Linked Data is a universal approach for naming, shaping, and giving meaning to data, using open standards. It was meant to be the second big information revolution after the world wide web. It was supposed to complement the web of documents with the web of data so that humans and machines can use the Internet as if it is a single database while enjoying the benefits of decentralisation1This is the balance between autonomy and cohesion – essential for any socio-technical system..
Today on the web we have 1495 linked open datasets, according to the LOD cloud collection. Some among them like Uniprot and Wikidata are really big in volume, usage, and impact. But that number also means that today, 15 years after the advent of Linked Data, LOD datasets are less than 0.005% of all publicly known datasets. And even if we add to that the number the growing amount of structured data encoded as JSON-LD and RDFa in the HTML, the large majority of published data is still not available in a self-descriptive format and is not linked.
That’s in the open web. Inside enterprises, we keep wasting billions attempting to integrate data and pay the accumulated technical debt, only to find ourselves with new creditors. We bridge silos with bridges that turn into new silos, ever more expensive. The use of new technologies makes the new solutions appear different and that helps us forget that similar approaches in the past failed to bring lasting improvement. We keep developing information systems that are not open to changes. Now we build digital twins, still using hyper-local identifiers, so they are more like lifeless dolls.
Linked Enterprise Data can reduce that waste and dissolve many of the problems of the mainstream (and new-stream!) approaches by simply creating self-descriptive enterprise knowledge graphs, decoupled from the applications, not relying on them to interpret the data, not having a rigid structure based on historical requirements but open to accommodate whatever comes next.
Yet, Linked Enterprise Data, just like Linked Open Data, is still marginal.
Why is that so? And what can be done about it?
I believe there are five reasons for that. I explained them in my talk at the ENDORSE conference the recording of which you’ll find near the end of this article. I was curious how Linked Data professionals will rate them and also what have I missed out on. So I made a small survey. My aim wasn’t to gather a very large sample, but rather to have the opinion of the qualified minority. And indeed most respondents had over 7 years of experience with Linked Data and semantic technologies. Here’s how my findings got ranked from one to five: