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The Great Escape

The Great Escape: Big Data Breaks Gartner’s Hype Cycle Model

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In August, “Big Data” did something unusual. It broke Gartner’s Hype Cycle model.

This is a model for emerging technologies that describes the initial, excessive excitement about a new technology, rising to the “Peak of Inflated Expectations”, before falling into the inevitable “Trough of Disillusionment”, where people give up on the technology having had their hopes quashed, before rising steadily again towards the “Plateau of Productivity”, where it starts to generate real value across the board, and is adopted.

The Gartner Hype Cycle model has been around for several decades, so it’s a well-worn path for emerging tech. It’s also one of the key pillars on which the reputation of the research firm rests; so it’s an important and well respected model. Which is why it’s interesting that it has now been broken.

Breaking The Mould

Last year, Big Data was falling from the ‘Peak’, and was sliding headlong into the‘Trough of Disillusionment’. This year, it has disappeared entirely.  That’s not because it has stopped existing or has been rendered obsolete, nor because it’s too small for anyone to worry about – which are the usual (and perfectly acceptable) reasons for Gartner to drop items from the Hype Cycle.

But because it’s now ubiquitous. Big Data “has become prevalent in our lives across many hype cycles”, as the Gartner Analyst responsible for breaking the news to the world, Betsy Burton, put it, “[it] has become a part of many hype cycles”.

In other words, Big Data has occurred in so many places that it’s already being used beneficially, and so is happily gambolling in the fields of the “Plateau of Productivity”. It has missed out the “Trough of Disillusionment”altogether; it’s broken the model.

That Big Data has moved so quickly to become useful in different areas of business is an interesting conclusion in itself. I’m sure those data scientists still struggling to get IT to integrate all their data into their Hadoop cluster might question that, and it would be very wrong to assume that it will be all plain sailing for all things Big Data from now on. But the challenge has moved from the technological side, to the people and processes side of the equation.It’s all very well to be able to store and query vast volumes of data, but you need to have the right resources to do this.

It’s All About Perspective

That’s the nature of technology adoption. Perhaps what’s more interesting about the story is people’s reaction to the news.

Some, particularly in the “Big Data” industry, appear to be mortified – “Big data is losing some of its lustre”, said one Information Week blogger, almost nostalgic for the good old days when simply using the words “Big Data” could practically guarantee you a conversation with the CEO.

Others seemed to disagree with the conclusion, presumably based on their experiences at the front line of data strategy – “for those in the data storage and analytics industry who use the phrase “big data” and expect others to nod in agreement, taking big data off the hype curve may seem a bit premature”(Datanami).

It’s as if the “Big Data” industry feels cheated, because it has lost its claim to be cutting edge.

At the same time, non-data analysts have taken different standpoints.  The neologists have moved on already to the next cool trend – “But don’t fret, dear readers, ‘Smart Dust’ has just made it on at the bottom, while both the Internet of Things and autonomous vehicles are about to pass the ‘Peak of Inflated Expectations’ and slide down into the ‘Trough of Disillusionment’ where they’ll each spend five to ten years” (The Register).

However, the one area that seems to have gone unchallenged is the model itself, and the Gartner analysts.

I would have thought that identifying that something has gained “hype escape velocity” and so missed out on the disillusionment stage would be a fairly major moment. This is especially true if you then are brave enough to call this out.  But that’s not what Betsy Burton said. In response to Big Data’s escape from the sequence, she said, rather huffily – “I would not consider big data to be an emerging technology. This hype cycle is very focused. I look at emerging trends.” So, that’s alright then?

  • If it’s possible for emerging trends to not follow this well-worn path to productivity, what is it that makes these different?
  • What has happened with Big Data that hasn’t happened with other developments?
  • Why isn’t this the story that people are talking about?  I think they – we – should be.

Big Data, Demythologised

Primarily, I think the reason why Big Data absconded the cycle is that it doesn’t really exist. Or, at least, it’s possible to draw the “big” line anywhere. When does “regularly sized” data become “big” data? A myth developed that told us that greater volumes of data were better, and generated something of an arms race for server space. If Big Data represents anything, it denotes the connecting of different data sets to provide a new set of previously unachievable insights. But that’s not really a technology – that’s an approach.

Which brings us to the second point. Even if you have endless, seamlessly integrated data environments, with perfectly cleansed data, it’s still only as useful as the information you can extract from it. That means that you need the best insight analysts or modellers/coders to work on the data to generate these insight nuggets.

We know several companies that have embarked on building and progressing these huge environments. However, the analysts are either extremely busy working on day-to-day tasks, or siloed in their individual business units and either not permitted, willing or able to use data from elsewhere in the business.

Meanwhile…the really valuable opportunities that data could help these businesses to unlock, sits there; untouched and unanalysed.

What’s important, then, is not what we call “Big Data” or where it sits on an emblematic cycle, but what we do with it and how we go about collecting, evaluating and using the insights that it yields.

Big Data has made a lucky escape from Gartner’s ‘Hype Cycle’. Now we can finally move on from the buzz word myth that clouds the term and work to realise its real value. Let’s be pleased that Big Data has finally been ‘de-hyped’. Now we can focus on what really counts: making the data work for you.


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