Brief review of Data and Reality by William Kent. This book was written in 1978, but is still remarkably relevant in many ways.

After going through all the different terms that people use when talking about data (some of these terms have fallen out of fashion, others are still in use), Kent points out inconsistencies and limitations of the relational and hierarchical data models:

the data processing community has evolved a number of models in which to express descriptions of reality. These models are highly structured, rigid, and simplistic, being amenable to economic processing by computer. […] Some members of that community have been so overwhelmed by the success of a certain technology for processing data that they have confused this technology with the natural semantics of information.

Of course Kent is full of understanding for those poor, misguided souls:

The builders and users of today’s commercial systems quite justifiably want to avoid cluttering their systems with anything that may impair efficiency and productivity. The argument that this new approach will make the overall management of data more productive in the long run has yet to be convincingly demonstrated to them.

He predicts that data integration will be the killer application for a more sophisticated data model:

The need for a more descriptive model will only gradually achieve general recognition. It will come from the headaches of trying to crunch together the diverse formats and data structures used by growing families of applications operating on the same integrated data base.

Kent then goes on to outline an ideal data model (graph-based). Nevertheless he manages to remain realistic:

Perhaps it is inevitable that tools and theories never quite match. There are some opposite qualities inherent in them. […] Thus the truth of things may be this: useful things get done by tools which are an amalgam of fragments and theories. Those are the kinds of tools whose production and maintenance expense can be justified. Theories are helpful to gain understanding, which may lead to the better design of better tools.