How do you maintain the integrity of your
market research data?  (Intro)



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Geoff expands on :

  • Why your market research platform needs to be data-source agnostic.
  • How you can cater to all the needs of all the people in the research workflow.
  • Why speed to insight is critical.


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In other videos in this series, Geoff talks about 

  • How you can determine if your market research platform is fit for purpose.
  • Why adopting new market research tools doesn't have to be as hard as you may think.


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How do you maintain the integrity of your market research data?

There's a really important principle when you're preparing research data for generating insights, and that is, you want to maintain the integrity of that data. Legacy systems - so that would be older systems that have been around the research industry for a long time, tend to be fragmented; they're siloed. So you'll have one platform that does the data collection, you then need to get the data to another platform that does the analysis, you then need to get the data to another platform that helps turn those analyses into visuals, and then you might even have another, in fact you almost definitely have another platform that gets those insights out to the client.

Every time you move data from one system to another you're introducing inefficiencies, so it takes time to do that. But more importantly, you are introducing scope for error; so every time you move data from one platform to another, something can go wrong.  You're breaking the connection from the original source to the thing that you're doing next, the analysis, and then the visualization and then the reporting. But it's worse than that, not only are you introducing scope for error, (You) you're adding a burden of if you want to make a change, you have to make a change now in multiple places. And that means that you've got more scope for error, data integrity is lost, potentially lost, and whenever you want to do anything, so say if you realize that you've got a brand name change, you've now got to change that brand or that label in all those multiple places that you replicated the data. So that's a very important principle, is 'one source of truth'. You've only got your data once, you only ever record it once, you store it once and everything goes back to that one source of truth. That eliminates the scope for error, and it makes everything, all that workflow that you do with the data, efficient and fast.

- Geoff Lowe, Executive Director