It has been interesting watching the discussion and I must say I have
learned a lot. (01)
Despite having spent the last 20 years in developing data driven systems, my
masters degree was not in technology but in marketing, agricultural
marketing to be precise. Agriculture is a data rich environment where it is
easy to turn information into money. When I started my career most farms
were still relatively small businesses run by owners who saw little benefit
in computers (at least at the price they were back then). As John puts it so
well, to convince a small business owner to invest in anything I learned
that you had to be able to show him the money, not the promise of money
sometime down the road but real money and real soon. So where is the money
in an ontology? I could show farmers that by collecting data on their
livestock they could easily differentiate them from each other and if you
believe Darwin and you select for the traits you want, it is not long before
you see the benefit in the form of real money. Same goes for identifying the
diminishing returns on fertilizers and sprays as well as deciding when to
replace machinery. I could prove beyond reasonable doubt that a computer was
better at objectively analyzing data than a farmer's memory and you could
turn data into information and information into hard cash. So I repeat,
where is the money in an ontology? (02)
I work for PiLog and I sell an ISO 8000 Master Data Ontology Management
(PiLog-MDOM) application. There is no OWL or RDF to be seen but the
application includes a terminology manager, a data requirements manager, a
classification manager and a rendering guide manager. The terminology manger
is linked to the ECCMA Open Technical Dictionary (eOTD) an open free
repository of over 2 million concepts with associated terminology where all
the concepts and all the terminology are identified through public domain
identifiers. The ontology (a formal representation of knowledge as a set of
concepts within a domain, and the relationships between those concepts)
allows users to formalize and manage their existing terminology as well as
document and manage their requirements for data. These data requirements are
used to measure the quality of their master data (data quality is the degree
to which data meets requirements) and to identify what data is missing (we
also sell a service to get the missing data if they need our help in doing
so). (03)
Where is the money in ISO 8000 data quality? (1) you can collect better data
faster and at a lower cost, (2) you can create multilingual screens and
descriptions at a fraction of the cost of translations, (3) you can identify
duplicate master data records, (4) you can reduce maverick (free text)
spend, (5) you can reduce inventory (6) you can minimize risk (see Darwin)!
The bottom line is that all ERP applications (SAP, ORACLE, IBM MAXIMO,
Microsoft Dynamics, Infor, Sage,....) run better on ISO 8000 quality data! (04)
If we are to persuade the market that there is real value in an ontology we
have to show them how they can use an ontology to generate money, they have
no more interest in the technology itself then they do in the metadata used
in a spreadsheet, a document or presentation be it in Microsoft Office or in
Open Office. Demonstrations of what an ontology does or how it works is of
no interest - we have to show them the money! (05)
Peter
Peter.Benson@xxxxxxxxxxx or Peter.Benson@xxxxxxxxx
Linkedin: Peter R. Benson (06)
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