Editorial intelligence: a paradigm shift for newsroom analytics

Content Insights was born out of editorial frustration about the analytics programs being used – and sold into – newsrooms. The problem was that the measures of success which were being used to rate, rank and analyse news  weren’t designed for the news and publishing industry – they were intended for a different sector entirely: advertisers. Our message has always been simple: for analytics to be useful in the newsroom, they need to have been developed with the newsroom in mind.

Too much data, not enough insight

“There’s a wave of data coming from customers and social media. And as the internet of things rolls out, there will be even more information on customers. Businesses are scrambling to figure out how they can extract value from that information”

That’s Richard Gordon, an analyst at Gartner, talking about data in the business world. For a few years now ‘Business Intelligence’ has been a thing – and we’re not talking corporate IQs.

Simply put, while analytics is a tech aided process in which software retrieves data, Business intelligence, is a process which goes a step further by interpreting and presenting that data in a digestible form before it reaches the intended recipient.

The reason why this is valuable? Well, unless you’ve asked precisely the right questions of that data, it doesn’t matter how good the numbers look, or how attractive the interface is: without relevant interpretation it’s still not going to be great use to you and your business.

Since it’s been embraced by the business world, it’s been a bit of a game changer.

Time for a change in publishing, too

In case you were in any doubt about where this was taking us, here’s the point: the same needs to happen in the journalism sector.

Analytics are, of course, commonplace in newsrooms. Analytics packages abound. We’re all likely aware of the problems and limitations of single metrics in the industry, and though the cult of the page view appears to be diminishing a little, it’s still a predominant force because quite simply it’s a convenient and apparently universal measure of ‘success’ – whatever that means.

The problem with universal solutions to complex problems is that simple solutions are likely to be unable to deal with the kind of complexity that each individual client, newsroom, or even department, requires. Sure, it would be lovely to have to deal with a binary measure of failure or success, but in the real world there are just too many variables, too many nuances for this to be convenient to anyone, save the people trying to market those ‘solutions’.

Analytics are undoubtedly better now on the UX front that they have been, but all the prettification in the world can’t change the fact that if all you’re doing is presenting raw data, you’re going to be no closer to what it actually means without some serious background in data analysis. And, although there are undoubtedly exceptions to this rule, most editors have neither this kind of skill set nor this kind of training – and certainly not the kind of time that’s required to do this properly.

Just as the first computers were not designed to be operated by those outside the then newly emerging field of computer science, neither has this first iteration of data analytics in newsrooms been able to be incorporated into the day to day workflows. When we talk about the resistance to ‘data culture’ in journalism, it’s hard not to empathize.

A brief interlude where we introduce an analogy

Take your car. The chances are you bought it, and that you didn’t build it. Being unable to tell the difference between a camshaft and a carburetor doesn’t preclude you from being able to the drive the thing and the process of driving is usually explained in different language to the process of fixing, tinkering or building it. In fact, automotive industry development has advanced to the point where very few of us focus on much beyond the user experience. When a typical driver thinks about performance, the things which are often discussed are efficiency, comfort, speed.

That industry understood long ago – just as Gordon says business did more recently – that products must ultimately serve their user, not their developers.

So, while the data – the raw material – is of course essential, it’s the context and insight that data reveals that’s absolutely key. The value comes from aligning data and information with a frame of reference and presenting it thus. It doesn’t ask that editors understand every detailed nuance and nor should it. Better to put editors’ and journalists’ skills where they’re most valuable, which surely makes better business sense anyway?

The missing link

Written down like this it seems a self-evident point, but we’ve spoken to enough newsrooms in the development of the CPI tool to know that this bridge between analytics and newsroom is far from common practice.

We’ve witnessed an evolution of analytics. From having almost no information about genuine patterns of consumption from our readers, we now have potentially more data than we know what to do with – and, more often than not, we don’t know what to do with it.

The problem has been that because there’s historically been no way to effectively incorporate and instill data culture into the newsroom workflow, there has been no opportunity for editors and journalists to shape its evolution. It has been left to those outside the editorial world – namely advertisers – to develop an analytics tool, but because that tool was designed to enhance advertising efficiency, it does nothing to aid editorial and journalistic practice.

Content Insights’ VP for Latin America, John Reichertz, has said: “the best way to get this data culture flowing through our newsrooms is to get everybody involved” and he’s right: improving access can and does have a transformative effect on the newsroom. If journalists understand the effectiveness of their own stories within the context of their own departments and specific audiences, they’re more likely to produce more efficient content. Similarly, with access to nuanced information, editors are increasingly able to make smart choices about where to place which resources.

we now have potentially more data than we know what to do with – and, more often than not, we don’t know what to do with it

Editorial Intelligence

Ultimately, we need to get to the point where analytics aren’t just a by-word for data presented in graphs and charts. They should do more than that because – now – they can. They must provide insight, context and meaning, and do so in line with the needs of not only each news organization, but each journalist in each department of that news organization.

When you’re working with an analytics approach that’s designed with the specific needs, abilities and requirement of the newsroom in mind, this information can only sharpen editorial instinct, it can’t undermine it.  It’s about integrating useful insights into the day to day workflows of the newsroom so that these kind of analytics are as user-friendly and commonplace as opening an email or uploading an article. There’s no reason why the user experience should be contingent on advanced degrees in data analysis, and that’s the difference.

We like to call it Editorial Intelligence and we think it’s the paradigm shift the industry needs.


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