The meteoric rise of AI is disrupting industries, automating tasks, and reshaping the future of work and having a profound impact across enterprises. The harsh reality is most organizations aren’t ready for AI. Industry analysts are increasingly placing contextualized information and graph technologies at the center of their impact radar of emerging technologies.
Acknowledging the significance of how these critical enablers define, contextualize, and constrain data for consistency and trust, is the maturity process for today’s enterprise data management practice to get AI ready. It shines a bright light on the emergence of the Graph Center of Excellence (CoE) as an important contributor to building a data and AI strategy.
Why do enterprises need Graph COE?
For organizations that are ready to take the leap from applications-centric to data-centric and become AI driven – the Graph CoE is an imperative foundation for ensuring data quality, interoperability, and reusability. It provides the semantic and contextual ingredients for building trustable and explainable AI solutions.
The goal of the Graph CoE is to build a scalable and resilient semantic knowledge driven graph as a data hub for all business-driven use cases. Building this is a critical step in the journey to efficiency and enhanced capabilities. The objective is to create a reusable architectural framework and a roadmap to deliver incremental value and capitalize on the benefits of content reusability with consistent semantics and data sharing across enterprise use cases. For organizations to progress on this path of Graph COE, they need dedicated resources for design, construction, ongoing maintenance and support of the foundational knowledge graph with the domain data model.
What is Graph CoE?
Graph CoE is an extension of the Office of Data Management and the domain of the Chief Data and AI Officer. It is a strategic initiative that focuses on the adoption of semantic standards and deployment of knowledge graphs across the enterprise to bring in the most important aspect of data – the semantics. Through an enterprise knowledge graph and core domain model, the goal is to establish best practices, implement governance, trustability and explainability of AI results.
Think of CoE as both the hub of graph activities within your organization and the mechanism to influence organizational culture of building context driven insights that power AI applications and solutions.
A Graph CoE is the best approach to ensure organizational understanding of the root causes and liabilities resulting from technology fragmentation and misalignment of data across repositories. It is the organizational advocate for new and enhanced approaches to data management. The message for executive stakeholders is to both understand the causes of the data dilemma and recognize that properly managed data is an achievable objective. Information literacy and cognition about the data pathway forward is worthy of being elevated as a ‘top-of-the-house’ priority.
Key elements of Graph CoE Data Governance
There are many elements that make up a well-structured Graph CoE. Some of the key elements are discussed below.
Information, data and AI literacy
Data literacy is the ability to read, write and communicate with data contextually. This includes understanding data sources, analytical techniques to elucidate use cases and represent business outcomes. AI Literacy is the ability to trustfully, ethically and responsibly understand, utilize, and guide AI solutions by considering the influence of AI on current business processes. AI literacy focuses mainly on applying AI technologies with context and requires skills with data and domain knowledge that goes beyond the foundational data competencies of data literacy.
Organizational strategy
One of the fundamental tasks of the Graph CoE data governance process is to define the overall strategy for leveraging knowledge graphs within the organization. This includes defining the underlying drivers (cost containment, process automation, flexible query, regulatory compliance, governance simplification) and prioritizing use cases (data integration, digitalization, enterprise search, lineage traceability, explainability of AI, access control and much more).
Data and analytics teams were supposed to provide guidance in making better decisions; instead, they created noise, thousands of dashboards and reports with inconsistent metric definitions and confusing insights have polluted the data driven insight strategy.
Organizational strategy should include establishing a Knowledge-Centric Culture that fosters a culture of curiosity, collaboration, and continuous learning, where employees are encouraged to share insights, expertise, and best practices.
Knowledge graph development
The Graph CoE should lead the development of each of the knowledge graph components. This includes working with subject matter experts to prioritize business objectives and build use case relationships. Building data and knowledge models, data onboarding, ontology development, source-to-target mapping, identity and meaning resolution and testing are all areas of activity to address. One of the critical components is the user experience and data extraction capabilities. The goal should be to create value without really caring what is being used at the backend.
Graph CoE data governance
Altogether, the Graph CoE is responsible for establishing data policies and standards to ensure that the semantic layer is built using software, data, and knowledge engineering principles that emphasize simplicity, interoperability, and reusability that provides the foundational capabilities for explainable and trustworthy AI. A Graph CoE based on knowledge graphs combines resolvable identity with a precise meaning – resolving ambiguity of semantics with data validation, and shifting data lineage and governance away from manual reconciliatory processes.
With a knowledge graph at the foundation, organizations can create a connected inventory of which data exists, how it is classified, where it resides, who is responsible, how it is used, how it moves across systems and semantic findability. This has profound implications to the governance operating model by simplifying and automating it.
Identifying business opportunities
Enterprises should focus on the creation of a “use case tree” or “business capability model” to identify muti-modal data that is part of their data ecosystem. This is to identify business priorities aligned with the data from initial use cases. Instead of transforming data for each new application, data must be stored once in a machine-readable format that retains the original context, connections, and semantics that provides reusability and eliminates ambiguities and inconsistencies.
Cross-functional collaboration
The pathway to success starts with the clear and visible articulation of support by executive management. This requires cooperation and interaction among teams from related departments to deploy and leverage the graph capabilities most effectively. Domain experts are required to build the domain conceptual model to provide the core building block for developing applications and services leveraging the graph. Business users identify and prioritize use cases to ensure the graph addresses their evolving requirements. Governance policies need to be aligned with insights from data stewards and compliance officers. Managing the collaboration is essential for orchestrating the successful shift from applications-centric to data-centric across the enterprise.
AI readiness
Before jumping on the AI bandwagon, organizations need to be data-, people- and technology- ready. Lack of AI-ready data is the primary hurdle for organizations to become AI driven — data that is enriched with business context, trust, and security. To “get AI right,” the urgency to rationalize siloed data becomes increasingly important. Building a Graph COE powered by KGs to govern AI can improve data quality, reduce costs and provide a better ROAI (Return on AI Investment).
AI-ready data component implies incorporating context with the data. This necessitates a shift from the traditional ETL mindset to a new ECL (extract, contextualize, and load) orientation, which ensures meaningful data connections. Hence it is advisable for enterprises to leverage semantic metadata as the core for facilitating data connections.
Success of AI initiatives is intrinsically tied to the quality of the data and the associated semantics. Without this foundational capability in an organization, AI solutions will always be limited in what can be built and achieved.
A Graph COE can fuel the transformative capabilities of generative AI to promote data quality, data transparency and AI explainability, the pillars for AI readiness.
Next steps
The pathway to progress for developing the Graph COE, should focus on the development of the team of involved stakeholders. The first step is to expand the identity of data owners who know the location and health of the data. Much of this is about understanding the organizational dynamics as to who the players are, who is trusted, who is feared, who elicits cooperation, and who is out to kill the activity.
This coincides with development of the action plan and assembling a team of skilled practitioners to ensure success. Enterprises will need an experienced architect who understands the workings of semantic technologies and knowledge graphs to lead the team. The CoE will need ontologists and/or taxonomists to engineer content and manage the mapping of data. Knowledge graph engineers coordinate the meaning of data, knowledge, and content models. Last but not the least, a project manager to be an advocate for the team and the development process.
Wrapping it up
Bottomline, in order to become AI driven, enterprises must use IA (Information Architecture). Connecting the dots and contextualizing data with graph technologies is a critical enabler for executing data-driven strategies in the age of AI. The Graph CoE is an important step in transforming an organization’s siloed data systems into a true enterprise data and AI platform that fuels the transformative capabilities of AI by promoting data quality, data transparency and AI explainability.
A well-structured CoE should be viewed as a driver of innovation and agility within the enterprise that facilitates better data integration, improves operational efficiency, contextualizes AI, and enhances user experience. It is the catalyst for building organizational capabilities for long-term strategic advantage and one of the key steps in the digital transformation journey.
