Our 3 C’s of Data Veracity

What do you think of when you hear the term data veracity? Do you think of the accuracy, relevance, or the quality of data? In reality, data veracity encompasses all of these aspects. As a result, we frequently talk about data veracity—after all, it’s in our nature as researchers to obsess over it.

But why we obsess over it so much more at GutCheck is due to the immense value it can provide. In fact, when we apply our 3C approach it acts as a framework to establish data veracity beyond what most would think is necessary.

The 3 C’s: Complete, Clear-Cut, Connectable

Our 3C framework is more than just our clever way of showcasing data veracity; it’s an analytical framework that actually establishes research-grade data. It uses a set of necessary conditions not only to uphold data veracity, but also to incorporate important components for successful and efficient use of data.

The first C of this framework is complete data. Data is complete when you have identified and filled any gaps in your understanding. In order to achieve complete data

• You must acknowledge the strengths and weaknesses of data. Just like when comparing qualitative and quantitative methods, different types of data have their strong suits and drawbacks.
• It has to go through an agnostic yet pragmatic evaluation. Think logically about the purpose of the data and whether or not it makes sense in the context of your needs.
• Not only leverage multiple sources of data, but do so in a way that allows them to compliment, confirm, or refute each other. Saying you use big data and conduct market research isn’t enough; how you use them together is what allows for a more holistic view of insights.

The second C of data refers to data that is clear-cut. An important aspect of any data set, whether it be a result of market research or not, is to be as transparent and free of errors and bias as possible. To ensure data is clear-cut, regardless of the source, it should be challenged with rigor—meaning the data collection process, cleansing, analysis, and everything in-between should be evaluated against industry best practices. In addition, conduct tests before trusting the output. This can be done by comparing results to benchmarks when possible. When errors are identified, use established research methodologies to either correct the error or remove the bias causing the error.

Maybe the most abstract quality of data is the third C—how connected data should be. As noted, for data veracity to be upheld, it must be relevant and applicable: this is exactly what connected data applies to. In other words, does the data come from a recognized source? Is the data a common currency? Can the data integrate with technology or other solutions?

If the answer is no, then it’s probably not ready to use yet. To establish whether data is connectable or not you must

• Begin with the end goal in mind. Where, how, and when do you want to use the data and are there existing connections to those endpoints? For example, if media activation is the end goal, then can the data be connected to the data management platforms needed to enable media buying?
• Link data to existing measurement tools. If the data you leverage can’t be tied back to results, how valuable is it? This is especially true when it comes to using multiple sources of data which require performance comparisons.
• Ensure you have a technology-enabled integration. Sometimes it’s nothing to do with the data itself, but if the data is not integrated through a technology-enabled solution it won’t be scalable for your business and will have a limited impact.

So, to apply this framework in a real use case, we look to a recent coffee category study that used a methodology leveraging our 3C framework.

Putting It Into Practice

A coffee brand needed to increase market share in their category. There were a variety of ways they could go about doing this, but they turned to us to help. After conducting research, the results showed this particular coffee brand had an opportunity to grow through acquiring new customers. Specifically, they learned they should acquire customers who were female, married, no kids, and interested in healthy cooking, eating, and going to the gym. In order to reach this audience, they also learned that targeting them through email, YouTube advertisements, or website ads would be most effective.

With all those insights, to ensure the data veracity of them throughout the entire process, we applied the 3C framework in a variety of ways, including

1. Compiling data based on both survey data and big data in order to gather attitudinal and behavioral market share for more complete data
2. Vetting and normalizing data in order to ensure it was clear-cut
3. Incorporating category buyer insights for greater depth and relevance and to provide the connection for future marketing gains

Approaching data in this way establishes data veracity, enables true marketing ROI, and of course, improves results and impact of insights—just some of the things that drive the value of data most. To see how each of these things was achieved in more detail, download the infographic below.