In a recent media interview I was asked about whether the requirements for data visualization had changed. The questions were focused around whether users are still satisfied with dashboards, graphs and charts or do they have new needs, demands and expectations.
Arguably, Ancient Egyptian hieroglyphics were probably the first real "commercial" examples of data visualization (though many people before the Egyptians also used the same approach — but more often as a general communications tool). Since then, visualization of data has certainly always been both a popular and important topic. For example, Florence Nightingale changed the course of healthcare with a single compelling polar area chart on the causes of death during the Crimean War.
In looking at this question of how and why data visualization might be changing, I identified at least 5 major triggers. Namely:
Increasing volumes of data. It's no surprise that we now have to process much larger volumes of data. But this also impacts the ways we need to represent it. The volume of data stimulates new forms of visualization tools. While not all of these tools are new (strictly speaking), they have at least begun to find a much broader audience as we find the need to communicate much more information much more rapidly. Time walling and infographics are just two approaches that are not necessarily all that new but they have attracted much greater usage as a direct result of the increasing volume of data.
IT infrastructure and operations (I&O) people have long bemoaned their service desk or IT service management (ITSM) tools. It’s a fact of life, well ITSM-life anyway, and analysts will often pepper conversations with clients (and anyone else that will listen to them) with comments such as “that on average an organization will change ITSM tool every five years.” Some analysts quote longer, others quote less. In many ways, whether it is three, five, or seven years is unimportant. It is the fact that organizations are changing tools that is.
In a soon to be published joint Forrester and itSMF USA survey and report my colleague, Glenn O’Donnell, offers up an interesting service desk tool statistic: that, with the exception of SaaS tools, approximately 30% of responders are unhappy with their service desk tool.
Of course, one could argue that this is a little “glass half empty” (that I’m an analyst trying to line the pockets of ITSM-tool vendors) and that the “full glass” view is one where 70% of responders are happy with their service desk tools.
Yes, I could take this view, but I would be doing the ITSM Community a disservice. The big question for me is “why is SaaS only at 4% dissatisfaction?”
During my research for the just-published document "For Developers, Dog Food And Champagne Can't Be The Only Items On The Menu," I had an interesting conversation with the team at Adobe that handles internal pilots, which in their case entails more than just putting the next version of an Adobe product on the network for people to try. Instead of the typical "spaghetti against the wall" approach to "eating your own dog food" (to mix food metaphors), the Adobe team actively looks for use cases that fit the product. If a product like Flex or Photoshop is a tool, then it should be the right tool for some job. (And if you can't find any use for the software, you're definitely in trouble.)
This approach might require additional work above the "spaghetti against the wall" approach, but it definitely has dividends for many different groups. The product team identifies functionality gaps or usability flaws. Marketers and salespeople have a much easier time figuring out what to demo. As a result, Adobe runs a better chance of both building technology that's compelling, and then explaining what's compelling about it to potential customers.
[As promised, here's the first in the series about the tech industry's drive to reduce complexity.]
Remember the magic number? It's the one thing from Psych 101 that you should recall, since it pertains to memory. The brain has an upper limit on the number of chunks of new data it can stuff into working memory at one time. The number is around seven, plus or minus one or two depending on the person and the task. It's the limitation that makes the old game Simon challenging, and that bedevils us when we try to remember a phone number that someone just told us.
The magic number is one way in which the human brain tries to trim down complexity. Another more recent discovery is the brain's fuzzy boundary between literal and metaphorical statements. Attach a candidate's resume to a heavy clipboard instead of a light one, and the interviewer is more likely to treat the candidate seriously, because the resume seems somehow weightier.
Countless other examples exist where the brain takes shortcuts, filters information, and otherwise simplifies the constant, complex stream of perceptions, thoughts, feelings, and actions that would otherwise turn into a "blooming, buzzing confusion." We're not stupid creatures, but the machine that grants us powerful mental capabilities also puts limits on them.
The Death Star Would Be Great If I Could Figure Out What All These Buttons Do