When I wrote about
the Baker-Robbins/LegalWorks KM conference, I purposely
left out the most impressive application/presentation
of them all: Morrison & Foerster’s Oz Benamram
discussing "AnswerBase," the
firm’s new KM system which will be rolling out next month.
AnswerBase is the fruit of over two years
of labor, and is, in my humble opinion, a revolutionary
approach to KM. Oz was kind enough to give me a
one-on-one guided tour in his office two weeks ago, and
what I have to say will draw from both his presentation
to the KM conference and to our private meeting. Suffice
to say that neither Oz nor I am aware of any other firm
taking the Morrison & Foerster approach at the moment,
but when I asked Oz who else might adopt it once they
see it, his response was "Everyone will, within two years."
Read on.
At the outset of their redesign of
the Morrison & Foerster KM system starting two years
ago, Oz and the team went back to first principles. These
were their guiding stars:
- We need "federated search" to search across the many
disparate databases which all contain information potentially
germane to a lawyer confronting a new task, including:- the matter tracking/management system;
- the client/CRM databases;
- the financial/accounting/time-keeping and billing
databases; - personnel information on individuals within the
firm; - the document management system;
- the email database; and last but not least
- the firm’s own internal "Knowledge Exchange"
system, a continuously-upgraded and dynamic compilation
of (manually managed) model documents and templates.
- To use precedents effectively, attorneys need context:
who worked on the transaction, what industry was it
for, the timing, etc. - Often the most expeditious way to gain expertise
is…by talking to an expert: This implies that
the system must excel at identifying people who
have worked on similar matters in the past, and preferably
a lot of them. - Finally, lawyers won’t use anything that’s not drop-dead simple. Extremely
comprehensive and nuanced search tools may be fine
for grad students, but lawyers want something resembling
Google or Yahoo.
Perhaps not surprisingly, when they went out into the
marketplace of "federated search" vendors to evaluate
products, they ran into the realization that while everyone
could do 80% (sometimes a different 80%) of what they
were seeking, no one could do it all. Products
that excelled at extracting meta-data to identify entities
to a transaction, for example, fell down on their relevance-ranking
engines, so that the "best" documents did not always
appear at or near the top. Similarly, products
that were strong on identifying individuals with relevant
experience mis-categorized documents.
At this point, the team was in a bit of a quandary—until
Oz happened to attend an "enterprise search" technology
conference where some e-commerce vendors were making
presentations.
When you or I think of e-commerce, we tend to think
of Wal-Mart, Home Depot, Barnes & Noble, not the AmLaw
50.
But Oz’s insight was that e-commerce platforms have
several built-in capabilities that more conventional
engines used to power legal KM systems may lack:
- they are "scalable" beyond belief;
- they make allowance for misspellings, imprecise
phraseology, etc.; - at least with the best-of-breed, they avoid the classic
search failure mode I refer to as "all or nothing"—where
the answer to your search is either "Search returned
no results" or "Showing 1-10 of 2,409" - they "hate" to come up empty-handed, so are configured
to provide near misses and close neighbors rather than
"Try Again." (For example, if you were
searching for a 2005 black Honda Accord coupe with
a 6-speed manual, and there were none in stock, it
might return a 2004 fitting those specs, or a four-door
sedan, or a red one, and ask you which criteria were
most important to you so it could re-order and refine
the results.) - perhaps most compellingly, they come ready-made
with the ability to conduct "faceted search," a
term perhaps more readily understood by example than
strict definition. "Faceted search" simply
means the ability to categorize the answer set of a
search by relevant characteristics. Endeca,
a leading vendor in this area, with clients including
Barnes & Noble, Boston Scientific, Circuit City,
CompUSA, Home Depot, IBM, the Library of Congress,
NASA, Patagonia, Putnam Investments, and Wal-Mart,
provides this example after one has searched for "Lego’s" at
eToys:
Although difficult to make out, you can
see that of the "172 results" returned, it invites you
to recategorize them (left-hand column) by Age, Gender,
Price, Category, Character, etc. In law-firm-land,
the equivalent is offering to recategorize the results
of a KM search by client, industry, type of transaction,
jurisdiction, office where it was managed, responsible
attorneys, date, or even the identity of the law firm
on the other side.
Even given the inspiration of Endeca and the e-commerce model, Oz and his team ultimately settled on the proven platform provided by Recommind, which has worked with such name-brand firms as Cleary-Gottlieb, Cooley Godward, DLA Piper, Paul-Hastings, and Shearman & Sterling.
Finally, the Morrison & Foerster system
obviously "knows who you are" when you’re conducting
a search, and adjusts its relevancy rankings accordingly,
giving greater prominence to matters arising in your
office or your department, or for clients you’ve worked
for. Moreover, it knows how much you’ve worked on similar matters (say, an antitrust deal) and if you’re new, or rusty, it will put training videos higher up in the search-return results.
If Oz is even one-quarter right that "everyone
will be doing this in two years," KM professionals have
a busy 2006-2007 in front of them.
