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We have decided to compile a list of some of the pitfalls companies encounter when they are hiring Data Experts in order to help you avoid these problems when you are hiring.

1. HR Managers who lack the skills to evaluate data experts.

Problem-solving and the ability to communicate are integral to working well within a team yet a HR Manager unfamiliar with in-depth data expert hiring practices will usually only focus on tech skills. Neglecting to examine, in fine detail, the candidates’ soft skills will result in the incorrect prospect being hired and will cause more headaches further down the line.

A lack of clarity when working out what is actually required of the hire is the main failing behind this challenge and the company should really consider whether their HR department has the required skills to fulfil the role when it comes to data science.

2. Unspecific job adverts

Companies are now coming to understand the role data plays within their business and the need to extract value from it.

The challenge comes in understanding what staff are needed to mine value in this gold rush. Specificity is the watch word here as some companies end up flailing around without a clear understanding of the skills they require for their data experts. This will alienate the most qualified prospective employees.

A reason behind this is not just a lack of understanding but because perhaps things have gotten a little too vague within data science which requires more specialism within it. The over-eagerness to hire and get started can hobble a data operation so companies should define what they need firstly, perhaps with outside advice, before recruiting.

3. Lacking a suitable technology infrastructure

Data is the future and a company needs to have the capability to succeed yet if the infrastructure is out-of-date this will be a turn off for any prospective data experts to join your team. Why bother when the environment is not conducive to achieving a ‘win’?

A recent NACE report found that, when it came to security and data analytics experts who were recruited, just under 40 percent of those who responded admitted that the business they worked for lacked a structure necessary for mine real value from their data.

If you take data seriously, you must assess your infrastructure with a clear mind and plot a course to correct this oversight before it becomes detrimental to your business. Data experts who are rightly in demand will not hang around and wait for a company to slowly make changes when time is of the essence.

4. Fighting for hiring resources

In order to attract the best data experts a company must be willing to offer a competitive salary. It all comes down to the appropriate resources being granted by higher-ups. If the bosses do not understand the inherent value of these professionals, this becomes detrimental.

Data experts are not cheap and will naturally gravitate to businesses where they feel they, and the art of data, are respected. If it is your responsibility to hire data experts, you must impress upon your managers the need for a long-term data strategy which will benefit the company in the long run.

Incentives are important and if your business refuses to pay the salaries expected within the profession and offer Employee Stock Ownership Plans, then it will be incredibly difficult to attract the talent you need to make a difference.

5. How the candidate will fit into the company

Apart from the major mistake companies make, which is a misguided belief that a data scientist must hold a PhD in order to be of any value to them, one of the most common errors is not understanding just how a data expert will slot into a team.

Hiring for these jobs takes work and understanding and there is no ‘one size fits all’ with this field. Those of an academic bent may not be best suited for corporate companies whereas someone with a more varied skillset may be perfect. Data science hiring requires a specific skillset too.

6. Hard data problems are only one aspect of data science

It must be clarified what the job isn’t, as much as what the job is. Clarification is important to find the right team for the correct stage of the data process. Often, companies put the cart before the horse.

A company may hire someone within machine learning before the data pipeline is even operational. If a company does not require a hard data specialist, they are throwing away time and resources in a misguided attempt to get to grips with an issue that isn’t really there, meanwhile the true problem festers and the business suffers.

At all points, the needs of the company must remain in focus and the correct type of candidate selected for the role. Anything else is simply counterproductive.

7. Some companies do not care about or prioritise data

While a company is interviewing a data expert, the data expert is interviewing the company. They are looking for signs that the business actually takes data seriously, that is funds it well and that it treats data scientists with respect.

If they work in analytics and will be reporting through finance, for example, that will set their mind at ease. If they have a lot of layers of management above them, that may make them reconsider as bureaucracy can hinder, especially if some or all of the management don’t really believe that data is valuable. They will look at the budget allocated, whether they have freedom to buy the tools they need or hire the support they need.

Erasing misconceptions on both sides and honesty are the real reason behind us making this point. We understand that time and resources are precious and we also understand the importance of data being treated with respect in order to become a useful tool within your business. Sometimes these issues can be tackled in-house and sometimes it requires an outside agency who can view it objectively with an expert eye.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.