5 ways to succeed at data storytelling

We have become a nation obsessed with data. We wait for daily stats and facts about coronavirus, looking for signs that the data is improving so the current lockdown will end. Data will be our saviour – when the data says we’re winning the war, then our lives can slowly return to normal.

Our lexis around data, too, has changed beyond recognition. Once upon a time, data analysis would have been mostly consigned to statisticians, economists, and finance. Nowadays, we consume data for breakfast, lunch and dinner.

There are journalists and entire editorial teams dedicated to collating and computing data for headlines. As consumers, we don’t shy away from percentages and broken-down figures, in fact they’re usually our take-away soundbites. And herein lies the opportunity for PRs and organisations. Not only will you capture your audience’s attention with a data-based narrative, you’ll instil more confidence in your brand, attach more credibility to your spokesperson, and demonstrate that you can be the trusted advisor your prospects need.

At Resonance, we have experience identifying, analysing and producing such stories. Last month, we analysed England’s population data to highlight the areas most at-risk of coronavirus according to age. The heatmap we produced was used across the Mail on Sunday and Mail Online. Our previous work with Exasol and UK prescription data won us PR Project of the Year.

So, what does it take to create a strong, compelling data story?

  1. There is such a thing as bad data

Don’t be fooled into thinking that all data will make a successful story. When a data set is too small, the disparities too great, and the correlations too tiny, it’s unwise to shape it into an impactful story.

Data can also be difficult to analyse for one source of truth. In fact, in many instances it can be contradictory and difficult to pin down. When dealing with data, you should ensure that you are factually representing your findings – the most important thing is to be able to confidently stand behind your methodology and sources.

  1. Go where the data takes you

While you often set out to analyse a data set or launch a survey to pursue a certain narrative, there’s a chance that you’ll end up with something difficult. If this is the case, don’t force the statistics to mould into your desired story.

Follow the data, it might give you something new and unexpected. The purpose of your story should always be to inform the reader, so use the information that most interested you as a data scientist.

It’s also important to note that data is only the start. It needs a story, a mix of qualitative and quantitative information, and perhaps a voice to interpret it in the form of a quote from a third party.

  1. Visualise

The easier the data is to digest, the better. Consumers don’t want to root around an article for their key take-aways, so heatmaps, infographics and visual statistics are a great accompaniment to a story.

Journalists, too, like to understand the gist of the information as quickly as possible, so you’ll also increase the chance that your story and visual will be included in a target publication.

  1. Aim big

The holy grail of data storytelling is a huge – national, if possible – dataset. The beauty of analysing such a large source of information is that your results will be nigh on impossible to dispute. You can accurately track trends and anomalies in the very data that makes up that area of interest.

Our analysis, for example, of England’s ONS data accurately revealed localities that had a higher ratio of over 80s – giving us a story and heatmap with unquestionable findings. 

While big datasets such as these can be difficult to obtain and expensive to create, PRs and organisations should always strive for this. Small data sets have value too – for example consumer surveys – but try to find extra information that can corroborate what you’re saying. The sum of two independent sources of data is bigger than its parts.

  1. See what’s already out there

When looking to analyse quantified information, there are lots of sources already out there that can be tapped into. Here are a few examples:

  • Public information. In the UK there are datasets available on data.gov.uk, the ONS website and NHS Digital. In the US they similarly have the data.gov, census.gov and healthdata.gov websites.
  • Open data on AWS - https://registry.opendata.aws/ - a real mix of open datasets
  • Data held by UK public bodies can be requested through Freedom of Information (FOI) requests. You can also look at previously requested FOI results at https://www.whatdotheyknow.com/
  • Google Trends http://www.google.com/trends/explore- I find this is often useful to test out a hypothesis

If you’re interested in learning more about data storytelling, and how we can help you achieve results with data, do get in touch!

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