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Can I trust the data?

Can you trust the data? It’s a question every researcher has asked themselves at some point in the process. The answers will depend on several factors.

Do you trust the source?

Where has the data been collected? Is it first party, through a process you or your team has overseen and dictated the structure for, or is it third party information? If the data was collected by you or your team, what steps have you taken to validate the data?

For researchers dealing with quantitative data, assessing its validity will always be a concern. The ultimate source of that data should be the initial guarantor of its bona fides; assuming it is gathered by the researcher themselves. After all, if you can’t trust your own data, then you are truly lost.

Has your data source declared its processes of collection?

Obtaining data from third party sources can greatly speed up the research process, but some caveats must apply:

  • Do you know what their methodology was? 
  • Are you sure the collection process took steps to validate the data? 
  • Are those processes as rigorous as your own? 
  • Will they share their process with you?

Your initial project management for any research project should take into account your likely data sources and their trustworthiness. If you or your team are personally gathering data, then it should be assumed to be relatively clean. If the data comes from surveys or questionnaires, then the initial design stages of that research should be utterly rigorous to ensure that there is little room for error or manipulation by respondents. After all, people will frequently tell researchers what they think you want to hear. Equally, as many researchers have proven by examining some survey companies, when you pay for answers, you may be getting the wrong ones, as Insightflow has discussed in the past.

What does that new data tell us?

Additional data is only useful if it tells you something new. Does it add any extra weight to your research project? Receiving data which simply confirms well-known facts is less useful, unless you need further examples in order to cite your findings. If you’re paying for new data, then this question becomes even more pertinent. 

Who collected the data and why?

What is the agenda of the people who collected the data? It may seem a strange question, but it’s worth considering, given the way data can be manipulated. Knowing the motivation behind a research project can shed light on the outcomes and data produced. It’s worth taking some time to explore that, in case it impacts your own project, particularly if the results are surprising or lead your research in a new direction. 

How do you plan to use the data?

Is the new data a cornerstone of your research project? One which defines its outcome and the potential insights you will produce at the end? If this is the case, then you may wish to consider a much more rigorous approach to validation than data used to simply add emphasis to your argument, or to provide context to a statement made in your insights. The level of trust you place in the data may well depend on its importance to the overall project, which is something you or the project lead will have to establish before the process fully gets underway. 

What do we do with the data at the end of the project?

Your research is complete. You have produced your reports and insights and are about to move on to the next project. What do you plan to do with all of the data you have collected? Your answer may be more important than you think. 

  • Information and data are valuable. 
  • Don’t delete it or dump it on a dusty hard drive. 
  • Summarise the data and let your colleagues know it exists
  • Create a research repository where that information can be captured and re-used by you or your colleagues. 

Creating a research repository and insight library provides you and your company with a powerful store of information which you can revisit and sift for further insights in the future, adding value to not only that project but also to your own team.

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