Are you really ready to take that step?

Around 5 years ago, ‘Data Scientist’ became part of the public subconscious and everyone was talking about it. Dashboards and 3D visualisations were flying around left, right and centre. Then things, rightfully, calmed down. Companies started to ask whether they had enough quality data to really require data scientists.

Yet, one of the major myths from that time still remains: if you have data, a data scientist can turn it into something that will benefit your business. It is possible that you do have valuable data that needs to be transformed, but then again, you may be sitting on fool’s gold. What you really need to find out, is whether you actually need a data scientist or not.

If you know what data scientists do, and what they are for, that is certainly one part of the battle won. It is not unusual for companies to hire a data scientist without knowing what that data scientist will do. This can be down to job spec confusion or it can be down to FOMO or it can be down to leaderless data projects. There can be an over-reliance on data scientists coming in to solve all of the company’s data problems. This isn’t technically within their natural remit. If these things are happening, it does not necessarily mean you do not need a data scientists. It may mean that you need more structure, a data team or an experience data recruiter.

Going back to the point of data scientists being expected to solve all data problems, this also applies to them having to make a low amount of data count for something more. They also cannot make low quality automatically become high quality by refining it. This does not mean you should automatically give up on data projects, but more analysis over where the data is coming from, how relevant the data being collected is and how much work it needs to bring it up to scratch, make your data make more sense. Perhaps you require a consultant in these stages before bringing in the data scientist.

At this point you should really be asking yourself if you truly intend to commit to a data-driven future for your business. It is worth considering a first stage of assessment in order to ascertain whether you require data, possess the kind of data that can be useful and whether you will give the project the resources, in terms of time and money, that it requires. The C-Suite will need to be on-board with these developments or there will be no hope for the projects to get off the ground, strangled as they will be by lack of enthusiasm and resources. Someone high up within the business should be evangelical about data, seeing its worth.

Figuring out what you need the data to do is key. Perhaps you don’t need automated solutions but instead you need manual ones, at least at first. Defining it before the data scientist has been hired is a wise move as then they will not spend all of their time defining it, making the C-Suite wonder why they are bothering. It also means that, when you come to writing the job spec, you are aware of what problems there are to solve and who you need to solve them. So many firms send these out before anything is defined and it wastes time and money. It is even worse when generalist recruiters are tasked to find the data scientists, as they will not be able to interrogate the job spec and ask the right questions.

Instead of rushing into data scientist hiring, you have to ask yourself a few questions around who you want, what you want, why you want it and whether you really want it. Once you have figured this out, then comes the step of actually hiring the data scientist.

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