Applying Standards to Information Centric Operations
1.1 THE DATA-CENTRIC MANIFESTO The Data Centric Manifesto states ( emphasis in original ): quote
“ We have uncovered a root cause of the messy state of Information Architecture in large institutions and on the web today . It is the prevailing application-centric mindset that gives applications priority over data . The remedy is to flip this on its head . Data is the center of the universe ; applications are ephemeral .
The key principles of this are applicable today in any large organization . These principles set out a way of working that can be achieved today by using semantic technologies , and will drive efficiencies in integration , application development and usability of the suite of applications used in large firms such as financial services providers .
The above quoted paragraph states : “ The remedy is to flip this on its head .”
However , let ’ s take a closer look at this . If we were to really flip this on its head , we would not simply be installing webs of linked enterprise data across a firm , we would be changing the way people interact with the tasks of word processing , presentation , calculation and the rest .
That ’ s not coming any time soon .
In this article we will consider both the current state of data-centric or information-centric working , with some examples and case studies , and take a brief glimpse at what the next steps might be , starting with recent developments in the digital twins area . The provision of data in a common format with common semantics is a pre-requisite for any “ flipping on its head ” that may happen later , and the benefits of this first phase can be obtained now . We will also look at how standards can contribute , both to the current innovations that are driving this first wave of datacentricity , and in what may come further down the line .
To start then : data that is shared across the organization and among applications needs to be in a common format and have some common ways of expressing the intended meanings of each piece of data . These are not the same thing .
For common data formats , we would look to the linked data or enterprise knowledge graph formats of RDF 4 and RDF Schema 5 , along with other graph data formats . These are easy enough to find , but first you need to get the semantics right . That is , the meaning of the data . To be clear , we are not using the word “ semantics ” here in any special sense other than to talk about what things mean . The technology for this is often referred to as “ Semantic Technology ” and at the heart of this is a kind of artifact , or more correctly a class of artifacts , called “ Ontology .”
4
RDF 1.1 Concepts and Abstract Syntax . Available : https :// www . w3 . org / TR / rdf11-concepts /
5
RDF Schema 1.1 . Available : https :// www . w3 . org / TR / rdf-schema / 14
February 2025