How a small company can make use of data

Against this backdrop, the way in which small businesses use their data and, by extension, use Artificial Intelligence (AI) has become a hotly discussed business issue. But can a small business ever truly unlock the power of its data? Given obvious resourcing constraints is it a realistic aspiration for a small business or should they focus their attention on other things until they can use their data effectively and with better resources?

The power of data

According to a recent report by Microsoft UK, organisations investing in developing their ethical use of AI are already outperforming those that are not by 9%. Clearly, the effective use of AI and data science can be game-changing, but it is not only small businesses that are struggling to deploy data science. The same report reveals that 51% of UK leaders admit their organisation does not currently have an AI strategy in place. Incorporating data science into an operating model is as big a challenge as it is an opportunity, but what are the issues specific to smaller businesses and are they so very different to larger companies?

Data can be tricky to make sense of and, though start-ups or small organisations may not be sitting on the same volume of data as their larger counterparts, the variety and velocity will often be comparable. Precisely because small companies are often competing with larger, better-resourced competitors, it’s often absolutely critical that they can quickly make use of their data. The good news is that smaller organisations are in some ways better able to do this than larger, better-established ones. Primarily because they are more agile and have a mindset and a culture that lends itself far better to do things differently, they can iterate on ideas and models, learn from mistakes and quickly put working models to work in order to derive better business value. Secondly, start-ups and scale-ups have almost always been built with data in mind from day one. They will not have to grapple with clunky and outdated legacy technology systems and even more outdated ways of working and thinking.

The challenges of hiring

Many of the challenges I see in the deployment of data science in organisations of all types and sizes lies with the recruitment and retention of data scientists. These are the people with the skill set that can make sense of dynamic and complex data sets and right now they are a rare and in-demand breed. This is historically where big business has had a massive advantage, for they have been able to hire data science professionals and, by doing so, start to put data front and are of their organisation. Hiring teams of data scientists is not without its challenges, however. They are highly skilled and, therefore, expensive to hire. Crucially, it can be particularly difficult for those without a data science background to know if they are recruiting those with the right set of competencies and experience.

Data engineer: The ‘real’ sexiest job of the 21st century

As the hype around data scientists fails to meet expectations, perhaps it’s time data engineer took the title as the “sexiest job of the 21st century”

The truth is, “data science” as a term really covers several different roles (e.g. data engineer, machine-learning specialist, visualisation expert, data analyst…) and as teams grow within companies, data scientists often start to specialise in areas in which they are most interested and adept.

Companies need to recognise that data science is a team sport. Data science and AI projects need to be staffed with individuals with a range of different skill sets. This, naturally, requires a bigger upfront investment but it delivers results much more quickly and therefore has a better return on investment. It also ensures against failure, as a team is much stronger and resilient than an individual. Sadly, it will also be completely out of the question for small businesses.

Savvy Hiring

The solution is to outsource your data science challenges to teams who can best solve them in the quickest time frame. By outsourcing a team for a specific problem, it removes the various headaches associated with recruitment and enables you to look only at the issue to be solved in a strategic way.

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Trying to recruit a data scientist or team by using traditional recruitment methods can cause frustration on both sides. If you outsource on a project-by-project basis you can make the personnel relevant to the project you are looking to manage, rather than trying to make a project match the talents of your existing team members. Using bespoke, outsourced teams of highly skilled data scientists is becoming the most efficient and effective way forwards. In many ways it has also democratised access to data scientists, cutting the costs of access and enabling small businesses to benefit from expertise as needed.

Small businesses need not feel they are mere spectators as the data science revolution happens around them. There has never been a better time to get engaged and start making your data work hard for your business.

Written by Kim Nilsson is founder and CEO of data science hub Pivigo.

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