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ACIA Main Page > Strange Connections > Little Blue Folders (July 10, 2000)
See also:
Autonomy
Northern Light
Semio
Fidelity's Folders
P.T. Barnum
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Peter Morville's bi-weekly column on the evolving definition of information architecture
Little Blue Folders
The Web is big. A billion pages big, according to a recent study by
Inktomi and the NEC Research Institute. It's the ultimate testing ground
for information retrieval technologies.
If your search engine can automatically bring order to this overwhelming
global mess of stuff, just think what it can do for a single web site or
intranet. No more agonizing over the design of topical hierarchies. No
more worrying about how you'll afford your growing staff of information
architects. Just sit back and let the software work its magic.
According to Northern Light,
there's no need to wait. Their television and
magazine ads claim they've developed "a search engine that delivers the
World Wide Web prioritized, categorized and organized into neat little
blue folders." And guess what, you can license this technology for your
web site or intranet.
Autonomy takes the
auto-classification hyperbole a step further, claiming
that their Portal-in-a-Box provides "an out-of-the-box solution that
enables online publishers and corporations to easily create and
automatically maintain an easy-to-navigate customized portal site,
removing the need for manual labor in the process of categorizing,
tagging, (and) hypertext linking large amounts of information."
The marketing folks at these companies obviously went to the P.T.
Barnum
Sucker-Born-Every-Minute School of Business. They are over-selling
these
automated classification products in a way that may pump up sales in the
short term, but will inevitably lead to a major back-lash as their
customers learn the hard way that software alone can't solve their portal
problems.
Information retrieval is inherently messy. Authors struggle to convey
complex concepts by stringing together words and phrases into documents.
Users try to articulate their information needs with a keyword or two.
Attempts to connect the right users with the right content are frustrated
by the ambiguity of language and organization and the subjective nature of
relevance.
If you take a mess and stuff it into a bunch of little blue folders, you
still have a mess. Remember the room-cleaning strategies of our
childhood? Take the dirty clothes, candy wrappers, banana peels, and
assorted pets and toys and shove them all under the bed. Declare your
room "neat and tidy" and head out to play.
For those of us with observant parents, we soon learned this to be a
play
now, pay later strategy. People who fall for the auto-magical claims
of
these search engine vendors are sure to learn the same lesson.
Perhaps the biggest problem with these automated approaches to
classification is the fact that they're completely content-centric. They
focus solely on organizing the stuff inside the folders, ignoring the
broader information ecology.
A succesful information architecture
The automated approach also ignores the valuable human interactions that
occur during the process of information architecture design. As you're
working to structure and organize a site, you inevitably ask hard
questions about business strategy and content. This process drives
towards a better definition of site goals and content policies.
In Built
to Last, James Collins and Jerry Porras explain that successful
companies make it a habit to "avoid the tyranny of the OR" and "embrace
the genius of the AND."
The key to success in designing information architecture solutions for
really large web sites and intranets is to intelligently combine manual
AND automated approaches.
It's silly for Autonomy to pretend their software eliminates "the need for
any manual labor in the process." It would be equally silly to ignore the
valuable role automated classification software can play.
Humans (preferably experienced information architects) are needed
to develop
an overall information architecture strategy,
to create key classification schemes, and to train and test the
performance of the software. Automated classification software is needed
to classify documents in large, dynamic collections for which manual
tagging of documents is impractical.
There are many gray areas in between that must be navigated based upon the
context of a particular web site or intranet. Let's examine just a few of
the dimensions that should be considered when trying to decide how to best
mix manual and automated approaches.
The Importance of Performance
However, if you're publishing a free, online database of the 20,000 best
jokes about over-hyped technologies, automated classification may be the
best way to provide topical access.
Volume & Dynamism
Content Diversity
Automated classification really breaks down on multimedia
collections. If you're trying to provide access to images, software
applications, databases, or other non-textual digital objects, human
application of metadata is still the best solution.
Did this article mess up your little blue folders?
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Morville.
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Don't Believe the Hype
Think Outside the Folder
If you don't understand the goals and strategy of the business, how can
you organize the content to further those business objectives? If you
don't understand the information needs, behaviors, and vocabularies of
your users, how can you organize content in a way that works for them?
finds points of intersection.
Embrace the Genius of the AND
Dimensions for Consideration
If you're designing a mission-critical intranet application to support
6,000 call center operators who must interact with the system to answer
questions for customers who are waiting on the phone, the value of
high-quality manual classification may outweigh the cost-savings of an
automated classification solution.
People are slow and expensive. Yahoo has roughly 1.5 million links.
Google claims an index of more than 1 billion URLs. Some sites are too
big for manual indexing (or at least for manual indexing of all content).
Some sites, particularly those featuring daily news reports, change too
quickly for people to keep up with them. Before you plan a manual
classification strategy, you need to carefully consider volume, growth,
and dynamism.
Automated classification solutions work best on large collections of full
text documents. They don't work as well on collections that vary in terms
of document type and granularity, for example, a web site that includes
long technical articles, short abstracts, and PowerPoint presentations.
End Notes
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