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.