Developing a data-driven business: Four scalable strategies for any SMB to turn data into problem-solving insights

While many small business owners might think that harnessing data science is out of their reach, and that analyzing data is supremely difficult unless they can justify hiring an expensive data analyst, anyone can become a citizen data scientist. It’s a bold claim, and one that many small business owners would question but, in today’s digital environment, it’s hugely achievable. The ability to take data and turn it into a problem-solving insight is no longer exclusively within the realms of those with years of experience or a specific university degree. 

With that democratization of the action of converting data into insights, we also see a correlation with the type of problem being solved. Without the need to justify the huge salary of a data scientist, business leaders can focus on small irritations, using existing data for insights, and leveraging existing staff to build incrementally from there. This, combined with the right tools and data, effectively opens up the benefits of data science for anyone who has a problem to solve. 

There is, however, still a significant perception disconnect where people simultaneously believe that data science is both unattainable and yet supremely valuable. There exist a range of supposed myths against the use of data analytics that desperately need to be dispelled. With this in mind, there are four key areas of any data project – areas that can easily scale up or down regardless of the size of the business or the size of the challenge:

Any business leader embarking on an analytics journey will undoubtedly have a problem in mind to solve. Just as the automatic telephone exchange was invented due to the irritation felt at misrouted calls, so too must your business begin the process of change by asking: “what irritates us most?”.

That problem itself may not have an immediate solution, but with the right data and analysis tools in place, it becomes far more achievable. A challenge can be something as simple as inputting emails into an analytic process to find which addresses are most likely to be spam and blocking those domains. In retail, it could be as simple as checking previous years’ sales data against seasonal trends and using that to inform staffing level guidance.

It’s important to consider the specific business outcome you want before working backwards to achieve it. By assessing the potential risk versus the benefit of the insight generated – being understaffed versus being sufficiently staffed, for example – we can begin to formulate a wider use case. 

As with any journey, the early stages of data analysis features a number of steps. The key here is to start small and work up to larger challenges. Developing the right processes, role responsibilities, and baseline standards – the core facets of data governance – is a core next step of scaling this process into something that delivers far more consistent business benefit.

A key early-stage challenge for any business looking to get started on their analytic journey is finding out what data and tools they already have. All businesses – in one way or another – have datasets that can be used for insights that can significantly impact business decisions. It’s likely that most businesses are already using some form of analytics, too… even if that is as simple as the VLOOKUP function in a spreadsheet.

In the early stages it’s wise to start small and build a bank of replicable successes.