The art and science of data analytics

March 25, 2019  /  



CMO, Alex Allen shares his thoughts on creativity as a competitive advantage

By Alex Allen

Many companies often view data science as a purely mathematical process. A common misconception is that data science is simply a team putting information into a program and then producing a tidy report. Approaching data analytics with such a limited view is a continual miss for marketing teams across the country. The truth is that meaningful, effective data analysis requires a human element, specifically imagination.

Try and try again

I frequently see companies getting stuck reviewing data from the past and never creating actionable solutions, never working in real time. Reviewing old data may give you interesting insights, but the goal should be to create real-time workflows parallel with insightful analysis.

There are endless ways to interpret data, and producing actionable results requires a vast amount of creativity. To go beyond the obvious answers requires testing, and many variables you test won’t provide lift.

Embedding creativity into feature engineering is one of the most difficult but important exercises in data science. The decisions about what to include and what to omit in your data sets are crucial. Your team’s ability to approach large data sets with a critical eye is invaluable.

Output depends on input

Creativity isn’t limited to building new things from scratch or coming up with totally new programs to do something specific for your business, although sometimes that’s necessary too.

When our team sets out to enhance the accuracy of our forecasts and predictions, we ask ourselves whether we should build what it takes to do so or identify existing technology that we can repurpose. Our first approach is to exhaust all of our buying options; why reinvent something that has the ability (with a little re-engineering) to provide you with what you need? You may not find the exact solution you had pictured, but if you can find progressive and collaborative vendors who are willing to let you help them reimagine their products to better serve their customers, you can save a ton of money and headaches in the future.

The process of finding the right partner taught us that we were on the path toward being successful in our industry and continuing to differentiate ourselves from our competitors.  Anytime you identify a need without an existing solution, you’re onto something significant.

Our number one goal as a business is to maximize the likelihood that potential clients are an ideal fit for the products we offer. By repurposing existing technologies to meet our needs and combining them with software we’ve built ourselves, we’ve been able to dial into some profound insights on how to understand and deliver on our client’s core needs.

Creativity in leadership

The best way to foster creative thinking in your data science team is to hire leaders who can think beyond the mathematics of analytics and communicate the meaning behind every project. Your leadership has to have a strong grasp on the why behind all the data they’re responsible for modeling to determine whether the outcomes you’re predicting matter. Ultimately, you want to ensure that your analysis isn’t simply theoretical, and is centered on events on which you can take prescriptive measures.

When everyone else has the same access to analysis tools and algorithms, a great way to differentiate your business is to focus on what data you feed those tools and algorithms. That’s where creative thinking comes in. Gathering a ton of data for the sake of it is not going to do much for your business—it has to be more than cool talking points. You have to know how to use that data to produce something meaningful to your business—or else you’re wasting your time.

© 2022 Spring Venture Group / Privacy Policy