Applying Standards to Information Centric Operations
and applications . This inversion may remain elusive , but there have been applications of elements of this vision . In this section we will look at some case studies in finance , manufacturing and pharmaceutical industries , where some of these techniques have been tried or used . In these we will see that many elements of data centricity have been already implemented or are starting to fall into place . These examples range from the application of formal semantics through enterprise knowledge graphs to industrial digital twins and data visualization .
We will look at lessons learned from these initiatives . Then we will consider what aspects of these initiatives may benefit from the development of new standards or the use of existing ones .
We start by looking at a core aspect of data centric working as outlined in the Data Centric Manifesto , namely the provision of formal ontologies .
3.1 ONTOLOGIES IN FINANCE
In the Global Financial Crisis of 2008 a lot of financial firms were left with exposures to the institutions that went down . They were not short of data ; the data was there , and the reality of their contractual exposures was there , yet in many cases it took several weeks to turn that data into the knowledge they needed .
The financial industry realized that it could no longer afford to have data maintained in disparate silos . Siloed data represented the potential of knowledge , but was not readily accessible to business stakeholders in forms they could digest and act upon when they needed it . Integration across data silos has long been recognized as a source of costly inefficiencies , and a number of data standards had been developed to provide some common language at a data level . These standards are well understood and widely adopted . However the feedback from business stakeholders was that what was missing in these data standards was a formal definition of what each data element really meant .
What was needed to supplement financial industry data and messaging standards was a business-facing , computationally independent model of the meanings of the concepts that are to be reflected in data . In response to this industry feedback , the Financial Industry Business Ontology ( FIBO ) 6 was developed by the Enterprise Data Management Council ( EDM Council ) 7 as a means to capture “ unambiguous shared meaning ” across the financial services industry [ 6 ].
3.1.1 FIBO PROOFS OF CONCEPT
Once FIBO was developed and delivered to the industry in its initial draft format , several proofs of concepts were carried out . These included :
6 https :// spec . edmcouncil . org / fibo /
7 https :// edmcouncil . org / 16
February 2025