I decided to conduct an informal survey in an attempt to gauge the current level of adoption and growth potential for Semantic Technology as an industry vertical. The results indicate to me that while progress is being made we still need to do a better job of delivering the message – this messaging problem is the number one reason why adoption of Semantic Technologies and Semantic Methodologies is proceeding slower than we had anticipated. 

So let’s examine review some of the assumptions first:

•    This is not a formal study.
•    This is more business focused – it measures how much demand there is for Semantic expertise.
•    While recognizing that the economy is in the midst of a Great Recession, that can’t be necessarily be used as an excuse for why adoption may be lagging because we’re looking at how demand for Semantic skills compares with traditional IT skills or other emerging trends.
•    If someone doesn’t understand what Semantic Technology is, they won’t include it in either their project or personnel requirements (or descriptions).

Tracking Semantic Skills Demand:

The survey premise was simple. I took a set of terms; both skills-related and technology-related and noted their prevalence on DICE.com search results. Why did I choose DICE.com? Because it provides a near-real time window into what projects are doing what technology trends have translated themselves into actual project requirements. I’ve been doing this off and on for several months – while I’m encouraged that a presence has been established, I remain convinced the IT industry as a whole still doesn’t quite appreciate the potential of Semantic technology.

The search terms are broken into 4 categories which highlight today’s results from DICE.com:

Skills or General Terms:
•    Semantic -184
•    Semantic Web – 19
•    SPARQL – 7
•    RDF – 44
•    OWL – 22
•    Ontology – 35
•    Taxonomy – 173
•    Semantic Integration – 0
•    Linked Data – 3

Job Titles:
•    Ontologist – 35
•    Semantic Architect – 1
•    Semantic Engineer – 0
•    Semantic Analyst – 0
•    Taxonomist -1

Similar Roles
•    Enterprise Architect – 594
•    Data Architect – 374
•    Data Modeler – 179
•    SOA Architect – 48
•    SOA Developer – 36
•    Web Services Developer – 40
•    Systems Engineer – 1695
•    Analyst – 11301

General Terms
•    SOA – 2356
•    Java – 12018
•    Cloud – 580
•    MDM – 312
•    Master Data Management – 1606
•    Metadata – 707
•    E-learning – 346
•    XML – 7646
•    C++ – 4847
•    Web Services – 4693
•    Data Integration – 575
•    Application Development – 5076
•    Database – 14904
•    UML – 1669

My Conclusions

Well, besides reinforcing the fact that different people often describe the same things using different terminology or role descriptions (another semantics discussion), the results seemed to indicate that we’re perhaps touching less than 5% of the potential market for Semantic Technology and Practice. Semantic technology standards, software and engineering practice can be exploited right now, most notably in the field of data interoperability.

One thing to keep in mind is that Semantic Technology is complimentary to existing capabilities – in other words, the most obvious near-term application for Semantic Technology is integration-related. Unlike SOA, we’re not talking about replacing both infrastructure or application logic – we’re talking about new ways to provide coherence across them. In some ways Semantic Tech parallels the conceptual approach being posited with Cloud Computing – a new way to leverage existing assets. However Cloud Computing, another emerging trend – is gaining more traction, more quickly without necessarily having proved itself yet either. The most ironic thing of course is that much of what Cloud Computing will become is wholly dependent on Semantic Data Interoperability. 
 
Copyright 2010 – Stephen Lahanas