RE: Some thoughts on knowledge management, content management and single sourcing

Subject: RE: Some thoughts on knowledge management, content management and single sourcing
From: SIANNON -at- VISUS -dot- JNJ -dot- com
To: "TECHWR-L" <techwr-l -at- lists -dot- raycomm -dot- com>
Date: Tue, 19 Mar 2002 15:1:15

John Posada wrote:
> What kind of knowledge management are we talkiing about here.
> 1) Making sure that everyone in a corporation who knows or learns
> something, documents it, or
> 2) Having a central repository where all information that a
> corporation produces is available. Here, I'm thinking Documentum for
> millions of gigs of research data, or Siebel where an 18,000 member
> sales force and sales support go to for all parts of the sales
> process, from lead qualification to money collection.

Actually, those are both smaller pieces of KM.

My view of the topic may be a little different from most expressed so far,
as it comes from a manufacturing perspective, not a documentation control
one. My understanding comes from participating in the early stages of
framing the requirements for such a project, a while back -- I was pulled
away by other priorities, but I found it a fascinating topic to research.

My take: "Knowledge Management" is the buzzword used to describe the
process of capturing data and turning it into information, in a manner that
produces knowledge instead of just 'static'. The scope of this may vary,
though I am familiar with it in an Enterprise-wide scope, covering data not
confined to one department or format.

Somewhat Over-Detailed Example, to show how it can be used for business
At Widgets-R-Us, a series of five manufacturing machines make three types
of widgets, which are then sold.

We start with raw data.
-- Data is collected from the machines, both on the products they are
producing, and the performance of the machine as it runs.
-- A series of manually-generated Excel spreadsheets put together by an
Inventory Control guy named Vinny show the loss from product testing and
quality control (e.g., 10 packs of widget A scrapped because the finish was
scuffed by the conveyor belt)
-- Sam the Marketing clone maintains a product database tracking market
performance of the three widgets.
-- Fred in accounting maintains sales figures on an AS400 mainframe.

We then make the data commonly-accessible (that central storage thing).
-- The machine-collected data is transformed and loaded into the Data
Warehouse from the original sources and formats they are stored in (e.g., a
DB2 dB and an InSQL dB), using business rules defined by the documented
-- Vinny's spreadsheets are transformed and loaded into the Data Warehouse,
too, using business rules to extract specific data, and then capturing a
snapshot of each report in full as a PDF to store as a blob.
-- Ditto Sam's data.
-- Ditto Fred's data.

We apply metrics to selected pieces of the data and make them accessible
from a common point of reference.
-- A manufacturing portal webpage draws data through another transformation
mechanism, which applies predefined metrics to the live data to calculate
the values of specific business-area indicators. the portal allows each use
a custom interface (like Yahoo, etc.) to see summary indicators and access
mail and reporting functions.

More flexible analysis of the information makes business decisions easier,
and helps identify problem areas to be addressed.
-- Marty the Manager looks at the portal webpage, and notices in his
high-level summary that even though widget A is in high demand (according
to the marketing indicator) sales are low. He clicks to drill down on the
accounting info. for the products, and sees that the saleable product made
for widget A is half that of widgets B and C. Clicking the Back button in
his browser, he clicks on the manufacturing indicator to drill down and
find out why less of the desirable product is being made, and sees that
there is a high rate of scrap, and a high rate of machine downtime.
Clicking to drill down on each of these, he can see from Vinny's original
reports that most of the scrap seems to come from problems with one
machine's conveyor belt, which is the same machine that spends half its
time down for that same belt. With a self-conscious twinge,
Marty vaguely remembers a purchase order for machine parts he glanced at
two weeks earlier and put off until later because he thought it was for a
new line not to be built for a couple months, yet. Backing up to his
personalized portal page, he checks his email and realizes the purchase
order was for a new belt for the problem machine. He fills it out and
submits it to purchasing on the spot.

Convoluted example, maybe, but it's the first anecdotal example I could
come up with on the spot, and the subject can be very complex, if you start
dealing with more variable types of data, more customizable forms of
output, and more complex analysis algorithms, not to mention hooking in the
documentation supporting the business rules, and any HR and training
functionality, etc.

Again, it will vary by implementation, but the concept does bear
investigation. It boils down to "know your stuff" the way orthodontia boils
down to "taking care of teeth". There's more underneath that isn't just
common sense, and derogatory comments usually show a lack of knowledge of
the subject area. As a buzzword, the term *has* been abused, but there's a
reasonable amount of good info out there. The websites forwarded by others
have been excellent.

Shauna Iannone
A human being should be able to change a diaper, plan an invasion, butcher
a hog, conn a ship, design a building, write a sonnet, balance accounts,
build a wall, set a bone, comfort the dying, take orders, give orders,
cooperate, act alone, solve equations, analyze a new problem, pitch manure,
program a computer, cook a tasty meal, fight efficiently, die gallantly.
Specialization is for insects.
-- Lazarus Long

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