As a blogger I spend quite a lot of time searching the internet for inspiration, information and opinion. Sometimes I know what I’m going to write about and sometimes I’m just fishing for the spark that sets the whole thing off. This produces two things – blogs (which is the whole point) and data (which is a by-product).
Every time I access a search engine or visit a site it creates data about that interaction that is collated, sorted, stored and (occasionally) used, but this is only the beginning of Big Data as we know it today.
When it comes to marketing, data is undoubtedly useful. It’s great to know what your prospective customers might be typing into a search engine and where they might be when they’re doing so, but the inherent problem with Big Data is in its very scope.
The internet exploration that has bought me here today will also have created some misleading data, I cast my eye over an article about using elephants as a scale of measurement, but zoology and quantity surveying are not really of interest to me.
I also read articles that I did not agree with, visited web pages with grammar that bought me out in hives and read one blog that I actually found quite offensive. So while the owners of those sites may be pleased to harvest my data and send me their next marketing campaign, I will be less than pleased to receive it.
When we’re trying to sort out what data is (and is not) useful it helps to think of it like water. Businesses rely on data that comes in a reliable, controllable stream (like a tap) sometimes referred to as Small Data. It helps them understand their marketplace in order to formulate marketing strategies and develop campaigns that target the right people. But too much data becomes a flood that overwhelms businesses hindering their progress and bad data (like dirty water) is not only less than useless, it can spoil the data around it. In this particular simile Big Data is a veritable tidal wave of information and without the capability to manage it correctly it can easily sweep away everything in its path.
From a Big Data perspective my internet shenanigans created lots more information than you might expect. On top of all the actual data I generated there’s a proportion of implied data that comes to life too. Blogging is part of my job so therefore I’m employed, a taxpayer and the proud owner of a national insurance number. I do not work from home so therefore I have transport needs. My computer uses electricity so therefore I have energy requirements. Already I’ve qualified for a plethora of marketing lists and that’s without even beginning to look at the trail of electronic communication that I create every day, or considering the fact that I bank online and my GP has a computerised system for recording my health. (I also inadvertently clicked on a link to an advert for cat food, and I don’t have a cat – sorry).
When so much information is generated it becomes fairly easy to find proof of just about any hypothesis you can think of, for example my cat food mistake could well become part of an ‘increasing demand for pet food in the East Midlands’. Data rarely allows for the foibles and failings that may create it and is always ready to trip those who may rely entirely on its veracity.
Big Data is a messy place and whether or not the thought of incessant spying keeps you awake at night, there’s still plenty to think about.
For many of the businesses that we work with the data balancing act neatly divides into two areas for consideration ‘data in’ and ‘data out’.
‘Data In’ is the stuff that will help you develop your product or service.
‘Data Out’ is about whittling that information down to the stuff that you need to share in order to persuade them to buy it.
So let’s pretend that Sid has invented an amazing new thingummy that will revolutionise how people brush their hair, Sid thinks it’s a great idea and he’s sifted through some Big Data and found out that lots of people have hair and a large proportion of those that do claim to brush it at least once a day. Sid knows exactly what the hair care market is worth and has worked out the exact demographic of his target audience and priced his product accordingly. He’s even done some good old fashioned market research which has created some Thick Data which when added to the Big Data has led Sid to believe that there is a vast untapped market for his new triangular hair detangling apparatus (RRP £49.99, batteries not included). Sid has paid someone to develop the prototype (who have no doubt consulted some of their own data too) and travelled around the world (creating travel data) to look at manufacturing facilities before placing an initial order for 50,000 units.
Everything Sid’s done so far has been backed up by seemingly sound data and now all he has to do is get the retailers on board. Obviously all the remains is to cram all the data (Big, Thick and Small) that has bought Sid to where he is today into a lovely presentation where it will make every retailer as excited as Sid and the orders will come flowing in.
Unfortunately, that simply won’t work.
The data that Sid collated and used is more than likely interesting only to Sid. It’s also quite likely that any data which didn’t reinforce his obvious excitement regarding his genius invention was ignored and /or replaced (apologies to Sid here, he is an otherwise upstanding and honest citizen). What the retailers need to know is how Sid’s fango dango new device will sit within their product range, how it will appeal to their customer base and whether the supply arrangements and costs are right for them. No problem at all, Sid has all that data too, just add it in to the presentation and we’ll be onto a winner.
But that won’t work either.
Because data is like water a great presentation should contain just enough, served in the right way, to efficiently quench your audiences thirst. Too much data and they’ll struggle to swim through it.
Balancing data is a tricky business and when it comes to presentations there’s more to consider than you might think. Audience Heatmaps are important in understanding which data to include and which to discard. Incorporating data into your story can be challenging and displaying data in a way that engages might well involve using infographics, graphs or charts.
As we know the presentation landscape is also changing and presentations are becoming less formal and more interactive making it even trickier to communicate raw data effectively.
Here at Eyeful we’ve been challenging concepts on presentation content for a while now and managing Big Data comes naturally to us. We’ve developed ways to help our customers identify the data that matters to their audience and then express it in a way that engages them.
We know that endless graphs and chart are soporific and that it’s easy to alienate an audience if they feel that you’re trying to blind them with science. How do we know? We simply asked them.
If you’re worried that Big Data might be drowning your ability to communicate effectively then we’d be happy to show you how your presentation can be improved with a Free Presentation Healthcheck (which will generate no extraneous data at all, but may well make a huge difference to your business).