6 steps to building confidence in your data

The importance of having confidence in your data as a researcher goes without saying. Whether you’re reporting results, breaking bad news or otherwise, you need to be able to swear by your data.

You may be making recommendations for a new product, advising on messaging or comms, or sharing a shift in consumer behaviours. Either way, your data will likely have a significant financial impact, and so you need to be able to stake your life upon it (though no reasonable company will hold you to that!).

How many times have you had to give a client or stakeholder feedback knowing it’s not what they want or expect to hear? This could be the CMO’s pet project or a campaign that an entire department has already sunk many hours into. So how can we build confidence in our data? How can we ensure our data has integrity?

What is data integrity?

Data integrity refers to the accuracy, validity, and completeness of data throughout its lifecycle. Data might lose integrity and become compromised through replication, alteration, or data loss. Anything which has the potential to change data after the fact could result in its being compromised. Data might even be compromised from the point of collection, through inappropriate collection methods and techniques, or source selection.

How do you ensure data integrity?

1. Background research

This is a given. you need to know your subject to have any chance of identifying things that look out of place. General reading around a topic and comparison with other, existing data sources can help give you an idea of what to expect, as well as highlight deviations and anomalies.

2. Really developing a partnership with the client

They will have the background data, sector sales data and more. Don’t bank on it being volunteered, but you can bet your bottom dollar it’ll surface if the research data doesn’t meet their expectations. To this end, making sure you really become an extension of your clients’ internal teams and becoming intimately familiar with their business and what they do can help give you a ‘sixth sense’ when it comes to understanding their data, especially when it might be compromised.

3. Press them for information

Following on from the previous point, don’t always expect useful information to be immediately provided. Press them for what you need. In the same way you might tell a client specifically and unequivocally what information and resources you need when briefing for a creative project, tell them what you need when briefing for data collection projects. See what results they’re expecting & don’t be afraid to question how well-founded this is. Ask for previous results, the context of these results and the methodologies used.

4. Actively monitor during fieldwork

Ongoing monitoring of results and projects helps to highlight when things might have gone awry. The earlier you catch any potential hitches in your data collection, the easier it is to address and the sooner you can do something about it. Look at your KPI’s so you can open a discussion up at the earliest opportunity if something isn’t working.

5. Don’t remove or suppress undesired responses without good reason

Whilst it’s perfectly acceptable to remove or suppress interviews from fraudulent respondents or bots there’s a clear line to be drawn between cleansing the data and suppressing unpalatable responses; e.g. unpleasant language may have been used in response to an open-ended question that you think your client will take issue with. If in doubt seek advice from sources such as the MRS Codeline service

6. Be confident in your abilities as a researcher!

You’re the expert! Your clients might know their industry inside out, but you know data collection like nobody else does. After all, they came to you for your expertise. This, alongside diligently covering all the other points in this article means you’ve done all you can to ensure data integrity, and that can only boost confidence in your data.

What do you do about fraudulent data?

  • Be aware of what the causes are: Bots, professional panellists and “spammers” in general can compromise the data you collect and are all examples of responses you should consider removing. They might be trying to access more incentives, or they might just be bored, but if you can spot these responses, you can omit them. Spotting these types of respondents can also help you remove them from future activities and avoid dishing out your precious incentives to participants who might not deserve them.
  • Build fraud safeguards into your activities: Build quality gates into your questionnaire, and use the technologies at your disposal to combat fraud; it might come at a cost of a little extra time in field, and a few more man hours, but ask yourself whether you’re prepared to  compromise your data, and possibly even your reputation.
  • Don’t over-gamify: Don’t kid yourself that “gamification” of a survey is going to gloss over what is ultimately a long & boring survey. It’s been suggested that 10-15 minutes is a sweet spot for an online survey; too much over that and you run the risk of respondents becoming disengaged mid-survey and rushing through the rest of it. It’s a much harder ask to disqualify ever-increasing volumes of interviews from a survey when a pattern is emerging that’s predominantly driven by poor survey design. Treat your audience with respect and they will do the same when responding to your surveys.

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