The Semantics are not yet clear.
March 15, 2012
I went along to "Demystifying Financial Services Semantics" on Tuesday, a one day conference put together by the EDMCouncil and the Object Management Group. Firstly, what are semantics? Good question, to which the general answer is that semantics are the "study of meaning". Secondly, were semantics demystified during the day? – sadly for me I would say that they weren't, but ironically I would put that down mainly to poor presentations rather than a lack of substance, but more of that later.
Quoting from Euzenat (no expert me, just search for Semantics in Wikipedia), semantics "provides the rules for interpreting the syntax which do not provide the meaning directly but constrains the possible interpretations of what is declared." John Bottega (now of BofA) gave an illustration of this in his welcoming speech at the conference by introducing himself and the day in PigLatin, where all of the information he wanted to convey was contained in what he said, but only a small minority of the audience who knew the rules of Pig Latin understood what he was saying. The rest of us were "upidstay"…
Putting this in the more in the context of financial markets technology and data management, the main use of semantics and semantic data models seem to be as a conceptual data model technique that abstract away from any particular data model or database implementation. To humour the many disciples of the "Church of Semantics", such a conceptual data model would also be self-describing in nature, such that you would not need a separate meta data model to understand it. For example take a look at say the equity example from what Mike Aitkin and the EDM Council have put together so far with their "Semantics Repository".
Abstraction and self-description are not new techniques (OO/SOA design anyone?) but I guess even the semantic experts are not claiming that all is new with semantics. So what are they saying? The main themes from the day seem to be that Semantics:
- can bridge the gaps between business understanding and technology understanding
- can reduce the innumerable transformations of data that go on within large organisations
- is scaleable and adaptable to change and new business requirements
- facilitates greater and more granular analysis of data
- reduces the cost of data management
- enables more efficient business processes
Certainly the issue of business and technology not understanding each other (enough) has been a constant theme of most of my time working in financial services (and indeed is one of the gaps we bridge here at Xenomorph). For example, one project I heard of a few years back was were an IT department had just delivered a tick database project, only for the business users to find that that it did not cope with stock splits and for their purposes was unusable for data analysis. The business people had assumed that IT would know about the need for stock split adjustments, and as such had never felt the need to explicitly specify the requirement. The IT people obviously did not know the business domain well enough to catch this lack of specification.
I think there is a need to involve business people in the design of systems, particularly at the data level (whilst not quite a "semantic" data model, the data model in TimeScape presents business objects and business data types to the end user, so both business people and technologist can use it without showing any detail of an underlying table or physical data structure). You can see a lot of this around with the likes of CADIS pushing its "you don't need a fixed data model" ETL/no datawarehouse type approach against the more rigid (and to some, more complete) data models/datawarehouses of the likes of Asset Control and GoldenSource. You also get the likes of Polarlake pushing its own semantic web and big data approach to data management as a next stage on from relational data models (however I get a bit worried when "semantic web" and "big data" are used together, sounds like we are heading into marketing hype overdrive, warp factor 11…)
So if Semantics is to become prevalent and deliver some of these benefits in bringing greater understanding between business staff and technologists, the first thing that has addressed is that Semantics is a techy topic at the moment, which would cause drooping eyelids on even the most technically enthused members of the business. Ontology, OWL, RDF, CLIF are all great if you are already in the know, but guaranteed to turn a non-technical audience off if trying to understand (demystify?) Semantics in financial markets technology.
Looking at the business benefits, many of the presenters (particularly vendors) put forward slides where "BAM! Look at what semantics delivered here!" was the mantra, whereas I was left with a huge gap in seeing how what they had explained had actually translated into the benefits they were shouting about. There needed to be a much more practical focus to these presentations, rather than semantic "magic" delivering a 50% reduction in cost with no supporting detail of just how this was achieved. Some of the "magic" seemed to be that there was no unravelling of any relational data model to effect new attributes and meanings in the semantic model, but I would suggest that abstracting away from relational representation has always been a good thing if you want to avoid collapsing under the weight of database upgrades, so nothing too new there I would suggest but maybe a new approach for some.
So in summary I was a little disappointed by the day, especially given the "Demystifying" title, although there were a few highlights with Mike Bennett's talk on FIBO (Financial Instruments Business Ontology) being interesting (sorry to use the "O" word). The discussion of the XBRL success story was also good, especially how regulators mandating this standard had enforced its adoption, but from its adoption many end consumers were now doing more with the data, enhancing its adoption further. In fact the XBRL story seemed to be model for regulators could improve the world of data in financial markets, through the provision and enforcement of the data semantics to be used with each new reporting requirement as they are mandated. In summary, a mixed day and one in which I learned that the technical fog that surrounds semantics in financial markets technology is only just beginning to clear.