I could give you tons of advice, most of it specific to some
technological domain or another, but over the years I've more or less
settled on one thing that beat out all the other ;

Data models.

Once you grok data models, what they are, how they work, and all the
extended family (schemas, ontologies, persistent identification,
querying, de-duplication, layered models, LUT/transcripts, stored
procedures [and why they are evil], RDBMS vs. NoSQL vs. whatever, and
so on), everything else is miscellaneous. The way we humans use
computers as tools are all rooted in a data model at the bottom of
some program or database, and the rest of the time is spent
interacting with the data model, trying to make it do the things we
need it to do, and so on. Everything is about and around that data
model, so getting it right is a lot more important than any amount of
beautiful coding against it.

So, that's my big tip; all that technology we much about with is
really trying to work well with a data model. Your task should rather
be to understand the why, who, how, when and the thenceforth of data
models, and everything else will follow.

Now, this tip could under normal circumstances be applied to any part
of the IT industry, but it makes especially sense in the library
world. Most of the time is spent converting data between data models
(whatever > MARC > whatever), or making sense of the one (MARC21/FRBR)
or other (AACR2/RDA and that third one I can never remember the name
of, that extension rules to AACR2?) or three (LCSH/DDC). We're all
battling against the original thought and implementation of data
models, and very often you'll find better technological solutions when
you understand the underlying human efforts of ... data modeling (and
by extension, you might discover my pet peeve, how all bad software
and systems in the world comes from bad data modeling, and *not* from
bad programming [even if there's plenty of that, too])


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