Why Knowledge Centricity Is The Key To Reliable Enterprise AI
by Chris Brockmann, CEO eccenca.
5 minutes read.
Only a few large enterprises like Google, Amazon, and Uber have made the mindset and capability transition to turn data and knowledge into a strategic advantage. They have one thing in common: their roadmap is built on data and knowledge-centric principles and leverages Enterprise Knowledge Graph technology as a foundational layer.
Over the last years it has become obvious that the majority of companies fail to achieve a real ROI on their Digital Transformation and AI initiatives as long as they continue to follow outdated, application-centric IT management practices. We, the knowledge graph community, have long been reacting to this with rather technical explanations about RDF and ontologies. While our arguments have been right, they did not really address the elephant in the room: true AI Readiness is not only a technological issue but a question of mindset and Data and Knowledge Centricity.
The Quagmire of Application Centricity
Commonly, IT management is stuck in silos with application-centric principles. Solutions for a particular problem (e.g. financial transactions, data governance, GDPR compliance, customer relationship management) are thought of in singular applications. This has created a plethora of stand-alone applications or systems in companies which store and process interrelated or even identical data but are unable to integrate. That’s because every application has its own schema and data semantics. And companies have hundreds or even thousands of different applications at work.
Every application has its own schema and data semantics. Even when discussing a Semantic Data Fabric, IT management often starts the argument from an application point of view, which only reinforces silos.
Companies Struggle With AI Readiness Because Of Application Centricity
This application-centric mindset has created an IT quagmire. It prevents automation and the scaling of reliable AI because of three main shortcomings:
Data IDs are local. The identification of data is restricted to its source application, which prevents the global identification and (re)use necessary for Trusted AI.
Data semantics are local. The meaning of data, information about their constraints, rules and context is hidden either in the software code or in the user’s head. This makes it difficult to work corporatively with data and also hinders the automation of data-driven processes and the creation of Valuable AI.
The knowledge (about data’s logic) is IT turf. This is the biggest barrier to Knowledge Democratization. Business users who actually need this data and knowledge to scale their operations and develop their business in an agile way are always dependent on an overworked IT which knows the technicalities but doesn’t understand the business context and needs. Thus, scalability and agility are prevented from the start.
Data Centricity: The Foundation for Scalable Data Governance
Data centricity changes this perspective because it puts data and knowledge before applications. Moreover, it simplifies data and knowledge management. The term was coined by author and IT veteran Dave McComb. The aim of data centricity is to “base all application functionality on a single, simple, extensible and federateable data model”, as Dave recently outlined in our Escape From Data Darkness webcast episodes. At first, this might sound like advocating yet another one of these US$ 1bn data integration / consolidation projects done by a big name software vendor, the likes of which have failed over and over again. Alas, it’s quite the opposite.
Findable, Accessible, Interoperable & Reusable
Of course, this sounds exactly like what we have been talking about all those years with knowledge graph technology and FAIR data. And we have seen it working beautifully with our customers like Siemens, AstraZeneca, Daimler and Bosch, among others. By implementing a Semantic Data Fabric, eccenca Corporate Memory provides them with a central knowledge hub for their enterprise information that digitalizes expert knowledge, connects disparate data and makes it Findable, Accessible, Interoperable, and Reusable (FAIR) – to both machines and humans. Still, what we have learned from those projects is this: Conviction comes before technology, just as data comes before the application. Knowledge graph technology certainly is the key maker to digital transformation. But a data and knowledge-centric mindset is key.
A Central Data Hub For Knowledge Driven Automation
Data centricity does not strive to exchange the existing IT infrastructure with just another proprietary application. Data centricity embraces the open-world assumption and agility concepts and thus natively plays well with the rest of the data universe. The application-centric mindset always struggles with questions of integration, consolidation and a religious commitment to being the "single source of truth". The data-centric mindset does not have to, because integration is (no pun intended) an integral part of the system. Or as Dave puts it in his book The Data-Centric Revolution: “In the Data-Centric approach […] integration is far simpler within a domain and across domains [because] it is not reliant on mastering a complex schema. […] In the Data-Centric approach, all identifiers (all keys) are globally unique. Because of this, the system integrates information for you. Everything relating to an entity is already connected to that entity" without having to even consolidate it in a central silo.
This article was adapted from a post first released as a guest post on DBpedia.
