3 Big Data Implications for Insights Practitioners

Walking away from Market Research in the Mobile World North America in April, our minds were on data, data, and more data. A few thoughts we walked away with… 

More Data Than Ever – Are We Actioning It?

A key theme discussed across various presenters is the fact that data is being amassed at a faster rate than ever. According to Forbes, 90% of the worlds data has been produced in the last 2 years! What are we doing with it, though? If we’re relying on out-of-the-box analytics tools, we’re likely missing some of the richness that human insight can bring.

Challenge to Insights Practitioners:

• Leverage the best of the tools, while using the data to answer your strategic business objectives by rolling up your sleeves to build custom metrics or manually structure unstructured data.
• Look beyond the out-of-the-box metrics and get creative about piecing things together to tell a cohesive story.

Multi-Method Tells a More Holistic Story

Many presenters were grappling with reconciling emerging data sets that reveal behavioral data like clicks and transactions with the typical metrics needed for decision-making. These data sets require strategic thinking, paired with additional methods to uncover the holistic story of why consumers are doing what they’re doing. We’re big fans of transactional data because it’s more honest than projected, opinion-based data. It’s often a bit messier than the surveys of the past, though. This mess is worth navigating because it yields more reliable data.

When we work with teams to test critical assumptions on their path to qualifying a product or service for launch, it’s crucial that we use a variety of these data sets to systematically de-risk the new product or service.

A challenge to Insights Practitioners:

• Be willing to build hypotheses from messy data. Test these hypotheses through additional experiments. It’s no longer about one data point!
• Ask: “how can we leverage existing passive data with primary research, or other existing data in order to better inform this decision?”

Use Common Sense & Healthy Skepticism

Many presenters raised awareness that with the growth of AI, machine learning, smarter algorithms, and panels that promise speed and agility, there is always a risk that the data you are gathering is not always from real humans, or that a few click farms are amassing lots of fake data. We’ve seen this to be appallingly true over the past 10 years of conducting social media ethnography. Often, when we’re cleaning data, only a handful of comments have been from humans in a dataset of hundreds or thousands.

A challenge to Insights Practitioners:

• Ask: “How well does this fit with the rest of the data we have amassed? Does it make common sense?”
• Dig into the actual data behind the numbers. Expect to have to clean the data and don’t take out-of-the-box analysis tools at face value without checking to make sure it’s built on reliable inputs.
• Check out this resource that was shared on ways to “beat the cheaters.”

We left MRMW with a reminder that the data is messier than ever, but worth the effort because it can also be more accurate and more behavioral than ever. How is your team leveraging the mess that is big data?