[Sigia-l] RE: Data vs. Information

[Thomas Vander Wal]

On 1/6/03 10:03 PM, "Boniface Lau" <boniface_lau at compuserve.com> wrote:

>> -----Original Message-----
>> From: sigia-l-admin at asis.org [mailto:sigia-l-admin at asis.org]On
>> Behalf Of Thomas Vander Wal
> 
> [...]
>> Since most of us interact with these folks, if not where these hats
>> it is good to know the difference so that we can intelligently
>> converse with fields we need to embrace to do our jobs well.
> 
> Looking down from the 64-thousand feet level, data and information are
> clearly distinguishable. But as we descent closer and closer to the
> ground level, the distinction becomes more and more difficult to
> make.

Because much of Usability, Information Architecture, and user-centered
design relies on it depends and blurry lines do we avoid them?  No.  We
learn and try to grasp the basic, like the distinction between data, and
information.  You may ask why?  Because we are crazy individuals that care
about the user and care about information being found and used.  To throw
out solid basic understanding because it is not a clear distinct line

You also just proved the distinction between data and information.  From the
64k foot view we can see the difference between data and information.  We
have enough data from the 64k view to create usable information and the
difference is distinguishable.  When looking at the lower levels there is
less of a distinction, hence we are looking at data and not information.  We
are lacking enough data to make a pattern, context, discernable
understanding of what we are looking at by being too close.  When we have
distinguishable patterns we have information and when we don't we have data.
When we can act on those distinguishable patterns that make up the
information we have knowledge.  Providing proper information is the key to
commerce, this is why Amazon has been successful, they find the patterns in
their data that provides information to their users.
 
>> 
>> The lines between data and information are blurred at best.
> 
> Thus, trying to draw a clear line between the two is futile.

The practice of drawing clear lines is anything but futile and it is why
most of us are Information Architects.  Drawing a clear line takes many data
points: the data, the user, the intended/expected use of the information
(once the line from data to information is crossed), the form of the data,
the form and medium and presentation of the data/information, etc all play a
role in drawing the line.  Understanding the finer points helps us
understand when we have a product that is usable and findable.  We are
Information Architects and not Data Architects (I have been a Data
Architect/data modeler/data analyst whose role has been to find data that
builds information that is usable).  As IAs we work with full pieces of
information and other times it is smaller chunks of information that may be
just a collection of data points to some.  We should now the difference, if
we charge clients we must know the difference as that is why we get paid.

At times we need to rely on the visualization of data points and take a
higher level view to see the hundreds of data points as a line and see the
patterns that emerge.  The line becomes our information.   See
http://www.fhwa.dot.gov/ohim/tvtw/02jultvt/figure1.htm, which shows a line
connecting the data points of the billions of miles of total vehicle miles
traveled in the U.S. From 1977 to present.  The individual data points would
not provide the information the line connecting the data points provides.
The line show dips in the increase of vehicle miles traveled at certain
points.  The flattening of the line and dips occur at the Iranian Crisis in
1979, a slight recession in 1982, the Gulf War in 1991, gas price increase
in 1999, and the beginning of an economic downturn in 2001.  Seeing the
changes in the normal trend of the line lead us to look at what was
happening in the world around the data points.  Seeing the data points when
combined with other data points auxiliary data provides enough clues to see
a pattern that becomes usable information.  We do not need to see the line
of data points if we have an algorithm that can use a predictive line trend
that will allow us to understand when there are variations to the increasing
trend and when the trend returns to its normal increase.  The visual display
of this information assists the user by providing enough information to look
for more information to describe what is happening.  To users who do not
understand what the line is describing the line is meaningless until the
user is told to look for the variations of the line, then describing
possible causation events.

All the best,
Thomas

-- 
www.vanderwal.net

The future is mine, not Microsoft's