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Convergence Analytics and the 2014 Gartner Hype Cycle

  |  August 25, 2014   |  Comments

A look at this year's Gartner Hype Cycle report, and a deep dive into what its results mean for digital marketers in terms of convergence analytics.

Every year, Gartner Group updates its "Hype Cycle" report, and this year was no exception.

figure-1-gartner-hype-cycle-2014-8-21-14

But if you look at the Hype Cycle chart close (go ahead, really squint at the chart) and let it sink in, only two areas of technology out of 119 (representing more than 2,000 individual technologies) will reach a plateau in the next two years (In Memory Analytics and Search Recognition). The rest of the areas might not mature until the 2020s.

For example, "Big Data" isn't going to plateau for another five to 10 years according to this report, and businesses are already getting disillusioned (going from the "Peak of Inflated Expectation" down to the "Trough of Disillusionment").

From the standpoint of convergence analytics, the predominance of immature technology groups in the Hype Chart should raise some red flags, particularly from investment perspective, as immature technologies are much harder to integrate (converge), and the use cases around those technologies (how the data will be used to support and drive better business decisions) are either not built out yet, or not widely enough deployed to be fairly evaluated. It's also much easier to "hype" up a technology whose efficacy has yet to significantly be manifested; I suppose that is why Gartner called their report "Hype Cycle" in the first place.

figure-2-gartner-hype-cycle-2014-six-business-era-models-in-the-digital-business-development-path-8-21-14

I did my own exploratory investigation (at a 10,000-foot level, naturally, expanding on my last post on the Analytics Selfie at ClickZ) via LinkedIn queries, to take a look at the last three phases to the Gartner Hype Cycle (Gartner did not define or focus on the first three of the six phases, analog, Web, and e-business - what they call "business models," because they already matured and essentially became commodities, therefore very resistant to being hyped).

figure-3-gartner-hype-cycle-2014-linkedin-queries-on-stages-4-6-8-21-14

Based on Figure 3, stage five (Digital Business) is easy to hype and have inflated expectations about, whereas stage six (Autonomous) has very few companies or workers identified, based on LinkedIn queries, with the corresponding technology areas (such as Bioacoustics Sensing; 3-D Bioprinting Systems; Machine-to-Machine Communication Services, 3-D Scanners or Consumer Telematics, to name a few).

figure-4-gartner-hype-cycle-2014-radar-diagram-linkedin-queries-on-stages-4-6-8-21-14

Would it make sense to build convergence (analytics) around such immature technologies that, while promising, haven't yet been developed enough to have a workable business model or business use case?

figure-5-gartner-hype-cycle-2014-common-use-cases-applied-to-linkedin-queries-on-stages-4-6-8-21-14

Looking at LinkedIn Query Search Results (I use "all" and focus on everything, not just people), most of the action is happening in phase four, and picking up in phase five, but is almost zilch in phase sox. Based on those results, the technologies for convergence would make the most sense to build out of the earlier phases, combining with phase four, and possibly phase five.

Overall, I think the Gartner Hype Cycle report is very useful because it identifies many potholes to avoid on the road to convergence, making it a little bit easier to build development roadmaps in our organizations that will ultimately be successful.

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ABOUT THE AUTHOR

Marshall Sponder

For over a decade Marshall Sponder has influenced the development of the digital analytics industry with his WebMetricsGuru writings that focus on social media metrics, analytics and media convergence. He also possesses considerable in-house corporate experience with roles at IBM, Monster.com, Porter Novelli, and WCG while continuing to work with start-ups. Marshall is a Board Member Emeritus at the Web Analytics Association (DAA) and teaches Web Intelligence at Rutgers University and Baruch Business College. Marshall is the author of "Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics," published by McGraw-Hill in 2011.

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