There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. Capturing data, converting it into the right insights, and integrating those insights quickly and efficiently into business decisions and processes is generating a significant competitive advantage for those who do it right. As a result, contextualized information and graph technologies are gaining in popularity among analysts and businesses due to their ability to positively affect knowledge discovery and decision-making processes.
The issue is many organizations have massive amounts of data that they collect and store in their relational databases, document stores, data lakes, and data warehouses. But until they connect the dots across their data, they will never be able to truly leverage their information assets. This is where graph technologies shine, as they allow enterprises to connect the dots across their data and progress on the vision of deriving knowledge, insight, and wisdom. This is why graph technologies have taken center stage in the data, analytics, and AI space because before one can really maximize the value from data, they must first connect the dots. The importance of these critical enablers to define, interpret, and constrain data for consistency and trust is all part of the maturity process for today’s enterprise.
However, there’s one component to the Graph maturation process that may trump all others: a Graph Center of Excellence (CoE). The concept of a Center of Excellence is not new, but when put into perspective with knowledge graph technologies and methodologies, a Graph CoE can help ensure the business can achieve its strategic objectives using a small-scale pilot initiative or “lighthouse project” that serves as a model for other similar projects.
Benefit of a Graph CoE
For companies that are ready to make the leap from being applications centric to data centric—and for companies that have successfully deployed graphs in business silos—the CoE becomes the foundation for ensuring data quality and reusability across the organization. Instead of transforming data for each new viewpoint or application, data is stored once in a machine-readable format that retains the original context, connections and meaning that can be used for any purpose.
Once value from the lighthouse pilot project can be demonstrated, 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 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. 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 knowledge graph.
Most often an extension of the Office of Data Management and the domain of the Chief Data Officer, the Graph CoE is a strategic initiative that focuses on the adoption of semantic standards and 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 the organization and the mechanism to influence organizational culture.
Five elements and benefits of a knowledge graph CoE include:
- Information literacy: A Graph CoE ensures 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 and helps executive stakeholders to understand the causes of the data dilemma and recognize that properly managed data is an achievable objective.
- 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 the 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 graph foundation is built using wise engineering principles that emphasize simplicity and reusability. When combining unified resolvable identity with precise meaning, quality validation, and data lineage, governance shifts away from manual reconciliation. With a knowledge graph serving as 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. Equally important, it simplifies and automates the governance operating model.
- 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. The user experience and data extraction capabilities need to take precedence so one way to win them over is to leverage visualization to create an intuitive user interface. The goal should be to create value without really caring what is being used at the backend.
- Cross-functional collaboration: To ensure success, start with the clear and visible support by executive management. However, the lynchpin 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.