For many years, the concept of Big Data has been embraced by large companies. As the costs of data storage have continued to decline, small and medium sized companies are also taking advantage of the opportunity to store huge amounts of data. This information has great value for these companies – if they can harness it. That’s where data science comes in.

Most successful data science projects at large organizations are delivered by a well-organized data science team. The data science team’s role at many organizations extends well beyond making sense out of big data. Often business leaders look to data scientists for deeper insights and guidance based on their analysis. The data scientists may even be the drivers of innovation.

A good database developer at a small company may be able to use sophisticated tools to collect and analyze big data, distilling enormous datasets into useful summaries. However, to gain deeper insights, or automate processes with AI and machine learning, often requires someone with an advanced degree and knowledge of a set of mathematical modeling techniques that are not likely accessible to a typical developer.

Small and medium sized companies cannot necessarily afford a data science team and often may not really understand what to look for in a Data Scientist. A simple Google search can provide many useful tools to help these organizations find the right person for the job. However, one aspect that is easily overlooked is that not all Data Scientists approach problems from the same perspective.

Depending on a particular Data Scientist’s experience, the approach to a problem can be very different. For example, there are different schools of thought when it comes to the development of Machine Learning algorithms. The Logicians create models that tend to be the inverse of deduction and borrow ideas from philosophy and logic. The Biologists tend to view problem solving from a neuroscience or genetic perspective. Their models are inspired by the human brain’s connectivity and evolutionary biology. A third group are the Statisticians who see learning as probabilistic inference and mathematical optimization.

Unfortunately, there is not a ‘right’ school of thought. Depending on the problem at hand, one approach will develop an optimal solution. It is important to understand the background of the Data Scientists you are working with in order to know how they see the world and the potential for limits in their experience.

Rather than relying on the view of one Data Scientist, small and medium sized companies are beginning to partner with organizations that can provide insights from multiple perspectives. Just as sports teams are comprised of people with different skills and abilities, a good partner will pull from different schools of thought and different academic backgrounds to get the right mix of perspectives. Mathematicians, Computer Scientists, Biologists, and even Political Scientists all have very different backgrounds and will be familiar with various problem solving strategies.

Find a partner with an interdisciplinary team to help you take advantage of your data. This information has great value to your company - harvest as much value as possible.