Data, mayonnaise and logistics

I remember once walking around the shelves of an enormous trade-only
food store. The size of the containers on the shelves created a weird
sensation. I felt as if I had been deposited onto the movie set of a low
budget science fiction film. A film featuring shrunken humans doing
battle with regular sized (but now enormous) mayonnaise containers. Come
to think of it, it could also have been a movie dealing with regular
sized humans doing battle with genuinely enormous mayonnaise containers.
This would have been closer to the real world but somehow a lot less fun
to think about.

In food distribution, it is no surprise that the raw material - be it
mayonnaise or any other foodstuff - is the same stuff regardless of the
size of the containers or the energy/complexity fabric of the
distribution network. Enormous containers or regular containers,
supertankers or egg cups, mayonnaise is always mayonnaise.

One of my most cherished analogies is that enterprise computing is a
glorified form of a distribution problem. How to get data (mayonnaise)
from producers (humans/applications) to consumers (humans/applications)
in the best way possible. The word 'best' here being a holder for an
enterprise defined metric - examples include least cost, fastest
end-to-end time, highest throughput, etc.

In the real world, we are completely comfortable with the idea that
although the raw material is the same, the size of the containers and
the machinery used to move the containers changes dramatically with
scale. In the world of bits and bites, we are less comfortable with this
notion. In a world of bits and bytes, do the same scaling issues from
the world of atoms apply in the distribution networks we create?

Clearly, some of the scaling issues do not apply. Geography for example,
is a major driver for the field of physical logistics. Moving stuff from
place to place involves moving things physically from one location on
the planet to another. In the world of bits, geography ceases to be a
defining factor. Two 'places' in virtual space can be right next to each
other in physical space but miles apart in other ways.

The most important yardstick for distance in the logistics of digital
data, is not geography, it is semantics. Two chunks of data are
logistically in the same place in virtual space if they have the same
meaning. Data payloads that share semantics flow friction free, distance
free in the virtual world.

Why does Microsoft Excel flow so easily from machine to machine in an
accounting practice? Because all machines therein are, logistically
speaking, in the same semantic place - they share an understanding of
Excel.

What about documents? Do they flow friction free in virtual space?
Definitely not. The semantics of a Microsoft Word document are not
shared by an OpenOffice document or by a FrameMaker document or by one
of the many variations of an HTML document.

Sign up for ITworld's Daily newsletter
Follow ITworld on Twitter @IT_world

• Email
• Print
• Share
  • Slashdot
  • Digg
  • Stumble
  • Reddit
  • Newsvine