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:
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1. Identify the right problems to solve
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.
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2. Assess what data and tools you have, and how you want to use them
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. Some believe that in order to pull useful insights, you need to have huge amounts of data to work on; however, many data projects use relatively small datasets to deliver disproportionate value. The key consideration is the quality of the data – not the quantity. With a small high-quality dataset in place, even data from legacy systems is often sufficient to get started. As the insights needed from data becomes more complex, more processes and more user-friendly tools can be layered on when a specific need arises.
It’s important to remember that, in the early stages of a data journey, these initial goals that have been set prior to embarking may be superseded by smaller, equally as important, challenges. This is normal.
3. Setting up for, and building on, success
In the early stages of any project - whether you are a business owner or report into someone senior – demonstrating small, easily replicable, and scalable successes is the key to being able to continue driving benefit. By positioning these successes as both financially viable and easily repeatable, we can begin to generate the political capital and buy-in needed to expand a data project further.
With early-stage proof of concept work successfully completed, more complicated projects can be tackled. Again, beginning with a simple question, businesses can begin to explore higher-value questions: who the most valuable customers are, and when the best time is to introduce new products, for example.
The true value of successful analytics work, however, comes from automating these insights – freeing up the project leader to focus on newer and higher-value projects in tandem. The actual adopters of these insights know the business process they relate to and therefore including them is essential to the design and rollout of such projects. The ability to integrate new data points into an analytic process to deliver real-time insights – and the ability to rapidly adapt to changing market demands - is what separates digital-native businesses such as Netflix and Amazon from more legacy organizations buried in technical debt.
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4. Implement, expand, and replicate
Once this preparation process has been completed, and processes put in place to ensure the right – clean – data is collected, businesses can begin to democratize the access to that data and begin transforming it into business insight. Training those closest to the problem to provide quick answers to questions is one of the most significant benefits of implementing a data-driven strategy. It’s also a benefit that is highly achievable.
The new question is now not whether your business is ready for analytics, but whether you have equipped your employees to help your business thrive with analytics. With a firm foundation based on assessment, preparation and identification, we can begin to expand the remit of the work that is done with data. While the requirement for quality data above all else is vital, more quality data can facilitate a deeper understanding of the issues facing your business and provide the direction to sidestep them.
This is what we mean by the development of a data culture – assessing, implementing, expanding and replicating the creation and facilitation of core data insights. By preparing your resources, tools, and staff for the task ahead, and by amplifying that human intelligence so the person closest to the problem can solve complex challenges, you’re paving the way to an efficient and pain-free analysis process.
While the road to a fully realized data and analytics strategy is a long one with numerous potholes, exits and turns, the end goal is a far more effective and replicable way to make effective and valuable business decisions. Even the most simple irritations – once fixed – can deliver a disproportionate benefit.
David Sweenor, Senior Director of Product Marketing, Alteryx