‘citizen data scientists’ will produce a larger quantity of analyses than the specialists themselves by as early as 2019.

By assigning employees to these related roles internally, an organization can build data analysis capabilities efficiently, while reducing its dependence on the tight data science job market. Another side benefit of this approach is that it gives current employees new opportunities for personal development, which results in higher employee satisfaction. So the question is: how can you retrain your personnel, considering the shortage of instructors at traditional training institutes? Fortunately, there are a wide range of alternatives, including: hands-on professional/executive studies, analysis tooling courses, Massive Open Online Courses (MOOCs), self-study, or internal training courses.

Organizations that aim to become more data-driven can find the ideal solution in a mix of these forms of training. In addition, combining theory and practice is crucial in order to apply the knowledge gained into useful skills. The best way to do that is through a broad internal program, where multiple layers of the organization (from analysts up to board members) can develop the necessary skills. Doing so embeds data-related knowledge in the organization, and boosts support for and understanding of new data-driven initiatives.

In the end, however, you will still need fully-qualified data scientists for the more complex (strategic) analyses. Together with the internally trained data science enthusiasts and employees, they can often find the most valuable data-related opportunities for the organization. In addition, the employees can complement the data scientist’s technical insight with their knowledge of the specific market in which they operate. But they can also take on the simpler analytical challenges on an ad-hoc basis, freeing the data scientist to concentrate on the more challenging projects. By working together, they can make the most efficient use of both their unique knowledge and skills.

Such an organization-wide approach to addressing the need for data-related knowledge also helps to rapidly build enthusiasm for data science throughout the organization. It makes the process actually fun for employees; they develop new skills, use data to justify their decisions, and gain more satisfaction from their work. It also frequently unleashes a flood of new business questions for the data scientists to apply their advanced analyses. And that’s what organizations really need in the end: finding data-driven solutions to all of their business challenges.

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Every Modern Employee Needs to Know the Basics of Data Science

Too many companies still see data science as a completely distinct area of expertise. That’s understandable when it comes to pilots, but if you want to increase the impact of data on your business, everyone in an organization needs to have a certain basic knowledge of data science. What’s more, this knowledge is essential if you aspire to be a modern, data-driven organization or employee.

Robert Monné, Manager at The Analytics Academy

I’ve always been fascinated by the value that data science can add to organizations. The benefits are substantial and are growing by the day. This is partly thanks to the fact that digital applications are becoming ever more intelligent as a result of new technological developments like machine learning and AI. If you want your company to capitalize on these opportunities, then it’s important to immerse yourself in data science. However, many organizations make the mistake of appointing only one or more data scientists, who are unleashed on the available business data without any coordination whatsoever with the business itself. Although I admire such enthusiasm, this approach is rather short sighted. You can’t achieve maximum impact by simply hiring expertise. That’s because everyone within an organization needs to have a certain basic knowledge of data science and should be involved with projects of this nature. That means everyone from management to the people in the workplace.

Lack of knowledge

There is relatively little knowledge of data science in the business world. At management level, people often don’t know about the possibilities it offers, and sometimes more importantly, the limits to what it can achieve. This results in an inability to assess the value of data science accurately and a lack of knowledge about what is needed to use it successfully. For instance, it certainly won’t be ‘mission accomplished’ if you simply hire a team of 5 data scientists and acquire a hip technological solution like for example a ‘data lake’. This is because deploying data science has a real impact on all facets of an organization, which means that a greater degree of change is required. In order to make a successful transformation to a data-driven organization, it makes sense – or rather, it is essential – for senior and middle management to have a basic understanding of matters such as statistics, algorithms and technology. This will ensure that they can participate in discussions with data science professionals at an equal level, to be a valuable stakeholder in data projects. This understanding also leads to decisions that are actually made through support of relevant analyses, and not on gut feeling..

Training and bootcamps

In recent years there has been growing demand for inhouse training on data science. This demand often originates from managers who have vision and are convinced that they need to ‘do something with data science’, while also being aware that they need to get the whole organization to participate. Training helps to put positive changes in motion at all levels of an organization. It helps to cultivate understanding, ensures that everyone knows enough to participate in discussions, and prepares people for what lies ahead. This training could for instance take the form of inspiration sessions for senior management, enabling high-level discussion of data science trends and methods, and of the changes needed to prepare the business to start reaping the benefits. But it’s also necessary to engage middle management.

They are the ‘power users’, who need a proper understanding of what data science is and how you can use it to take decisions in operational and customer processes. And you also need more in-depth, substantive training for the hands-on data professionals, the people who will ultimately be making the analyses and building the models to extract value from data. A broad-ranging, well-coordinated training program, reinforced by an internally created knowledge community, is essential for ensuring that the entire organization is on the same wavelength and speaks the same language when it comes to matters like data science, machine learning and AI. In this manner you can create support for data science and ultimately lay solid foundations for a successful transformation to a data-driven organization.

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