Take a look at the subjects of data architecture and data modelling today. What do you find? A big mess. Some people like data modelling. Some people don’t like data modelling. Some people think that data architecture only includes structured data. Some people include both structured data and text data in data architecture. Some people think that a data model is an ERD. Other people include ontologies and taxonomies. Some people don’t think a data model is needed at all.

In a word, the subjects of data management, data architecture and data modelling is a world of chaos today. Many opinions, many theories. Much confusion. A cacophony of thoughts, ideas, and no real consensus.

So how did all of this come about?

Once upon a time data management was a simple thing to do. You had a master file. You had a data layout. And you had records on the file. Easy peasy.

Then things started to get messy. Data bases. Transaction processing. Redundant data. Incomplete data sets. Same data different name. Different data same name. Naming standards run amok. Inability to look across the enterprise.

Further confusing things were vendors who kept coming up with the silver bullet. “Just buy my (fill in the blank) and your problems are solved.” But the problems grew worse and were never solved.

So how has data management progressed throughout all of this? There were no textbooks – no roadmap -as to how to manage data when the evolution was occurring. Progress was made the painful way – by trial and error.

Error – Case technology (does anyone even remember CASE technology)

  Error – don’t use GOTO statements and programming problems go away

Error – application development without programmers (I never understood that one)

Error – dashboards as the key to analytic processing. All you need is a dashboard.

Error – Big Data – Hadoop. With Hadoop, you don’t need a data warehouse

Error – data science as the magic key that unlocks secrets.

Just hire an expensive academic data scientist and watch your problems disappear

  Error – data dictionary. A good idea, a terrible implementation.

Success – database management systems

Success – transaction processing systems

 Success – Internet

 Success – the personal computer

 Success – the spreadsheet

In a word, the world tried many approaches and learned from the failures. Or at least the smart people learned from the failures.

Throughout all of this, there was no single place a person could turn to in order to find what was working and what was not. In terms of communications, there was –

•   Vendor hype
•   Internet articles
•   Books

But there simply was no single place to track the reality of the evolution of data management.

But now there is.

If you want a definitive record of such subjects as –

•   Inmon vs Kimble
•   Ontologies
•   Data modelling
•   Success/failure case studies

A reality-based account of what is going on in data management, take a look at Bill Inmon’s Substack account. Just find Bill on Substack. Everything is free.

The format of the articles is 10 to 40 pages in the article. They are monographs – longer than a standard article and shorter than a book.

The subjects covered are all about data management, data architecture, and data modelling. Every article is reality-based.

Give it a try. You’ll be happy you did.