Dennis D. McDonald ( is an independent consultant located in Alexandria Virginia. His services and capabilities are described here. Application areas include project, program, and data management; market assessment, digital strategy, and program planning; change and content management; social media; and, technology adoption. Follow him on Google+. He also publishes on and aNewDomain.

Do Real People Care About Disruptive Technologies?

By Dennis D. McDonald

One of the most interesting blog posts I’ve read recently is 8 Disruptive Technology Changes by Robin Bloor. Here is his list:

  1. Multicore Chips
  2. The GPU Absorbs the CPU
  3. Memory Replacing Disk
  4. The Network Infrastructure Absorbs the Servers
  5. The Network Becomes Real-Time
  6. The Network Gets A Management Circuit
  7. Virtualization: Everything goes Virtual
  8. Cloud Computing

What Robin’s list suggests to me is that, increasingly, it doesn’t matter where work gets done in a computer system as long as it gets done, you control it, and you can get at the results. Worlds like “virtual” and “cloud” are pointing in that direction. In line with this, the increasing popularity of web-enabled smartphones and ultraportable devices makes it easier for people to keep in touch, work, and communicate with friends and jobs.

It makes sense, though, to distinguish between technologies that are disruptive because they have the potential for fundamentally altering the technology infrastructure surrounding computing, and technologies that are disruptive because they have the potential for changing how people work. The two overlap but are are not necessarily the same.

Processing data more quickly and at a lower temperature, and displaying it more beautifully on a handheld device, does not necessarily change the nature of the use that is being made of that data. Moving away from a spinning disc to solid state memory for storing and accessing digital files may reduce the size and weight of a device, but by itself that won’t alter what is being done with the data. In fact, it may be difficult to convince management to invest in some of these “disruptive” technologies if they have the potential for disrupting — too much and too expensively — the processes by which mixed-platform technology architectures are maintained.

As I pointed out in The Justification of Enterprise Web 2.0 Project Expenditures, it’s one thing to automate manually performed processes, as happened in many cases in the move to client server systems back in the 20th century. It’s another thing entirely to move to fundamentally different architecture that not only alters maintenance costs but which may also have unknown impacts on work and supported business processes.

Still, I think that Robin’s list is not just referring to a “more/better/faster” definition of disruption. Taking the technologies together suggests that, as he states, “From a technology perspective, we are in the most disruptive phase of Information Technology since the computer was born.” The question is, though, how these technologies will impact the things that people use technology to support. We’ve already seen how digital technologies have shaken the structure and business models of the communication, publishing,  and entertainment industries.

My own votes for “disruptive technologies” are not on this list but are substantially enabled by the technologies that are.

My first vote goes for social networking technologies and the ability they give people to communicate and collaborate across many of the traditional boundaries around organizations, neighborhoods, professions, associations, countries, and other traditional ways of organizing work and relationships. It will be interesting to see, for example, if the Chinese government can continue its current level of political control over its population without also increasing control over how people form relationships and interact with each other both socially and professionally.

My second vote goes for genetic technology and the increasing understanding we are developing of how evolution works and how individuals are similar to, or different from, each other. Today’s rapid advances in genetic sciences have been enabled by the application of powerful and tireless computing technologies that perform the countless, complex, and repetitive steps needed to gather and analyze data in ways undreamed of by previous generations that spent untold hours over fruit fly or plant trait inheritance experiments.

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