In April, I had the opportunity to attend the Insights Association's Ignite: Data Quality conference in New York—an event that brought together some of the industry’s sharpest minds and most forward-thinking voices. The discussions were rich with insights on strategies, methodologies, and technologies that are shaping the future of our field.
What stood out to me most was not just the validation of many of the techniques we already use at Phase 5, but also the exposure to new approaches—like the intriguing “Qualchas” concept shared by Atym’s Rossi Dobrikova.
Speakers such as Tia Mauer (Procter & Gamble), Brad Franz (Burke), Katie Casavant (Data Quality Co-Op), and Paul Johnson (The Harris Poll) emphasized a vital reminder: our respondents are people first. They may differ from us in language, culture, communication style, and educational background. When we’re too quick to dismiss responses that seem implausible, we risk introducing bias and systematically excluding voices that deserve to be heard.
Throughout the sessions and hallway conversations, a shared message rang clear—data quality is not a one-party issue. It requires a collaborative effort across the ecosystem: clients, agencies, sample providers, and tech platforms. And yes, agencies and clients play a role… in our designing methods and surveys; we must consider respondents as equal stakeholders, not just a resource to be mined. We should all know that long, and poorly designed surveys can "turn a good egg bad". Tammy Rosner from Dynata presented compelling data on respondent engagement, highlighting a metric I believe is too often overlooked: the completion ratio, in which drop offs should be considered as one of the indicators of disengagement.
One of the biggest takeaways for me was the reminder of the classic project management triangle. As an industry, we’ve been clamouring to make things faster and cheaper, and it’s easy to forget the quality aspect, and I think there is a need to pivot back to that. AI does not help, unless we harness it for good things. With the growing use of AI in our workflows, the industry has leaned even more heavily into faster and cheaper. But we can’t afford to sacrifice quality. AI should enhance our work, not shortcut it. It’s on all of us to use it wisely—and responsibly—so we elevate standards rather than erode them.