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Subject: CIQ TC WELCOMES JOHN D. PUTMAN OF FEDEX AS A NEW MEMBER
- From: "Ram Kumar" <RKumar@msi.com.au>
- To: <ciq@lists.oasis-open.org>
- Date: Thu, 16 Dec 2004 14:18:21 +1100
CIQ
TC,
John D. Putman of FEDEX is interested to contribute
to the TC.
John is having some problems with the Kavi setup
and will be on
our mailing list soon. I have copied this email to
him.
Please find below John's expertise.
Welcome John and the CIQ TC looks forward to your
contributions!
Regards,
Ram
------------------------
From: "John D. Putman"
<jdputman@scanningtech.fedex.com>
To: "'ciq@lists.oasis-open.org'"
<ciq@lists.oasis-open.org>
Subject: Hello
Date: Tue, 14 Dec 2004
18:21:30 -0600
I have just completed getting into this CIQ / on the
mailing list and would
like to introduce myself to the committee -
My
main experience with names and addresses has been in my current capacity
as
"the address expert" for Federal Express ("FedEx"), starting around
1993.
Throughout my IT carrier, however, I have had some intermittent
or
peripheral name and address experience elsewhere; for instance
-
+ at a bank where names
and addresses were "sacrosanct" identifiers
for account
holders;
+ at St. Jude
Children's Research Hospital, where patient AND
parent names AND addresses
were critical to identifying and keeping up with
clinical trials enrollees
(children were often enrolled - at least serially
- in several clinical
research trials and so needed to be cross-referenced
using those data
elements);
+ I also like to
think that a couple of stints as a DBA (for
network topography and mil-spec
equipment assemblage modeling, and then for
clinical trials data at St.
Jude), helped prepare me somewhat for complex
data modeling - address
definitely being complex.
At FedEx, I have been involved in the
implementation of standardized address
correction APIs for USA, Canada, and
International. That included not only
vendor product evaluation,
familiarization / implementation, and
maintenance, but also custom design for
"special features" such as
+
possible address production when postal quality matching
fails,
+ character-level
standardization change detection and
indication
setting,
+
dropped data sensing and preservation /
reinstatement,
+ address
element input order reinstatement after correction
/
standardization,
+
address quality and confidence codes
generation,
+ and a variety
of custom controls to affect address correction
behavior and/or output
format;
+ in addition, I
designed and helped perform "address audits" over
our 10-14MM account
addresses to detect "false positive" matches.
Practically all of these
required becoming very involved in address element
identification and
parsing.
At FedEx, address correction is used not only for FedEx shipment
addresses
but also for postal invoice mailing, employee and customer
communications,
and vendor / supplier information. Indeed, "address"
permeates almost all
of our systems (a study during the Y2K effort found that
address elements
were 4 times more prevalent in FedEx systems than date
elements!). Perhaps
some of the more esoteric address uses to which I
have been exposed or in
which I have been involved include
-
+ inbound shipment sensing
and notification based on name and
address for the purpose of recipient
manifests,
+ in-transit
rerouting of misrouted packages (misrouted due to
"bad"
addresses),
+ location,
shipment network node, and service area identification
based on address
and/or postal code,
+ and
courier route planning and optimization.
I am most familiar with USA
address data / parsing and correction processing
and its intricacies (of
which there are quite a few even though USPS has
made USA addresses VERY
"standard"). Though I have somewhat less experience
with Canadian
addressing, it is very like that for the USA and so I think I
have a fair
handle on that. As for International addressing, I have worked
with
that for some time (starting around 1999), but - due to the scope and
breadth
of that (along with the only recent availability of fairly good
reference
data) - I feel I have a long way to go there.
My academic background is
in Philosophy and ancient Greek (the latter of
which, with little use, I have
almost totally forgotten), with a Math minor.
My interest / emphasis in
Philosophy gravitated toward philosophy of
science, symbolic logic, and set
theory, which somewhat prepared me for a
career in IT in general and data
modeling in particular.
I look forward to working with the
committee,
David Putman
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