One of the best investments any company striving to be more customer-centric can make is an insights engine.

A Harvard Business Review article describes insights engines as market research groups with a “fundamentally strategic role,” responsible for distilling insights that “enable transformational actions” for their organizations. We’ve defined an insights engine as “a combination of the right people, technology, and processes to increase the accessibility and visibility of insights, ultimately empowering stakeholders to make customer-centric decisions.”

In both definitions, the key is that research groups must be distilling and distributing insights in ways that drive meaningful business decisions. And that means organizations shouldn’t just be investing in conducting or commissioning research, but in ensuring the resulting insights are packaged, shared, and preserved in ways that maximize their value.

Implementing an insights management platform, which centralizes insights and makes them easily accessible to stakeholders, is often one of the first steps

Editor’s Note: Usually to end the year we post predictions from a variety of industry leaders. However, this year we decided to take a different tack and sit down with one of the true seers of the insights industry: Simon Chadwick, and to dive deep into a conversation on what the trends we saw play out in 2022 might mean for what we’ll see in 2023.

The full conversation can be found on the GreenBook Podcast, but for those who want a quick summary of the major ideas we experimented with the AI system ChatGPT to high points. Not only was the core interview fantastic, but the quality of the summary from the AI system is mind-blowingly good as well. Perhaps 2023 will be the year of AI? We shall see …

As the year 2022 comes to a close, it’s a good time to look back at the

In the insights industry, experts have described 2022 as the Year of Data Quality. There is no doubt that it has been a hot topic of discussion and debates throughout the year. However, we find common ground where most agree there is no silver bullet to address data quality issues in surveys.

As the Swiss cheese model suggests, to have the best chance of preventing survey fraud and poor data quality we need to approach the problem by thinking of it in terms of layers of protection that are implemented throughout the research process.

To this end, the Insights Association Data Integrity Initiative Council has published a hands-on toolkit. It includes a Checks of Integrity Framework with concrete data integrity measures. This is essential to all phases of survey research: pre-survey, in-survey, and post-survey.

The biggest challenge yet remains: objectively defining data quality

What constitutes good data quality remains nebulous.