Technology Hype Cycles

I had a great idea this week. It started because I wanted to write about Business Intelligence and the benefits of flash memory for Decision Support Systems, but realised that it’s hard to mention those subjects these days without referencing Unstructured Data. That got me thinking that the hype surrounding Big Data and the way in which trends such as Cloud and In Memory Databases work.

Let’s consider the Y2K bug as an example. Back in the 1990’s it became apparent that systems which had not been coded to account for the Y2K issue might fail, so steps were taken to investigate and fix potential issues. This was clearly a Good Thing. However, as the hype surrounding Y2K exploded, every man and his dog felt the need to join the party, no matter how tenuous their connection, until it reached the point where (as I’ve mentioned before) the school that I attended when I was young received a letter from a “Y2K conformance specialist” offering to check the football pitch for “Y2K compliance”. I thought about these situations and then sketched a graph, very much like the one above, showing the hype rising inexorably, then falling away as everyone got bored of the idea (or saw through the charlatans), then finally rising slightly to reach a plateau.

I was very pleased with myself at this point and decided to write a blog showing the world how clever I was. However, a five second Google search was enough to pop that particular bubble of hubris, because it turns out that Gartner not only thought of this years ago but even publish their own Technology Hype Cycle every year. So instead of being clever it turns out I am just ignorant. No wonder Gartner’s net worth is significantly higher than mine. (And I bet you knew about this too didn’t you? So why didn’t you tell me? It could have saved us both a lot of time…)

Another thing I have to credit Gartner for is the fantastic names used to describe the phases of the cycle:

  1. Technology Trigger
  2. Peak of Inflated Expectations
  3. Trough of Disillusionment
  4. Slope of Enlightenment
  5. Plateau of Productivity

I love these names. The Peak of Inflated Expectations? I’m sure I’ve climbed that peak at some point. The Trough of Disillusionment? Wallowed in it. The Slope of Enlightenment? I’m hoping to climb it one day.

Anyway, now that we’ve established I am no substitute for Gartner, let’s have a look at the current Gartner 2012 Emerging Technologies Hype Cycle graph (courtesy of Forbes):

Gartner’s 2012 Emerging Technologies Hype Cycle

There are a couple of interesting things to note from this graph. Firstly, Big Data appears to be entering the phase known as the peak of inflated expectations. That sounds about right to me. Note that this is not the same thing as suggesting Big Data is a waste of time and should be ignored. Far from it. This phase is identified as the period when “a frenzy of publicity typically generates over-enthusiasm and unrealistic expectations“. My reading of this is that some people are far too keen to throw away the benefits of relational data and transactional consistency in order to embrace a new trend which they feel their organisation should be following.

The other thing that really interests me is the position of In-Memory Database Management Systems, just beginning to accelerate downwards on the big ski-slope towards the trough of disillusionment where it will join In-Memory Analytics. This, lest we forget, is identified as the point where technologies “fail to meet expectations and quickly become unfashionable“. Gartner also indicates that they believe it will be 2-5 years before these In-Memory technologies reach the plateau of productivity, where their benefits “become widely demonstrated and accepted“. Again, it’s important to emphasise that a technology located in the trough of disillusionment is not a bad technology nor one that should be avoided; it is merely one where any potential suitor should be extremely careful to ignore the marketing hype and concentrate on the facts.


Now, I am fully aware that we all see what we want to see, so you may disagree with my perception here. But for the In-Memory technologies I see a correlation between the over-hyping of the term In-Memory Database and the redefinition of Oracle Exadata X3 as a “Database In-Memory Machine“. I also see a correlation between the gradual maturity of SAP HANA and the suggestion that In-Memory technologies will achieve the plateau of productivity within 2-5 years. I am not convinced that In-Memory technologies will become “unfashionable” but I do believe that there is a danger users (and potential users) will become sceptical about the claimed benefits of IMDBs. As more vendors attempt to portray their products as In-Memory I feel this is inevitable.

Maybe you agree, or maybe you see it differently; in either case I’d love to here your views. In the meantime it’s back to the drawing board for me, to see if my latest idea will make me a million. I’ve decided to place vendors on a sort of square graph and divide it into quarters, which I am going to call the Magic Quadrangle. I just need to check if it’s been done before

[Legal Notice: I am (unfortunately) not the inventor of either the Gartner Technology Hype Cycle or the Gartner Magic Quadrant. All copyrights and intellectual property around the Technology Hype Curve and the Magic Quadrant are therefore owned by Gartner, Inc. If you find the content discussed here interesting then I urge you to go to and purchase a subscription so that you can avoid being as spectacularly uninformed as I was prior to researching this article.]


2 Responses to Technology Hype Cycles

  1. Brian Pardy says:

    Great post. I’m a DBA in an SAP shop so I’m hearing plenty about HANA as well. I see a lot of hope and inflated expectations coming from the SAP development side that it will cure all our ills, meanwhile there’s a comparable amount of mistrust of it on the operations/admin side. We see people complaining about how slow “the system” is at times when the not-in-memory database is nearly idle — a RAM-backed database isn’t going to help there, it’s the algorithms and processing techniques used in working with the data that are inefficient. This won’t be cured by HANA or TimesTen or anything, so it seems like a classic case where the hype will lead to early adopters that end up disappointed, stomping it down into the trough of disillusionment. Then, over time, stuck with this dog of a purchase, dev teams and vendors will learn to make use of it and eventually reach that plateau of productivity.

    So many of the technologies called out in the graphic require fundamental rethinking of processes and access patterns and measurement methods. Just to take a couple of them, NFC payment would be great if it were ubiquitous, allowing numerous efficiencies. Until then, though, it’s a gimmick (and I own an NFC-capable device and let a $10 NFC payment credit expire). Complex-event processing requires a more holistic understanding of one’s business than most are capable of getting. I would add “Enterprise Architecture” to the chart, somewhere between Gesture Control and In-Memory Analytics — it was all the buzz a while back, we were going to give the on-the-line business users all these mix-and-match blocks of business processes they’d be able to reorg on the fly for increased productivity; instead it’s turned into at-best shelfware to point at like an ISO9000 certification: “yes, we have an enterprise architecture”.

    Our business investment climate is based on growth more than functionality. Starting from zero, a new tech, useless as it currently is, experiences massive growth. Once people start using it in production and find it doesn’t solve everything (as they’re all inevitably hyped to do), the early adopters either learn to get some use out of it or consider it a stinker and tell all of their contacts about it.

    This S-curve seems to be a fact of life that shows up in numerous contexts, from bacterial growth to collapse and leveling off to technology lifecycle uptake, abandonment and acceptance to many more.

    Again, good post.

    • flashdba says:

      HANA is a bit of an unusual one, because although In-Memory technologies are still in the midst of the hype curve, HANA is SAP’s strategic direction. I can imagine a point in the future where SAP only supports HANA for some or all of its modules. And since SAP is a massive company with a huge number of customers, that kinda makes HANA the future. Sure, some customers might hang back whilst they wait for it to mature – and it’s possible that others might decide to go with an alternative ERP solution (Fusion Middleware anyone? No?) – but the backing of SAP almost makes HANA a success by default.

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