It is that time of year again when tech geeks pull back the curtain to see how their new favorite technologies are developing and how long they are going to take to get to full blown success: The Gartner Hype Cycle for Emerging Technologies has been released.
For those not familiar with the concept, Gartner study emerging technologies to create a graphic that shows the success and development of specific technologies. It indicates how developed the technologies are in addition to predicting roughly how long it will take them to become mass-market ready and contains five stages through which they go before reaching mass market adoption:
- Innovation Trigger, this is the stage at which technologies become known amongst early adopters
- Peak of Inflated Expectations, the stage at which the expectations of the technology is hyped up to almost unrealistic expectations
- Trough of Disillusionment, here the realities of technologies becomes better known and the speed of their development frustrates people
- Slope of Enlightenment, the technology begins to pick up momentum and is more widely adopted
- Plateau of productivity, this is when technologies become more wide spread and accepted by a larger number of people/companies
This year's updated version does not throw up too many surprises, with technologies like 802.11ax and 4D printing just making their way onto the innovation trigger section, and Blockchain and Autonomous vehicles hitting their peak inflated expectations. However, the element that is truly indicative of the progress that we have seen within data is that 39.4% of everything on the graphic is directly related to data collection and use.
It says much of the impact that data has had on our society, given that in 2012 only 12.5% of those on the list were directly data related. Bearing in mind that in 2012 big data was not even at the peak of inflated expectations, its direct derivatives now make up more that one third of current emerging technologies.
There has even been controversy that some of the descriptions within these are too broad, with Gil Press asking of the inclusion of machine learning, ’Is [it] an “emerging technology” and is there a better term to describe what most of the hype is about nowadays in tech circles?' Instead, he argues, there should be 'deep learning' or 'artificial neural networks' used in its stead, given that machine learning is already a well established technology. Gil is certainly correct, with at least a relatively basic form of machine learning appearing in things like suggestion engines and programmatic advertising to some extent.
However, if we were to look at almost every element of the current hype cycle, there are only a handful of technologies that aren't at least profoundly influenced by data. Technologies like brain-computer interface and volumetric displays may not necessarily be directly data related, but they do require a considerable amount of it to function, showing that data is impacting almost every emerging technology in 2016.
This is not surprising given the rise in the amount of data being collected, Cisco believes that by the end of 2016 we will create a Zettabyte of data in one year, rising to 2.3 yearly by 2020. This growth in data is acting as a catalyst for the development of new data informed technologies and helping to not only inform innovators but also help with the functionality of technologies.
2016 may have been a year to forget in many ways, but it seems to have been a watershed for new data technologies.