What Defines Quality Insights?

Filed Under: Data Trends


Rich Ratcliff

I recently was a guest speaker at Suffolk University in Boston, MA within one of their master’s classes on Insights. My guest engagement focused on data quality. I had just come from the Quirk’s Chicago Event (Great job, Dan Quirk) where my conversations and attended sessions were all about data quality which made for an interesting mental backdrop. I asked the students ‘What defines Quality Insights?’  The dialogue that ensued really made me think about how each of us has a unique perspective on what defines quality insights. 

The concept of ‘defining quality insights’ can get esoteric, but I am setting the bar low here, with hopefully some pragmatic points. It is also the subject of acute scrutiny today given the fraudulent activity that has been a discussion point in many conferences and webinars already this year. I would like to pose a perspective that we can all agree on. Our whole industry needs to view ‘Quality Insights’ as our North Star. Any company should see its value as adding and/or contributing to that vision or we are at risk of a diminishing return. Yes, competition should be fierce because it creates better products; and yes, we absolutely have room to move around and have opinions on research methodologies, the tech innovations, collection techniques, and how to manage data quality, but we all have one particularly important common thread: our job as an industry is to produce quality insights. I am sure if we ask our main benefactors, the end clients, and their stakeholders, it would be their number one priority every time. 

So, in an attempt to define quality insights, here are my top three items that are staples in my definition. I hope it inspires you to design your own, and I would love to hear what they are.

# 1 – Fit for Purpose: I have been a fan of Melanie Courtright and what she’s been doing with the Insights Association since she took the helm. In the most recent Town Hall on Quality, she commented ‘You can’t talk quality without saying ‘Fit for Purpose.’ ‘Fit for purpose’ should be the question asked at every intersecting point of our process…

“Is the data fit for the purposes of transforming into Information?”
“Is this information fit for the purposes of transforming into insights?”

#2 – Data Dimensions: The research process tends to always have significant sets of milestones and checkpoints. I have always seen the dimensions of the project driving these data dimensions. These dimensions create a frame of quality that keeps the total process from going off the rail.

  • Accuracy/Veracity – Is the data representative of the universe we are measuring?
  • Timeliness – It’s not fit for purpose if the purpose has passed
  • Consistency/Stability – Correlation and Repeatability are the lineages to Quality Insights
  • Completeness – In research, this starts with ‘The Question.’ Putting aside the mechanical definition of ‘missing data,’ the analytic storytelling only happens if the total process started with the right ‘Question’ to be answered. 

#3 – Confidence: Yes, confidence in your network that helps you be a part of the ecosystem that creates quality insights. Making good decisions about your people, their processes, and the technology used must happen. Look around and make a mental note about who you have confidence in. If you are honest about that reflection, you will see quite a wide spectrum. That being said, here is a small checklist of just a few of those partners that change our world, create a better quality of life for us and make us look good. 

  • End Clients need confidence in their insights partners or insights platforms. That confidence builds trust and synergistic partnerships. 
  • Insights Agencies need confidence in their sample partner(s) as they deliver the qualified respondents needed. 
  • Having confidence in your project managers keeps those surprises from becoming showstoppers.
  • Having confidence in our researchers who are the keystone to our ecosystem. I am still amazed at their expertise and still get excited to read a good insights summary.

Lastly, I must give a plug to a group of folks that we have lost some confidence in lately with the hope that there is a bright future for them and the partnership we’ll have with them: The survey participants.  They play an important role in quality insights. 

So that is my attempt to get you, the reader, to think about what the definition of quality insights is for you. For me, the definition has evolved over the years, but the evolution has had a profound impact on whom I choose to surround myself with, what I choose to focus my efforts on, and lastly, it is my north star. 

Back to the Suffolk University students. I would be remiss if I did not make a plea to anyone reading this to get involved in creating a better welcome mat for our industry. Guest speaking to aspiring researchers on data quality has been a goal of mine for quite a while. I do not see a chance of sustained success unless we welcome, mentor, and guide our first-year professionals. I can guarantee that I received much more out of that class by giving my time, as it always gives me perspective.

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