Kyle Banerjee wrote:
> Starting with data modeling is like trying to learn a new spoken language
> by focusing on grammar [...]

Hmm. It seems that a lot of people are, shall we say, somewhat
misguided to what data modelling is, even mighty WikiPedia who makes
it into a formal process of sorts, and I can see it repeated ad
nauseum wherever you go, giving us the idea that it is all about the
schema of columns and the nuts and bolts of tables and relations in a
RDBMS. That's confusing data modelling tools or processes with the
generic open-ended category of data modelling.

Data modelling is simply the act of exploring data-oriented
structures. Over time I've learned that everything we do, every little
problem you battle with in your every daily life, revolves around some
data structure, the names of such, and their internal and external
relationships. The simplest web form has a model, simple and complex
applications do as well, enterprise systems, library systems, formats,
databases, documents, spreadsheets, this conversation, your bicycle,
your morning routine, *everything*.

There is, in my strong opinion, a horrible conflation of the concept
of modelling data and implementations pinning down data types; it's an
evil so strong it blinds us, cripple us, and I feel like screaming out
in terrifying agony the horrors within! The wrongly applied indeces!
The labels on columns! The semantic binding of one sub-structure to
another! The optimising tricks used! The stored procedures! The
conceptual semantics of labels in n-ary graphs!!

The wretched *name* of a single field and how it quietly eats up any
disambiguous notion we put in place, through the many well-meaning but
afflicting layers of abstraction and implementation, it drives me
insane! "Name"!? What does that mean in the context of an email
address? What does "comment" mean when it reaches my ORB? What were
they thinking when the model designed resulted in SQL statements 1K

There's so much information written of the topic of data modelling,
and most of it ignore that very thing that it should embrace and focus
heavily on; good semantic design. (Granted, it has become far more
focused on in the last 10 years, and I'm extremely happy for that) Put
some heavy thought into your tables, because what you perceive as a
simple table of users becomes an overwhelming problem when you add
special users to the system. Have any of you ever created an ILS with
a table "book" in it? (C'mon, raise your hand, I know you have!) Yeah,
that's the sort of evil I'm talking about! Libraries don't deal with
"books", they deal with bibliographic meta data of objects, and
sometimes those objects are called a "book" which has certain
constraints and properties that link to special meta data that isn't
static. Version 1.0 of any system if famously rubbish because of the
learning process of getting all this stuff wrong. Version 2.0 is
famous for being overly abstracted and incomprehensible. Version 3.0
is getting there, but you're bogged down in the middleware,
translating between good but incompatible models. By the time you get
to version 4.0 you realize that the underlying concepts which drove
versions 1 through 3 are flawed, and you need to work in terms of FRBR
sub-graphs instead of MARC records. Version 5.0 is so re-written and
re-conceptualized, you decide to call it something else, version 1.0
And we repeat the cycle. If your software isn't like this, consider
yourself lucky (or at worst, self-deluded :).

> Data modeling is extremely useful, but
> mistaking drips and drabs of it early on for reality can poison your
> thinking.

Sorry, you got that back to front. We all agree that understanding
what user want and / or need is King, but unless you've got that
understanding of not only what the users want but how systems can
deliver this without creating constraints that will screw things up
when you extend that original delivery idea, you're going to suffer.

It's easy; take great care to what you call things in your system (no
matter whether it's in the database, your objects / classes /
instances / interfaces, user interface, buttons, messages, windows,
data types, loops ... they're all data models that need to be as
cooperative as possible, speaking the *same language*, to be
compatible in the meaning they give the concepts used. If your Wheels
API has different semantics from your Steering API, making that car is
going to be a really crappy experience, for you as a developer, for
testers, for maintenance guys, for service people, and most of all
don't think for a second that the driver won't notice. These semantics
are far more important than what our industry traditionally have given
them, and in my opinion it is our biggest flaw.

Trust me, I've stared at data models up and down so many systems over
the years (10 of them in a high-flying big consultant agency where we
came in when projects otherwise failed) it's amazing I'm still sane.
But just like in the Cthulhu myth, little by little I feel my sanity
waning from staring at all those hundreds of assumed harmless semantic
differences and oddities in them, the little things that at first
looks like it possibly wouldn't cause any kerfuffle. Those are always
exactly the little gremlins that cause big hickups further down the
line. None of these little engulfing little smiles of hell should be
kept alive. Just like two little cute rabbits in a meadow is adorable
at first, you curse yourself for bringing in the rabbits to this land
where they have no natural predators, and they cause an expensive and
horrible mess that sometimes are simply impossible to clean up. All
this from just two fluffy rabbits.

Well, I say; don't trust the rabbits! They're all evil rabbits of
Caerbannog! Kill them, kill them now, with grenades if needs be ...


Where was I? Ah yes, evil little gremlins. You think them innocent at
first, but they will destroy your otherwise carefully crafted systems,
make you cry and question your worth. Don't let this happen to you!
Don't think that pure programming can even fix this problem, because
it's a very human problem. It's human abstract thinking that fails,
and pollutes digital logical systems because something wasn't thought
out well.

So, young Padawan, may I suggest you understand this before you leap
forth in haste, armed with information and programming rather than
knowledge and understanding of semantics?

> The most important question to understand is why because it drives
> everything else.

No argument there. For example, why are we having this conversation?  ;)


 Project Wrangler, SOA, Information Alchemist, UX, RESTafarian, Topic Maps
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