There’s a big push at the minute by marketers and technology vendors around the concept and importance of Big Data. Run a Google Search for the term and the resulting titles of posts, articles or books speak for themselves:
Big Data: The Next Frontier for Innovation, Competition and Productivity;
Big Data: A Revolution That Will Transform How We Live, Work and Think;
Big Data Transforms Business;
Put a Fork In Big Data – It’s Done (just to balance the positive/negative results).
So, Big Data is clearly big business, and – with more than 1.7 billion search results – something that businesses are looking to understand, come to grips with and benefit from.
That’s understandable – after all, the potential of Big Data is huge. My colleague Hessie Jones, for example, recently wrote an insightful piece on how Big Data is transforming advertising, and in March 2012, no less an institution than the White House itself announced the Big Data Research and Development Initiative.
So, yes, Big Data = Big News.
The thing is, though, while access to such huge amounts of data helps us be better marketers and – by association – better businesses, there’s also the danger that we let this data inform our decisions, without stopping to think of that most important aspect of any data analysis – context.
Context Drives Educated and Informed Decisions
Think of any major decision you’ve made in life, either personally or professionally. While there will be examples of impulse buys or snap decisions made in the heat of the moment, the majority of your actions will be based on the context surrounding them.
I wanted the sports car, but it wasn’t kid-friendly;
Job A offered more money, but Job B offered me deeper satisfaction;
The penthouse condo in the city offered amazing views, but the suburb neighbourhood was safer.
Three very simple examples of decisions that looked at the bigger picture of context, and took into account the long-term view versus the short-term buzz. Each option would satisfy our basic instincts, but the latter option of each choice is the one I’d go for based on its deeper context.
It’s simple economics of educated decisions, based on the data available – yet as the following examples show, context is still being missed where it’s needed the most.
Visual Data is Great, Real Data is Better
Professional social network LinkedIn is continuously looking to increase connections and the viability of its service with new additions, some useful, others less so. At least, currently.
One of the new features they’ve released is the visual ability to see who’s viewed your updates, and how far they’ve spread. Visually, it’s pretty cool, as can be seen below:
The problem is, functionality-wise, it’s very limited.
While the image on the left tells me my update had 536 views, it doesn’t allow me to dive into the data to see who actually viewed the update. The same with the image on the right – I can’t click into the big purple circle to identify the type of people viewing my content.
The potential for this visual data is obvious – I can see if I’m attracting my target audience to my content – either potential clients or new employers – and, by having access to this information, tailor my sharing even more, as well as connect with these folks in particular.
It’s not just LinkedIn that’s missing the importance of context, though. Check out the image below from a technology/data company in Toronto (click to expand):
The results are from a search around the words “social business”, and show not only the main keywords around the topic, but also who’s discussing them, via what platform, and the time they’re most likely to be discussed.
This basic data offers a simple overview of that particular search – but where’s the bigger context?
For example, you can see that “business” is the most discussed word, and then I’ve highlighted “product”, “agencies”, “customers” and “platform”. As you can see from the two yellow circles I’ve overlaid, a couple of people are in multiple results. So what’s the context behind that?
Is it simply because they mention the words together?
is it because they’re connected to these different communities?
Is it because they’re seen as influential around these joint topics?
Is it because they’re more active than the other profiles?
Again, these are simple questions, but ones that the software doesn’t answer, or at least attempts to help with. Because of this, other software and analysis is needed to see how valuable these folks might be to my business.
That’s not to advocate lazy marketing, nor to forget about the legwork that real analysis requires. But if a software tool can’t provide further context around the solution it offers, why use that platform at all?
Dig Deeper, Think Bigger
And this is where Big Data’s main weakness can be found – it’s encouraging lazy solutions that seem to offer reams of data, but in reality offer very little. By doing so, it’s impacting the true potential of Big Data when used properly.
It’s this type of limitation that’s attracting valid critique of Big Data.
In his 2013 paper entitled Big Data for Development: From Information to Knowledge Societies, Martin Hilbert raised the concern that Big Data-led decisions are “informed by the world as it was in the past, or, at best, as it currently is.”
Last year, Harvard Business Review published an article, Good Data Won’t Guarantee Good Decisions, which highlighted the bigger issues around the data available to us today.
For all the breathless promises about the return on investment in Big Data, however, companies face a challenge. Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making. Meeting these challenges requires anthropological skills and behavioral understanding—traits that are often in short supply in IT departments.
Simply put, we can have all the data in the world available to us, but unless we understand the context in which it’s presented, and the actions that will drive based on our analysis, we’re as effective as driving at night with the lights off.
It’s up to us to think bigger when it comes to Big Data, and start providing the context and meaning behind it, as opposed to just the “But it looks cool, right?” mindset that seems popular today.