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. 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:

LinkedIn Visual Data

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 technology vendor Jugnoo (click to expand):

Visual data screen

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 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.

Challenge on.

image: Kris Krug

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Comments

  1. says

    Great post Danny!
    I have the same view as you here – there are so many data companies these days, measuring something in a “cool” way. But really, when you look into the data, it doesn’t mean anything. Or at least the people developing it don’t really know how to apply it.
    I can think of several local companies actually that are experiencing this right now. They’re measuring big data, it sounds cool, but when it comes to business application, they’re doing a terrible job of explaining how to use it.

  2. data_nerd says

    tommy_landry Without context, insight, cleansing, structural knowledge, etc, it takes more than “want” to make data sing. Thanks 4 mention!

  3. says

    Hey Danny have you read datajournalismhandbook.org
    One of my favorites. Thought you might like it if you haven’t run into already. 
    You are really dialed in to something I’ve been thinking about lately….it’s one of the reasons I think journalism has a lot to offer, because data is shaped by context, attitudes, stories, conceptual frameworks, etc… I kid you not, I got a call recently from one of the world’s largest self-billed “integrated marketing communications agencies” and a very senior person told me they wanted to provide us with a few topics to get large data sets on, and then visualize it to find out what was important, and all of this was going to to fit into their client’s image as a thought leader. Now, if she’d offered me 100k I probably would have said, sure I’ll make it work, but her idea was that you could just put a bunch of data into a visualizer and see what comes out. That’s the kind of misperception that reinforces your point about context.

  4. says

    JoeCardillo Hey there Joe, I haven’t, but will certainly put it on my list following your recommendation – just checked the site out, looks a great resource.
    Now I really want to know who that agency was because, yes, that’s a fantastic way to spend your client’s money… Like you say, seems education still has a long way to go. Ah well, who’s up for the slog? 😉

  5. says

    This is a classic dilemma. Is the data accurate? Did the data acquired meet what we need? Did your analysis of the data result in the right outcome?
    Context is key. Often when businesses want to automate it is to save costs. But somethings you can’t automate.
    I run social for 2 businesses. I am always checking out our customers on social media. A lot of my research is based on my experience.

    Automated Scenario 1} Subject A tweets a lot because Hootsuite says they tweets 10x per day
    Subjective Scenario 1] Subject A tweets alot with 2 other accounts 90% of the time #context

  6. says

    Howie Goldfarb Exactly, Howie – it’s why I appreciate the thinking of Traackr CEO PierreLoic, who’s a firm believer in the human touch that will always be required, no matter how advanced Big Data mining gets.
    While it’s cool to have stuff like http://www.youtube.com/watch?v=NJarxpYyoFI keep us on our toes, the human mind (for me) is way too complex and can adapt to situations much better than any machine. Such is the case for Big Data – like you say, some things you just can’t automate.

  7. says

    Completely agree.
    I was COO at a data-driven marketing agency in New York a couple of years ago, and big data was a huge part of our offering. And I would say this:
    – Big data analysis is exciting when it comes to correlations, but crap at establishing causation. Many marketers end up with lousy analysis — and they don’t even know it.
    – 9/10 of the data value is created by the human filter (context). These analysts must understand big data mathematics AND the client’s business and industry. 
    – Majority of companies only look at single data-sets, but it’s running them across each other that’s interesting.
    – Majority of companies isn’t merging their internal analytics data with their external monitoring, nor with their sales data — unless it’s about e-commerce.
    Personally, I think that big data is truly promising. But it’s extremely complicated — and to your point, wildly and widely misused.

  8. says

    Jerry Silfwer Great points, Jerry. A couple of months back, I was in San Francisco as part of the book tour, and one of the questions raised at the event was: “What would you advise anyone looking to succeed in influence marketing?” While the question was about influence, it also gels well with your point on the 9/10 ration, since my answer was, “Hire people that understand people.”
    We can have millions of terabytes of data, but if we miss the nuances and subtle inflections of human behaviour, we have nothing. 
    Here’s to more folks like you leading the way, sir.

  9. says

    I totally agree with you Danny Brown. Everyone’s missing the point. Just because it’s a fad doesn’t mean we have to follow. What’s more important today is how this tool can be useful for data analysis or I mean are they that important or reliable? :)

  10. says

    Danny Brown BelindaSummers It’s total a waste of effort, time and MONEY. You can even used manual metrics if you want to, well that is if you have more people because that’s a lot of data. For me, google analytics is still a big help though.

  11. says

    Hey, I’m Texan…so I *HAVE* driven at night with the lights off…wearing sunglasses. C’mon, everybody remembers Corey Hart’s song, right…so I had to try it as a teenager.

    I agree with the comments around context and Big Data. So, Mantis will focus on the Big Data (we even dedicated a page to it on the new website)…and count on the domain experts like yourself to recognize the context that allows you to find the correlations in the data.

    Of course, we then have the expertise to make that plenty visual for you marketer-types that like pretty pictures and interactivity!

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