Whether you call it the new oil or the new gold, since the The Economist declared data the world’s most valuable resource way back in 2017, its value only seems to have risen. Like most of our peers in marketing and the media it’s certainly become more integral to what we do as an organisation. Virtually every project we take on now involves an element of data measurement and analysis. At the same time the near-perpetual excitement surrounding data and the emerging technologies it fuels, like artificial intelligence, has caused even its biggest advocates to warn we’re in a time of “data gluttony.”
I wouldn’t dare claim to be a data scientist, but it’s quickly become clear to me that both the skeptics and the data champions are right. In many fields, including marketing, data can be an immensely powerful tool for targeting audiences and measuring outcomes – one global study from consultancy Bain, for example, shows leading marketers are more likely to refresh the data they use and consistently factor it into decisions. But there’s also plenty of evidence of data failing to deliver the desired results, or steering companies in the wrong direction.
What it comes down is that getting data is no generally longer a problem; extracting meaning from it is. With so many data sources and metrics at our disposal, trying to process information into something that can be acted on is often the mental equivalent of trying to drink from a firehose. In this kind of environment it’s no wonder people crave simplicity and jump at any clear conclusion they can get – which can fuel some pretty questionable decisions.
Bigger isn’t always better
As an example one recent project had us delving into the Twitter traffic on a certain topic to see who was effectively leading the conversation on it. One organisation seemed to be clearly in the forefront with almost everything they posted garnering a truly exceptional number of retweets – surely a sign they were doing something right? Until we dug a little deeper and noticed the vast majority of these retweets were from just one or two sources. Their closest competitor may have been a distant second in terms of sheer numbers, but its shares came from a far more diverse base – a much stronger indicator of credibility in our view.
In addition to the obsession with volume, data analytics (as many marketers practice it) is excessively retroactive. AI-powered predictive analytics is starting to make some impressive inroads into marketing, but in general the majority of analysis concentrates on results.
The fact is, by the time the data tells you conclusively something isn’t working, it’s too late – whereas a degree of analysis before a campaign is launched might prevent you from going down an unproductive path in the first place. Applying the right tools, there’s an incredible amount you can learn from what (and where, and how frequently) peers or competitors are publishing on a subject that can then be factored into your plans – whether on the themes that strike a chord with professional networks or what phrases risk putting you in the jargon danger zone. Our head of digital will be sharing more on those possibilities in the weeks to come.
Whatever light it sheds on audience engagement or the topics of the day, to me it’s only become clearer that data needs to be examined from a number of angles, and filtered through the lens of good old-fashioned human inquiry and cynicism. That’s why even as our practice becomes more ‘digital,’ I’d prefer to call it data informed, rather than ‘data-driven’ – no offense to our future robot overlords intended.