Making sense of data: operatives at an IBM 711 card reader and IBM 704 electronic data processing machine in 1957. Image c/o NASA
For several years now, storytelling has been back in vogue as a concept for marketers to explore and exploit. It certainly felt as if it went away, with media strategists and planners viewing the 80s and 90s as a Blipvert-saturated mediaplex of snackable content (yes, I went there). Storytelling is back because we have a more developed, ambient media and campaigns which are able to work across a multitude of channels in a linear narrative. It’s why the term “transmedia” was hot for a while.
The great irony with storytelling is that its neologistic interpretation is often too confined, too restrained. Storytelling predates writing as a form of communication, and yet “storytelling” feels too confined to media: that marketing views telling stories as something done to consumers, rather than an activity which should permeate back throughout the organisation. In other words, storytellers simply aren’t telling enough stories.
So, how should storytellers tell more stories? Let me remind you of two probably-now-very-familiar words: big data. Companies and agencies are now churning out so much information because they can – Les raisons du bricolage – that volume is not keeping up with meaning.
Let’s take an example of web analytics, where we’re still focused on pageviews, CTR and even – and it pains me to say it – “hits”. That’s all good for volume, but is a decontextualized, stripped-of-meaning set of numbers what everyone wants? Of course not. Imagine throwing a birthday party where all of your friends and family are there, and recording in that day’s diary entry: “Birthday party. 35 people turned up.” That’s still how analytics are often perceived: a numbers-and-charts game.
The more that we can tell stories, make meaning, of numbers and charts, the better the outcome for all: for client, for agency, and for the end customer. Can you look at a chart for your project or enterprise and know the chain of events? This is where storytelling is so important: giving a narrative to data. A customer group that goes from one point to another and ends up with the completion of a task is not just a “user flow” or a “customer journey” – they are writing a story. They are giving you a story; it’s your opportunity to understand and interpret it.
I have been pondering this in recent days, while thinking about how IBM markets its offerings. They are grouped into an offer called Big Data & Analytics, and the more that I thought about it, the more that I see it as correct: big data is practically pointless in isolation – its value is its analysis and meaning. It’s something which IBM’s cognitive technology, Watson, might provide further help with in the future: the ability to answer questions such as “Based on this chart, why did customers in the east dump their cart first”, “Tell me the usability struggles which this group had, based on the differences in user duration between pages”, or even “What are the reasons behind this campaign being so successful in driving sales from Twitter”. It’s something which the “what if?” planning features from IBM Cognos can start to help with today.
The more we can apply human storytelling to data, the more meaning it has to our lives. While data analysts are rightly in demand, are we not missing an opportunity by giving roles to the other side of the coin – data storytellers? We need programmatic poets, bards of business, narrators of networks. While we’re heading towards a data-driven future, it’s a story-driven future that will mean at least as much to us.