A blog about policy communications and digital public affairs, in a networked age.

Policy Communicator Blog | Aidan Muller

 

The Policy Communicator Blog

Aidan Muller

Helping organisations shape political and policy conversations.

A blog about policy communications and digital public affairs, in a networked age.

The blog includes commentary on new developments and trends in the sector, original models and frameworks, best practice and case studies. Insights draw heavily from the latest developments in cognitive psychology and linguistics.

In addition, the blog addresses broader societal issues, as they relate to communicating in politicised environments. Our success as a sector is not just contingent on what we do individually or as an organisation – it is also largely tied these days to the nature and health of our information environment.


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This data is interesting – but what can we do with it?

 

When Google released data on Theresa May-related searches last week, to celebrate her 100 days in office, a few people raised an all-too-familiar question.

It goes something like this: “Ooh, that’s quite interesting – but what can we do with it?”

This is a question which any communications professional who has introduced analytics to a team or organisation will have heard before. How can we leverage this intelligence for the company or for clients?

The simple answer is often: “With difficulty.” In its current form, this data is ‘only’ interesting.

This is not to say it has no value. Google searches are a good indicator of how concerned the population is about a particular topic. The findings were interesting enough for the Telegraph to use as a basis for an article comparing May’s first 100 days to other party leaders’.

The data might even stretch to being quite useful for May’s army of Special Advisers, as they add it to the piles of information they collect about her ratings or media coverage, on a daily basis. And it might provide fodder for a whimsical tweet by a political consultant.

But there is a gulf between interesting and useful to know. And an even bigger gulf between useful and actionable

Data is not insight.

Ultimately, data is just data. It is not automatically imbued with meaning. It is not inherently actionable. Insights, on the other hand, tick both of those boxes.

The difference between data and insight is worthy of a post in and of itself, and there are already many excellent articles on the subject. This post by Kristin Kovner, for example, sets out five rules to turn data into insights.

In summary, insights should tell a story which leads to action. And to do this, we need to:

  1. Make the data relatable: In this case, can we provide a benchmark? How does interest in May compare to other party leaders (as the Telegraph piece sought to show)? And how do all of these compare to interest in Paul Hollywood (as it didn’t).

  2. Provide context: Is a high level of interest good or bad? Is this interest positive or negative (or neutral)? What is driving this interest (as Google sought to do by providing topics)? Why those topics in particular (as it didn’t)?

  3. Enrich the data through detail or anecdote: Why were the public so obsessed with how tall she is? Further investigation shows the peak to be on the day she became PM. Was it the picture of her kneeling in front of the Queen; or maybe the one with her husband on the doorstep of Number 10? Can we confirm by cross-referencing with another dataset (e.g. data from social media)?

The reality is that insights rarely come pre-packaged and off the shelf. It is our job as communications professionals to give meaning to the data. We have to work the data to earn the insights.

We don’t owe the data anything.

The crucial point here is that ‘What can we do with it?’ is the wrong question to ask. It’s not about the data, or the platform. This is tantamount to trying to find a problem for our solution.

Instead, we need to start with this question: ‘If we could get the answer to any question in the world, which questions would we ask ourselves to improve our communications, or to shape our campaign strategy?’

Having defined those questions, we can move to identifying the appropriate data sources which will allow us to create the graphs to answer our questions.

Sometimes it may be more than one data source. Often, one question will require a combination of graphs. And occasionally, we might have to satisfy ourselves – at least initially – with proxy indicators and imperfect datasets, to paraphrase McKinsey.

Google didn’t produce the data on Theresa May with the aim to be useful (though it might inadvertently have been useful for some). They produced it to showcase what Google Trends can do.

So in answer to the original question of what to do with it: we can turn it into insights, if we’re willing to put in additional work. Failing that, the key takeaway is simply to understand the mechanics of the platform.

And the next time we ask ourselves a question which involves public awareness or interest, we know where we can find the answer!

 
Aidan Muller