The value of high-quality data is evident within digital PR, since campaigns are shared with a brand name attached, meaning that the quality of that work is often deemed reflective of the quality of the brand.
What’s more, if your data is inaccurate or fails to meet the standards expected of ‘good quality data’, then you could even be opening your brand up to liability, since false claims can quickly evolve into defamatory ones. So, it is fair to say that there are risks attached to collating and sharing weaker datasets and research methods.
Moreover, the sole purpose of digital PR is to generate online visibility and backlinks for your brand or client. However, the likelihood of your campaign being picked up by publications and shared online is very small if your data isn’t credible or reliable.
So, why waste your time on crafting datasets that won’t gain traction, could risk legal action and that you aren’t proud to put your stamp on? The truth is, too many brands simply don’t have the quality checks or processes in place to ensure that their data is of a high standard, or they don’t know where to begin.
That’s why the data specialists at uStats have compiled a list of 8 simple ways that you can enhance and check the quality of your datasets, before releasing them to the world.
For your data to reach the high expectations required for a PR, you need to ensure that it is as up to date as possible and that no data that convincingly overrules it has been published since. This means that, prior to using the datasets you have sourced (if you are using secondary data), you need to check that no updated datasets are available and that the data source you are using is the most reliable and recent of its kind.
One top tip for sourcing up to date and reliable data is to sign up for data releases or create a data release calendar like that offered by the Office for National Statistics.
Another vital aspect of ensuring your data is of a high value is to keep it consistent. This often means that you use standardisation processes to ensure that your data is as accurate as possible, particularly where it relates to data from different sources, or data using different measurements.
If you happen to be extracting data from existing sources, you have a duty to ensure that these sources are reliable, authoritative and trustworthy, since it is your obligation to first verify the reliability of any data before using it for your own work. To do this, you should ask yourself the following questions:
Another top tip for ensuring that your data is high quality is to ensure that you have extracted it from an appropriately sized data pool.
By this, we mean that if you are conducting a survey, for example, you need to have a substantial number of respondents from representative backgrounds for the results to be credible. Likewise, if you are looking to assess annual trends, looking at the data from just one month alone may not reflect this very well, so you will need to branch out and study longer, more substantial periods of time.
For optimal data quality, you must also ensure that the data is actually relevant to what you are trying to prove.
One danger of data-scraping certain websites, for example, is that your scraping will pick up on results that are not relevant to, but mention a certain phrase in their text. It is important to have any checks in place prior to your data collection that can identify when these issues might arise.
With every high-quality dataset is a methodology that explains and maps the stages of research involved. This is valuable since it enables journalists and readers to verify the results by following the methodology themselves, but also because it increases the transparency behind your data collection, which presents your brand as more trustworthy as a result.
For best practice, your methodology should:
It is not uncommon for a piece of research to come from a place of bias, particularly since most campaign ideas will begin with a hypothesis that you are looking to either prove or disprove.
However, bias can remain apparent throughout data collection, even if it is subconscious. This bias can be as simple as neglecting certain values in your dataset, or asking leading questions within an interview. Therefore, it is important that your team proofs and checks your datasets from an outsider’s perspective to ensure the collection and analysis is as neutral and fair as possible.
To ensure that your data is of a high quality, you should ensure that your methodology accounts for any limitations in your processes, or any data that you have chosen to eliminate. This should be supplemented with a strong explanation as to why this has been done, and why it does not affect your study negatively.
If you struggle to justify these limitations, then it is a sure sign that your data quality may suffer in some way, and you should work on improving it.
At uStats.org, our team of highly qualified data analysts are on hand to assist brands with the creation of well-researched, authoritative and original datasets. So, if you want primary data for your next campaign without the hassle, email us today at firstname.lastname@example.org.