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Telling stories with data

By Anne Janssen, ZPB Analyst

Data adds another dimension to a story. It provides insight, legitimacy, enhancing your message creating impact and important context. It always needs to be handled with responsibility and integrity – using data to stand up a story that’s not valid is wrong – but ultimately damages your brand.

I am the analyst at ZPB, and I work with the client teams to help them to use data and insight to communicate

In this blog, I am going to walk through how I use data to help our work have as much impact as possible, provide an example of where this approach has proven effective and how to use data to describe the value our clients bring, the context in which they work and the potential for positive change in how services are delivered and how patients receive care.

In this example for our client Hourglass, a charity that campaigns for the end of abuse of elderly people, we sought to find some insight into people’s attitudes to abuse in older people.

When using data, it is important to start out with a hypothesis. Ideally, this should be open-ended, since at this point you don’t always know what the data is going to look like. However, it is important to have a clearly defined question in mind when starting out.

A poor hypothesis is vague and has no point of context and can be answered with ‘Yes’ or ‘No’:

‘Do people think abuse of older people is bad?’

A good hypothesis has context and allows for further investigation:

‘What are people’s attitudes towards abuse in older people?’

Once you’ve narrowed down your hypothesis, it is time to look through available data, for example, NHS Digital data on hospital admissions, to figure out what you have available to work with. In an ideal world, you would find a dataset that answers your question perfectly, but more often than not, the data is not always available exactly how you need it. This is where creativity comes into play. Whether it is combining multiple datasets to start forming a story or working the story around a single dataset, it is important to be flexible. If no data is available for your purpose, it can be a good approach to conduct polling, especially to assess people’s attitudes around an issue, as with the example above.

After initially screening the data and refining the hypothesis, you can dive into the deep analysis of the data. This is the time to be investigative and think carefully about what meaning you draw from the data. Here is where most of the insight happens. It is important to interpret your data in the context of your strategy. At this point, it is important to keep your audience in mind. For the general public, figures that relate to their personal experience or make them think about their own loved ones are impactful. For commissioners, more system focussed data can be more useful. It’s easy to get carried away and analyse the data in every possible variation, but sometimes top-line data is enough to supplement a story. Sometimes, however, you will inevitably find the story more deeply buried in the data, especially with polling data, where often the nuance of regional data or age-groups can bring more insight to the story. This will mostly depend on your audience and the role the data will play in your story. When the story is data-led, most of the time, it will pay off to analyse the data in more detail. When the story could stand on its own, but the data adds a little more credibility and dimension, deep analysis can potentially have a negative impact by overcomplicating it.

Example:
As part of a wider relaunch campaign, we aimed to raise awareness of the issue of elder abuse in the UK, with the assumption that the severity of the issue is often underestimated. We started by researching whether there were any statistics available on ONS, NHS Digital or NHS Statistics, or other publicly available sources. After finding that there was not a lot of information on the prevalence of elder abuse published, we decided to assess public opinion of elder abuse in the UK by conducting a survey designed to give us insight into what people perceive as elder abuse and what they think would help tackle the issue. We analysed the survey data and cut it both by age groups and by regions to identify any insightful differences and used that to build media stories to raise awareness. Through this approach, we were able to identify the most impactful stories and managed to raise awareness of elder abuse in the media.

Analysis doesn’t necessarily just mean digging deeper into data. It can also mean combining different data sets to create a more holistic approach to a story. When done right, even a few numbers can add value to a story by emphasising and underlining your key messages.

Overall, data can be important to grab people’s interest. When used effectively, it adds credibility and gravitas to communications that can massively enhance the validity and impact of communications whether that is a social media campaign, a news story, a marketing campaign or a thought leadership report.


For more on the Hourglass campaign launch, see the case study here.

Get in touch with Alex Kafetz if you’d like to find out more about how the ZPB Analytics Unit can support you.