Where Business Concepts, Enter=
prise
Integration, and Technology Converge - A Taxonomy Framework
Copyright 2002-2004 Gwen Thomas
Abstract:
The success of Enterprise
Integration projects relies upon a clear understanding of terms and their
relationships. Taxonomies are classification schemes for things: concepts,
objects, actions, and events. They are built using controlled vocabulary se=
ts.
The result is a single source of the truth and an unambiguous starting point
for communications between groups or individuals who typically use different
terms to describe the same or similar things. Most business architectures
require multiple taxonomies. Initial iterations should start small, leverage
existing efforts, and be built manually. Once they become large enough,
taxonomies benefit from specialized tools to mine documents for taxonomy te=
rms
and to classify documents according to their content.
In an enterprise that is organized, managed, and ana=
lyzed
through functions, projects, efforts, and timelines, it is easy to forget
essential truths that apply to every person in every position across the
enterprise. Several of these essential truths concern how we think, speak,
organize information, and search for information.
Names for things
Ø
If we don’t have a name for an object,
concept, action, or event, it’s very difficult for us to manage it.=
p>
Ø
If our name for a thing doesn’t match =
the
name used by someone else for that thing, we’re at risk for not takin=
g others’
things into consideration when dealing with our things.
Ø
Because we use our own names for things more
often than others’, we’re more likely to forget theirs, rank th=
em a
lower priority, or consider them unrelated to what we’re doing.
Ø
If we know that multiple names for things ex=
ist,
and if it’s easy to see what those names are, and if we believe
it’s important to do so, we’re more likely to take the other
person’s thing into consideration when dealing with our thing.
Organizing things<=
/b>
Ø
Everyone has an internal classification sche=
me
for their things that - in the absence of other instructions - =
they
will use to think about and analyze their things.
Ø
If our classification scheme doesn’t m=
atch
someone else’s, we’re at risk for not taking their schemes into
consideration.
Ø
Because we use our own organizational schemes
more often than others’, we’re more likely to forget that theirs
exist, forget that theirs may be more important in certain situations than =
our,
rank theirs as less applicable to any given situation, or consider them
unrelated to what we’re doing.
Ø
If we know that multiple schemes exist, and =
if
it’s easy to see them, and if we believe its important to do so, we=
8217;re
more likely to take the other person’s scheme into consideration when
working with ours.
Where Business Concepts and Technology Converge
Because of the above truths, the natural state of an
enterprise is to have significant disconnects. And so, an enterprise must m=
ake
a concerted effort to align the way its people speak about, organize, search
for, and analyze information. The enterprise must support individuals’
and groups’ abilities to adopt work styles, practices, terms, and
organization schemes that will make them the most successful at accomplishi=
ng
their particular strategic and tactical objectives. However, the enterprise
must also provide a mechanism for translating, aligning, and prioritizing
disconnected information when it is important to speak about, organize, sea=
rch
for, or analyze information from an enterprise perspective.
This becomes especially important when an organizati=
on
moves a function - such as organizing a set of documents - from=
a
manual process to an automated process.
A human is capable of having an Aha! moment, in wh=
ich
they make a connection and perform a course correction in the execution of
their task to extend their results to account for the connection. Indeed, t=
his
ability to have frequent and valuable - but typically unheralded R=
11; Aha! moments is part of the value brough=
t to
organizations by their best Technical Writers, Customer Service Reps, QA
Analysts, and Trainers.
What happens when information organization or retrie=
val is
automated? SQL queries don’t have Aha!
moments. They execute the same way every time. And if an information retrie=
val
mechanism is set up to deliver only that information that fits a specific s=
et
of criteria - without considering synonyms, preferred terms, informat=
ion
hierarchies, and other options considered by every human being as they ment=
ally
formulate or refine a search - then the automated function may be mor=
e of
a disservice than a service to its users.
Even worse, most automated technology hides the step=
s it
employs from the user. This deprives the user of a mechanism for evaluating=
the
results against the process and to determine whether unexpected results are=
the
result of missing content or an inadequate process. This lack of transparen=
cy
can lead to lack of user confidence in the technology and therefore a refus=
al
to use the technology.
Inadequate information retrieval technology choices =
or
implementations can, therefore, result in user rejection of a valuable and =
expensive
set of content.
Enter Enterprise
Integration
Enterprise Integration projects are typically though=
t of
in terms of technology and implementation challenges. They are often architected and
implemented by individuals highly skilled and experienced in overcoming
technology hurdles. They may be managed by individuals highly skilled and
experienced in boundary-spanning project management.
However, training for these positions typically does=
not
include an emphasis on words: how people think, how they articulate their
thoughts, the differences between different modes of spoken and written
communication, the way the brain deals with terms that express similar or
related concepts. These conce=
pts,
on the other hand, are core to the work performed by “word” peo=
ple
who work with individual content units - writers, editors, indexers,
trainers, search engine optimizers.
Another skill needed by Enterprise Integrators is the
ability to think about language sets as systems. Training for technical Ent=
erprise
Integration positions does generally include system thinking and working wi=
th
patterns. However, these systems and patterns are more likely to revolve ar=
ound
code and technical architectures than language. The way chunks of content a=
re
organized, maintained, and cross-referenced differs in significant ways, and
requires input from those who work with content sets from a conceptual
viewpoint: information architects, documentation managers, indexers,
librarians, knowledge base designers, knowledge managers, and meta data
managers.
The Role of Taxonomies
Taxonomy
(from Greek taxis meaning arrangement or division and nom=
os
meaning law) is the science of classification according to a pre-determined
system, with the resulting catalog used to provide a conceptual framework f=
or
discussion, analysis, or information retrieval. In theory, the development =
of a
good taxonomy takes into account the importance of separating elements of a
group (taxon) into subgroups (taxa) that are mutually exclusive, unambiguou=
s,
and taken together, include all possibilities. In practice, a good taxonomy
should be simple, easy to remember, and easy to use.
Taxonomy definition from whatis.com
Understanding Taxonomies
Taxonomies are classification schemes for things:
concepts, objects, actions, and events. They are often hierarchical, but th=
ey
don’t need to be. Successful taxonomies work with controlled vocabula=
ry sets.
The result is a single source of the truth and an unambiguous starting point
for communications between groups or individuals who typically use different
terms to describe the same or similar things.
A good taxonomy can also improve decision-making. It=
can
frame the way people make decisions, and also help them find the informatio=
n they
need to weigh all the alternatives. With a=
good taxonomy, decision makers enjoy a new perspective, "drill
down" to get details from each, and explore lateral relationships among
them.
Imagine this scenario described from several perspec=
tives.
Patient perspective
|
Physician perspective
|
IT System perspective
|
A
person is sick. The person makes an appointment, sees a doctor, gets some medicine, and pays a co-pay.=
|
A
patient calls the appointment line and is
scheduled for an appointment.<=
/u>
Upon arrival, front office sta=
ff
check in the patient, =
and the responsible party guarantees payment for a =
standard
visit and makes a co-p=
ay,
using a check. A nurse works up the patient, and the physician examines the patient, makes a diagnosis, and writes a prescription. Back office staff for the=
practice process the visit, add the check to the night’=
s deposit, and submit an insurance claim.
|
An
appointment is entered=
into
the Appointment System=
. A diagnosis is entered into=
the patient record and the insurance claim. A prescription is entered i=
nto
the patient record and=
the insurance claim and is la=
ter
extracted for prescription rep=
orts.
An insurance claim is =
filed
in the insurance system, and
a co-payment and amount due are entered in=
to the
general ledger system.=
A co-payment is added into =
the nightly deposit record=
.
|
<=
/p>
How might this info=
rmation
be classified according to each perspective?
Patient perspective
|
Physician perspective
|
IT System perspective
|
Person
&nb=
sp; Roles
&nb=
sp; Patient
&nb=
sp; Responsible
Party
&nb=
sp; Insurance
policy holder
&nb=
sp; Health
&nb=
sp; Well=
&nb=
sp; Injured
&nb=
sp; Sick=
&nb=
sp; =
Severity
of illness
&nb=
sp; =
Slight<=
o:p>
&nb=
sp; =
Moderat=
e
&nb=
sp; =
Severe<=
o:p>
&nb=
sp; =
Responses
&nb=
sp; =
Ignore<=
o:p>
&nb=
sp; =
Self-tr=
eat
&nb=
sp; =
Visit
doctor
&nb=
sp; =
Visit
hospital
&nb=
sp; =
Call
ambulance
=
|
Office procedures
&nb=
sp; Front
office
&nb=
sp; Appointment=
&nb=
sp; =
Schedule
&nb=
sp; =
Check-in
 =
; =
Update
phone #
&nb=
sp; =
Receive
payment
 =
; =
guarantee
&nb=
sp; =
Log
in co-pay
&nb=
sp; Medical
&nb=
sp; Nurse
&nb=
sp; =
Work
up patient
&nb=
sp; =
Assist
physician
&nb=
sp; Physician=
span>
&nb=
sp; =
Diagnose
&nb=
sp; =
Prescribe
&nb=
sp; Back
office
&nb=
sp; Process
co-pay
&nb=
sp; Update
patient rec.
&nb=
sp; Update
general ledger
&nb=
sp; Process
nightly
&nb=
sp; deposit
<=
o:p>
|
IT Applications=
Appointment
system
&nb=
sp; Create
module
&nb=
sp; Check-in
module
&nb=
sp; Patient
system
&nb=
sp; Contact
module
&nb=
sp; Responsible
party
 =
; module
&nb=
sp; Insurance
module
&nb=
sp; General
ledger system
&nb=
sp; Receivables
module
&nb=
sp; =
Co-payments
 =
; =
function=
&nb=
sp; =
Insurance
payments
&nb=
sp; =
function=
&nb=
sp; Deposits
module
|
To successfully build, integrate, or support the IT
systems in this example, IT must clearly understand the data that the syste=
ms
will process as well as the business processes they will support. For examp=
le:
·=
; =
The data architect must unequivocally unders=
tand
the difference between patient=
u>,
responsible party, and insurance policy holder. Th=
ese
are all types of people, but it would be poor practice to assign the name person to all the data fiel=
ds
that store this data. Such a
practice would inevitably lead to confusion, mistakes in data sourcing, and
mistakes in data integration.
·=
; =
It would be poor interface design to use the
label person for each of=
these
fields when they appear within applications. Such a practice would probably
lead to confusion and data entry mistakes, with errors being propagated thr=
ough
data records.
·=
; =
It would be poor form design to use the labe=
l person for each of these fields when they =
appear
on patient forms. Such a practice would lead to poor productivity, since fr=
ont
office staff would need to perform quality checks on forms and correct erra=
nt
data. It would also lead to degraded customer satisfaction, since a signifi=
cant
percentage of patients could be expected to experience frustration.
Aligning Multiple Taxonomies
In the example above, communication between patients=
and
their physicians does not require patients to adopt system terminology or t=
o be
burdened with categories of information that do not concern them but do con=
cern
the physicians’ staff. It is good practice to present information to
different user groups using the terminology that they are familiar with. Mo=
st
business architectures require multiple taxonomies.
However, employing multiple taxonomies within an
enterprise requires that data and application architecture groups must
accommodate these taxonomies. Processes must enforce mapping of local or specialized taxonomies to each o=
ther
and to the appropriate terms in a master thesaurus, or controlled vocabular=
y list.
Benefits Of Aligned Taxonomies
Once classification schemes and controlled vocabular=
y lists
are in place, IT and the busi=
ness
should realize the following benefits:
·
Less ambiguous communications
·
Increased ability to map data to business
entities
·
Increased ability to conceptualize business
processes
·
Greater alignment between process naming and
data naming standards
·
Easier migration to Services Oriented
Architecture
·
More consistent Business Process Mapping
·
Easier migration to Rational Unified Process=
·
Higher data quality
·
Users can more easily and more quickly find
information they need
·
Supports inventory and monitoring efforts to
knowledge resources.
Deciding Scope for Your First Set of Aligned Taxonomies
The first iteration of your set of aligned taxonomies
should have a modest scope. It should encompass limited set of data about the data,
concentrating on a few types of concepts and objects as described by a few
groups. The goal should be compiling a working taxonomy set in just a few
weeks. After this is made available to the enterprise and is in use, it sho=
uld
become apparent how to best add to the taxonomy set in future iterations.=
p>
In establishing scope for iterations of the taxonomy=
set,
consider the following characteristics of the language sets and classificat=
ion
schemes you’ll be collecting:
·
Value to the business
·
Complexity
·
Knowledge required to understand underlying
conceptual models
·
Size
·
Availability of documentation (glossaries,
indexes, data dictionaries, etc.)
·
Tagging Approach
·
Usage
Leveraging Existing Efforts
Often, the enterprise will have already addressed
taxonomies, although the efforts may not have been labeled as such. It is
possible to jumpstart a taxonomy collection by identifying and leveraging t=
hese
efforts.
Leveraging Documentation and Training Efforts
Look for system documentation, glossaries, and
back-of-the-book indexes. Ask if staff members have notes that have not been
published, but help them do their jobs. Ask for XML DTDs and keyword lists.
Don’t overlook training vocabulary lists.
Leveraging Meta Data Management Effo=
rts
Meta data managem=
ent
programs generally include logical descriptions of data. It is good practice
for the naming standards and documentation standards for these programs to =
call
for using terms from the single source of the truth. However, most knowledge
workers in the data department will also use work products: term definition=
s in
embedded meta data, case tools, data dictionaries, etc.
Leveraging Web Site Authoring and Optimization Efforts
One key to successful web marketing is an integrated
taxonomy that encompasses categories, keywords, and hierarchical structures
required for multiple business functions. While it is possible to focus onl=
y on
the taxonomy required to navigate the site, such an approach invariably lea=
ds
to higher costs and lower results, as the company discovers disconnects bet=
ween
the terms used to index and market the site and those used to describe data
that is collected, inferred, and analyzed to better understand customers and
sell to them.
Ø
For instance, users need to understand the r=
ight
words and organizational schemes to:
·
Navigate the site
They’ll use Page titles, Headings, Navigation bars, links, Site Maps,
Topic trees, Menu items, and Buttons.
·
Retrieve information once in the site
They’ll use find functions, knowledge bases, FAQ topic schemes. When =
they
use internal search engines, they’ll also be able to take advantage of
ALT text, meta tags, and page/document metadata, if the site has been tagge=
d.
·
Find the site (and content in the site)
They’ll use search engines and directories, which utilize both visible
text and metatags visible only to the search engine.
Ø
If the business wants to employ suggestive
selling through personalization of the site, they need to:
·
Develop category/keyword master trees
·
Develop and implement consistent personaliza=
tion
categories for:
·
page function
·
page content
·
relationship to marketing campaign
·
visitor information
·
Associate pages with consistent personalizat=
ion
keywords from the controlled vocabulary list:
·
page function
·
page content
·
sales/product
Ø
If the business wants to use the site to
reinforce marketing campaigns, they need to:
·
Align language from the marketing campaign w=
ith
language used on the site
·
Modify page-level metatags to support tracki=
ng
of the campaign
Ø
If the business wants to collect information
about users’ browsing habits, they need to:
·
Align language from the clickstream records =
to
language describing the pages visited
·
Align clickstream language with user informa=
tion
available from other sources:
·
Volunteered data
·
Non-volunteered data
·
Purchased data
Managing Taxonomies Strategically
Managing taxonomies strategically requires the manag=
ing
group to avoid certain temptations and apply best practices that are someti=
mes counter-intuitive.
Ø
Temptation: =
It
may be tempting to assemble a single, master taxonomy.
Ø
Best Practice: The group should collect specialized taxonomies from across the
enterprise. They may find that one taxonomy is used by so many groups that =
it
is a defacto standard. However, this does not diminish the need for -=
or
the value of - other specialized taxonomies. Collected, these form the
taxonomy set that will be managed.
Ø
Temptation: =
It
may be tempting to think of a taxonomy as a single entity
Ø
Best Practice: Any
taxonomy consists of a set of terms describing concepts and objects, the
classification scheme used to organize those terms, and references to how a=
nd
where those terms are used. An aligned taxonomy set also includes
an
enterprise thesaurus that compiles the terms used in specialized taxonomies=
.
Ø
Temptation:&=
nbsp; The
group may be tempted to thoroughly investigate and document enterprise
conceptual models prior to beginning to collect vocabulary sets and special=
ized
classification schemes.
Ø
Best Practice: Collect
and document the conceptual models and specialized controlled vocabulary se=
ts
from various groups, and use those to iteratively revise the aligned taxono=
my set.
Ø
Temptation:&=
nbsp; It
is tempting to try to create a textbook taxonomy.
Ø
Best Practice: Remember
the purpose of the taxonomies you build: the business problems they’re
trying to address, the problems they’re trying to avoid or minimize, =
and
the habits or disciplines they’re trying to support. Value serving the
needs of real users over building what looks like an
‘”ideal’” taxonomy.
Ø
Temptation:&=
nbsp; It
is tempting to build a broad and deep taxonomy quickly.
Ø
Best Practice: Adopt
a scope that will allow a quick first iteration. Focus on categories of
information that customers use and the places they look for information. Be
sure to include layman's term=
s as
well as technical terms, to develop the thesaurus for cross references as y=
ou
go, and to include documents, people, and informal communications as source=
s.
Ø
Temptation:&=
nbsp; It
is tempting to set a goal of establishing a dominant, common mental model.<=
/p>
Ø
Best Practice: Again,
remember to think globally but work locally. It is both natural and best
practice that knowledge workers’ predominant mental models will be ba=
sed
on the terms and classification schemes they use frequently. An aligned tax=
onomy
set allows for smoother, more complete, and more consistent translation of
ideas and concepts, as well as a resource to be used when thinking outside =
of a
default mental model.
Taxonomy Development Teams
Taxonomy development in the corporate environment is=
a multi-disciplinary
activity that spans boundaries and crosses organization lines. The typical =
centralized
taxonomy team consists of representatives from the following information
specialties:
Ø
Information technology
·
Data architecture and management
·
Records/document management
·
Content management
·
Meta data
management
Ø
Library or information center (whatever group
organizes and indexes large information sets)
Ø
Web site management
Ø
Corporate communications
Ø
Documentation specialists: writers, editors,=
and
publishers.
Within this group, it is important that representati=
ves
from the last four categories - those whose day-to-day work deals with
words, content creation, information organization, and information retrieval
- maintain the prominent voiced when determining the focus, goals, and
processes for the team. Technology’s role should be to support goals,=
not
to set them.
As this centralized group moves beyond early iterati=
ons,
they will naturally uncover specialized vocabulary sets and taxonomies in u=
se
in additional groups across the enterprise. They will need to plan to expand
their efforts to seek input from - and to provide input to - ot=
her
functions:
Ø
Product Development, which develops new term=
s as
it plans for new products, services, and uses for them.
Ø
Marketing, which specifies terms used in
specific marketing campaigns and also determines terms that are favored, te=
rms
to be avoided, terms and taxonomies used by competitors and industry analys=
ts.
Ø
Sales, which uncovers terms that are success=
ful
in selling products and services, and also uncovers terms and taxonomies us=
ed
by customers to describe desired products, services, attributes, concepts, =
and
activities.
Ø
Customer Service, which uncovers terms used =
by
users who have been unsuccessful finding the information they need or who n=
eed
assistance beyond what has been presented to them by the organization. Cust=
omer
Service also uncovers terms used by customers for the problems they encount=
er
and the tasks they perform.
Ø
Training, which often brings a unique
perspective to how different classes of users think, talk, and find
information.
Ø
Search Engine Optimization specialists, who =
have
access to specialized tools to understand how users search for and navigate
through online content. They also should be aware of how tagging, labeling,=
and
naming choices affect competitive ranking of web pages for major search
engines.
Ø
Business Intelligence, which needs to unders=
tand
the relationships between related terms - and the differences between
similar terms - so they can provide the right sets of information for
analysis and reporting.
Ø
Data Architecture and Application Architectu=
re,
which need to ensure that their models encompass the enterprise’s true
needs.
Ø
Governance and Compliance, which need to ens=
ure
that reporting and controls across the enterprise compare apples to apples,=
and
oranges to oranges.
Sources to Mine for Taxonomies
Controlled vocabulary sets - specialized lists=
of
terms and their synonyms - can be compiled from typical project
documents.
Ø
Glossaries
Ø
Back-of-the-book indexes
Ø
FAQs
Ø
Knowledge base entries
Ø
Keyword lists
Ø
Pick lists
Ø
System documentation
Ø
Data dictionaries
Ø
Training materials
Ø
Meta data
embedded in case tools and business intelligence applications
Ø
Library card catalogs
Ø
Bibliographies
Ø
Product lists
Ø
File systems
Ø
LAN directory structures
&nbs=
p;
Taxonomy Applications
Taxonomy structures can be used in a variety of
applications. These applications may be standalone or embedded in other
applications. Either way, they can help:
Ø
Developers
·
web developers, as they create navigation
schemes to help visitors search for information or navigate through informa=
tion
·
search engine technicians, as they optimize =
the
site for searches and configure the search engine to look for synonyms and
related terms
·
IT workers, as they build filtering tools, e=
mail
alerts, or other keyword-based utilities
Ø
Users
·
researchers, as they find source materials&n=
bsp;
·
readers, as they locate information in a
book or electronic content
·
buyers, as they locate products and
services
·
decision makers, as they locate sources of
expertise
Ø
Analysts
·
executive decision makers, as they review and
analyze corporate data
·
compliance officers, as they review breadth =
and
depth of their efforts
·
functional managers, as they plan for
taxonomy-related efforts.
Taxonomies and Technology
A taxonomy does not require technology.=
It
is, at its core, a mental model captured so that it can be shared. It is be=
st
practice that proofs of concept and early iterations eschew specialized
technology, instead capturing information in easy, accessible document form=
ats.
However, once baseline taxonomies are in place, the
enterprise might to mine their content to uncover additional terms and conc=
epts
to fill out the taxonomy. They might also want to move taxonomy information=
in
the opposite direction, automatically reading content, classifying it, and
assigning keywords from the approved taxonomy.
Vendors offer an array of taxonomy software and rela=
ted
applications to address these needs. These tools promise many benefits,
especially to enterprise customers. They offer boons to content management
efficiencies, portal usability, information retrieval, and knowledge discov=
ery.
They employ automatic categorization, tagging, keyword extraction and analy=
sis,
and workflow.
It’s important to remember, however, that taxo=
nomy
products represent emerging technologies. And they don’t eliminate the
need for human input, since even the most automated systems require some ma=
nual
assistance from people who know how to classify content. They are not
out-of-the-box solutions, and should be considered as automation tools that
greatly enhance the output of humans.
Also, most technologies are expensive, with license =
and
implementation costs running in the hundreds of thousands of dollars. Best
practice is to prove the value of taxonomies in your environment and culture
before considering expensive technology investments.
Once this value is proven and carefully crafted prot=
otypes
have been customized to your environment and culture, then consider employi=
ng
auto-classification tools to rapidly increase the breadth of your taxonomy
efforts. Consider, also, data visualization tools such as the Brain, which
helps people visualize where information resides across the organization.=
p>
Security, Access, and Governance
Just as with any centralized repository of enterprise
information, a taxonomy repository will require policies and procedures to
oversee security for its contents, to ensure appropriate access only by
authorized users, and to provide careful governance and stewardship to ensu=
re
that content is appropriate, current, and accurate.
When planning a taxonomy program, remember that sing=
le
sources of the truth offer great benefits, but also increased risk: It now
becomes easier to make mistakes faster, more efficiently, and more
consistently.
Plan for standards and oversight for areas such as:<=
/p>
·
Scope of contents
·
Formats of content
·
Acceptance of material into the repository=
p>
·
Change control
·
Scheduling
·
Decision-making
·
Issue resolution
·
Monitoring / input by user groups.
When deciding what approach to take for managing this
program, carefully consider standards and practices that have been successf=
ul
in your organization for similar repositories and knowledge bases.
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