Understanding the Graph Center of Excellence
The Knowledge Management community has gotten good at extracting and curating knowledge.
There is a confluence of activity – including generative AI models, digital twins and shared ledger capabilities that are having a profound impact on enterprises. Recent research by analysts at Gartner places contextualized information and graph technologies at the center of their impact radar for emerging technologies. This recognition of the importance of these critical enablers to define, contextualize and constrain data for consistency and trust is all part of the maturity process for today’s enterprise. It also is beginning to shine light on the emergence of the Graph Center of Excellence (CoE) as an important contributor to achieving strategic objectives.
For companies who are ready to make the leap from being applications centric to data centric – and for companies that have successfully deployed single-purpose graphs in business silos – the CoE can become the foundation for ensuring data quality and reusability. Instead of transforming data for each new viewpoint or application, the data is stored once in a machine-readable format that retains the original context, connections and meaning that can be used for any purpose.
And now that you have demonstrated value from your initial (lighthouse) project, the pathway to progress primarily centers on the investment in people. The goal at this stage of development is to build a scalable and resilient semantic graph as a data hub for all business-driven use cases. This is where building a Graph CoE becomes a critical asset because the journey to efficiency and enhanced capability must be guided.
Along with the establishment of a Graph CoE, enterprises should focus on the creation of a “use case tree” or “business capability model” to identify where the data in the graph can be extended. This is designed to identify business priorities and must be aligned with the data from initial use cases. The objective is to create a reusable architectural framework and a roadmap to deliver incremental value and capitalize on the benefits of content reusability. Breakthrough progress comes from having dedicated resources for the design, construction and support of the foundational knowledge graph.
The Graph CoE would most logically be an extension of the Office of Data Management and the domain of the Chief Data Officer. It is a strategic initiative that focuses on the adoption of semantic standards and the deployment of knowledge graphs across the enterprise. The goal is to establish best practices, implement governance and provide expertise in the development and use of the knowledge graph. Think of it as both the
hub of graph activities within your organization and the mechanism to influence organizational culture.
Some of the key elements of the Graph CoE include:
• Information Literacy: 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 approaches to data management. The message for all senior 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.
• Organizational Strategy: One of the fundamental tasks of the Graph CoE is to define the overall strategy for leveraging knowledge graphs within the organization. This includes defining the underlying drivers (i.e., cost containment, process automation, flexible query, regulatory compliance, governance simplification) and prioritizing use cases (i.e., data integration, digitalization, enterprise search, lineage traceability, cybersecurity, access control). The opportunities exist when you gain trust across stakeholders that there is a path to ensure that data is true to original intent, defined at a granular level and in a format that is traceable, testable and flexible to use.
• Data Governance: The Graph CoE is responsible for establishing data policies and standards to ensure that the semantic layer is built using wise engineering principles that emphasize simplicity and reusability. When combining resolvable identity with precise meaning, quality validation and data lineage – governance shifts away from manual reconciliation. With a knowledge graph at the foundation, organizations can create a connected inventory of what data exists, how it is classified, where it resides, who is responsible, how it is used and how it moves across systems. This changes the governance operating model – by simplifying and automating it.
• 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. Tools should be easy to use and help teams do their job faster and better. Remember, people have an emotional connection to the way they work. Win them over with visualization. Invest in the user interface. Let them gain hands-on experience using the graph. The goal should be to create value without really caring what is being used at the backend.
• Cross-Functional Collaboration: The pathway to success starts with the clear and visible articulation of support by executive management. It is both essential and meaningful because it drives organizational priorities. The lynchpin, however, involves cooperation and interaction among teams from related departments to deploy and leverage the graph capabilities most effectively. Domain experts from technology are required to provide the building blocks for developing applications and services that leverage 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.
After successfully navigating the initial stages of your project, the onward pathway to progress should focus on the development of the team of involved stakeholders. The first hurdle is to expand the identity of data owners who know the location and health of the data. Much of this is about organizational dynamics and understanding who the players are, who is trusted, who is feared, who elicits cooperation and who is out to kill the activity.
This coincides with the development of an action plan and the assembly of the team of skilled practitioners needed 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 to engineer content and manage the mapping of data. Knowledge graph engineers are needed to coordinate the meaning of data, knowledge and content models. This will also require a project manager to be an advocate for the team and the development process.
And a final note, organizations working on their AI readiness must understand it requires being ready from the perspective of people, technology and data. The AI-ready data component means incorporating context with the data. Gartner points this out by noting that it necessitates a shift from the traditional ETL mindset to a new ECL (extract, contextualize and load) orientation. This ensures meaningful data connections. Gartner advises enterprises to leverage semantic metadata as the core for facilitating data connections.
The Graph CoE is an important step in transforming your lighthouse project or silo deployment into a true enterprise platform. 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 the 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.
