Leveraging Ontologies for Better Data Integration by David Linthicum.
From the post:
If you don’t understand application semantics ‑ simply put, the meaning of data ‑ then you have no hope of creating the proper data integration solution. I’ve been stating this fact since the 1990s, and it has proven correct over and over again.
Just to be clear: You must understand the data to define the proper integration flows and transformation scenarios, and provide service-oriented frameworks to your data integration domain, meaning levels of abstraction. This is applicable both in the movement of data from source to target systems, as well as the abstraction of the data using data virtualization approaches and technology, such as technology for the host of this blog.
This is where many data integration projects fall down. Most data integration occurs at the information level. So, you must always deal with semantics and how to describe semantics relative to a multitude of information systems. There is also a need to formalize this process, putting some additional methodology and technology behind the management of metadata, as well as the relationships therein.
Many in the world of data integration have begun to adopt the notion of ontology (or the instances of ontology: ontologies). Ontology is a term borrowed from philosophy that refers to the science of describing the kinds of entities in the world and how they are related.
Why should we care? Ontologies are important to data integration solutions because they provide a shared and common understanding of data that exists within the business domain. Moreover, ontologies illustrate how to facilitate communication between people and information systems. You can think of ontologies as the understanding of everything, and how everything should interact to reach a common objective. In this case the optimization of the business. (emphasis added)
The two bolded lines I wanted to call to your attention:
If you don’t understand application semantics ‑ simply put, the meaning of data ‑ then you have no hope of creating the proper data integration solution. I’ve been stating this fact since the 1990s, and it has proven correct over and over again.
I wasn’t aware understanding the “meaning of data” as a prerequisite to data integration was ever contested?
You?
I am equally unsure that having a “…common and shared understanding of data…” qualifies as an ontology.
Which is a restatement of the first point.
What interests me is how to go from non-common and non-shared understandings of data to capturing all the currently known understandings of the data?
Repeating what is uncontested or already agreed upon, isn’t going to help with that task.
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