PureSpectrum - Schedule A Demo
Our new GreenBook Directory site is live!
COVID-19 guidance, tips, analysis - access full coverage here

Silos: Good for Grain, Bad for Market Research

Silos in market research detract from making actionable, big picture recommendations. This can be overcome by combining different parts of the data puzzle from the beginning to make them work together.

Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Keri Vermaak will be speaking at IIeX North America (June 12-14 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.

Silos, you might say, run in my blood. In fact, back in the day, my grandfather built concrete slab silos in South Dakota for their original use – storing grain. Believed to be around since ancient Greece, the term “silo” has evolved from being a purely agricultural term, to a metaphor used today to describe the isolation of functions or systems in an organization, particularly when it comes to technology.

In market research circles, we hear a lot of negative commentary about project functions operating in such silos – and while silos are good for grain storage, they are not good for market research. After all, our goal as researchers is to be able to show others a joined-up “big picture” of insights that will help them make data-driven decisions that deliver better business outcomes. But how do we help put the data puzzle together when the pieces are so often stored separately?

Remember: we’re not grain!

As an industry, we know we must overcome the inefficient compartmentalization of the steps that make up the research process: collecting data, organizing data, analyzing data, creating visualizations and presenting them. When these steps are done in isolation from one another, we often end up with orphaned data that we have to try to piece back together so we can deliver a coherent “big picture”. We can manage it through largely manual processes, but it’s clumsy and slow and often results in mistakes or, worse, key insights getting lost in translation. These manual processes break links at every step, putting quality and useable outcomes at risk.

When you look specifically at the market research industry, a common disconnect is found between the market researchers (qualitative focus on bias, attribution, emotion, sociology) and data scientists (background in computer science, numbers and modeling). Going back to the beginning and combining different forms of data from the outset is one way to start breaking down silos. This combination should carry through to the output, creating data stories and insights in an uninterrupted, cohesive process across different data sources.

Three tips to help overcome silos in our space

Here’s what I’ve seen work really well when it comes to delivering a coherent “big picture”:

  • Bring all the pieces together at the outset and combine different data sets to make them work together
  • Utilize single-platform technology that is able to bring these pieces together without damaging outcomes or original data sources
  • Make the insights accessible – layer and share that data with the right people, in a way that can deliver new insights and impact business decisions

The end goal of course is to get high quality data stories into the hands of the people who can use them to create value for their brands and markets. I’m looking forward to sharing more on this topic at IIeX North America next month with my presentation No More Mutant Toys: Playing nice with market research outcomes.


Please share...

One response to “Silos: Good for Grain, Bad for Market Research

Join the conversation