… is not the title of the book I’ve been re-reading this week, though it might well be. Bill Kent’s Data and Reality was first published in 1978, and comes from the heroic age of database design and development, a period when such giants as Astrahan, Chen, Chamberlin, Codd, Date, Nijssen, Senko and Tschritzis were slugging it out over the relative merits of the relational, network, and binary database models and the abstractions they supposedly modelled : a struggle predominantly over terminology and ways of thought since (as Kent shows) almost all of these differently named and passionately advocated models were fundamentally very similar, differing only in the specific compromises they chose when confronted by the messiness of reality. Whether you call it a relation or an object or a record, the globs of storage handled by every database system were still records, combinations of fields containing representatives of perceptions of reality, chosen and combined for their utility in a specific context. The claim that such systems modelled reality in any complete sense is easy to explode; it’s remarkable though that we still need to be reminded, again and again, that such systems model only what it is (or has been) useful for their creators to believe. Kent is sanguine about this epistemological lacuna : “I can buy food from the grocer, and ask a policeman to chase a burglar, without sharing these people’s view of truth and beauty”, but for us, living in an age of massively interconnected knowledge repositories, which has developed almost accidentally from the world of more or less well-regulated corporate database systems, close attention to their differing underlying assumptions should be a major concern. This applies to the differently constructed communities of practice and knowledge which we call “academic disciplines” just as much as it does to the mechanical information systems those communities use in support of their activities.
In its time, Data and Reality was remarkable for introducing the idea that data representations and the processes carried out with them should be represented in a unified way, the basic idea of what we now call object-oriented processing; yet it also reminds of some fundamental ambiguities and assumptions swept under the carpet even within that paradigm. Are objects really uniquely identifiable? “What does ‘catching the same plane every Friday’ really mean? It may or may not be the same physical airplane. But if a mechanic is scheduled to service the same plane every Friday, it had better be the same physical airplane.” The way an object is used is not just part of its definition. It may also determine its existence as a distinct object.
Kent’s understanding of the way language works is clearly based on the Sapir-Whorf hypothesis: indeed, he quotes Whorf approvingly “Language has an enormous influence on our perception of reality. Not only does it affect how and what we think about, but also how we perceive things in the first place”. There is an odd overlap between his reminders about the mocking dance which words and their meanings perform together and contemporaneous debates within the emerging field that Wilks has charmingly characterized as “Good Old Fashioned AI”. And we can also see echoes of similar concerns within what was in the 1970s regarded as a new and different scientific discipline called Information Retrieval, concerned with the extraction of facts from documents. Although Kent explicitly rules text out of discussion (“We are not attempting to understand natural language, analyse documents, or retrieve information from documents”) his argument throughout the book reminds us that data is really a special kind of text, subject to all the hermeneutical issues we wrongly consider relevant only to the textual domain.
This is particularly true at the meta-level, of how we talk about our data models, and the systems we use to manipulate them. Because they were designed for the specific rather the general, and because they were largely developed in commercially competitive contexts, the database systems of the 1970s and 1980s proliferated terms and distinctions amongst many different kinds of entity, to an extent which Kent (like Ockham before him) argues goes well beyond necessity. This applies to such comparatively arcane distinctions as those between entity, attribute, and relationship, or between type and domain, all of which terms have subtly different connotations in different contexts, though all are reducible to a more precise set of simple primitives. It applies also (and here the TEI in me sits up and smirks) to the distinction between data and metadata. Many of the database systems of the eighties and nineties insisted that you should abstract away all the metadata for your systems into a special kind of database variously called a data dictionary, catalogue, or schema, using entirely different tools and techniques from those used to manipulate the data itself. This is a needless obfuscation once you realise that you cannot do much with your data without also processing its metadata. In more recent times, one of the more striking improvements that XML made to SGML was the ability to express a schema and the objects it describes using the same language. Where what are usually called the semantics of an XML schema should be described and how remains a matter which only a few current XML systems (notably the TEI) explicitly consider.
Kent seems to have been a modest and likeable man. He retired in 2000, and died five years later, leaving a legacy of accessible and still provocative papers, most of them available from his website . Like many other pioneers in computer science, his academic qualifications come from unrelated fields (in his case, chemical engineering and maths); like many others he worked long hours for IBM and HP, but achieved fame and intellectual satisfaction outside the corporate world in the development of industry standards and professional associations. Maybe that experience is also what underlies the much quoted paragraphs which end his book: