Adopting a Graph Center of Excellence can help organizations effectively manage data, maximize AI-driven insights, and enhance decision-making
As digital enterprises enter the fourth industrial revolution – a world where AI permeates across every nuance of the digital world, organizations are trying to seamlessly integrate AI into the organizational fabric, heralding new possibilities and challenges.
Critical to the success of AI is the “Data Journey” that has a profound impact for enterprises to become data and AI driven. Capturing data, transforming it into actionable insights, integrating those into business processes and decisioning generates competitive advantage for those organizations who can do it right.
Over decades, organizations have stock-piled massive amounts of multi-modal data that is siloed across heterogeneous data stores. However, until the disparate data points are contextually connected across the silos, organizations will never be able to leverage the latent potential of their information assets. Realizing the full potential of GenAI is often hindered by outdated data integration methods and lack of a comprehensive semantic layer that ensures data quality, interoperability and consistency for use in AI solutions. This is a roadblock to AI innovation, impeding developer agility, hampering data reuse, and slowing innovation.
Graph technologies as an AI enabler
The crux of the matter is an organization’s AI strategy is intricately tied to its data management strategy and practices. Until the foundational challenges of data management are addressed, AI capabilities will be constrained, perpetually waiting for contextualized data to augment meaningful decision-making and transition to the knowledge and wisdom layer of the DIKW pyramid.
This is where graph technologies are fast emerging as a critical AI enabler. Graph based approach to data management, allows enterprises to connect the dots across their data in their odyssey to derive actionable insights, knowledge, and wisdom.
Graph technologies have taken center stage in the data, analytics, and AI space as they maximize the value from data by connecting the dots semantically and unambiguously. Contextualized data with graphs are gaining in popularity among analysts and businesses due to their ability to streamline knowledge discovery and decision-making with semantics.
This is the reason why Gartner’s technology impact radar for 2024, puts knowledge graphs as the epicenter for AI success and initiatives. The core component to the adoption of Graph maturation process that is cardinal to all others, is building a Graph Center of Excellence (CoE). Graph CoE — when put into perspective with semantic driven knowledge graph-based technologies — ensures business can achieve its strategic objectives and unlock the power of semantics to find meaningful connections across heterogeneous data.
Elements of the Graph CoE
The Graph COE is an extension of the Office of Data Management in the domain of the Chief Data Officer. It 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.
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 understand root causes of data dilemma.
Think of it as the hub of graph activities within the organization and a mechanism to influence organizational culture. For enterprises that have made the leap from being applications centric to data centric and those that have deployed graph solutions in silos — the CoE becomes the foundation for ensuring semantic data integrity, promotes data quality and reusability across the organization. Instead of transforming data for each new viewpoint or application, data is mapped in a machine-readable format that retains the context, connections leading to better data comprehension.
Some of the major elements of a Graph COE are discussed below.
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 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.
Team Development
The path to Graph COE centers in building a team with the goal of building a scalable, resilient semantic graph as a data hub for business use cases. To ensure successful establishment of Graph COE organizations should focus on development of a team of stakeholders. This coincides with the development of an action plan and the assembly of the team of skilled practitioners needed to ensure success. It includes identifying and expanding, identity of data owners knowledgeable of the location, purpose, utility and health of the enterprise data. This requires understanding the organizational dynamics – who the players are, who is trusted, who is feared, who elicits cooperation, and who is out to kill the activity.
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 required to coordinate the meaning of data, knowledge, and content models. It also requires a project manager to be an advocate for the team and the development process.
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 are imperative skills to create intuitive user interfaces and end user interaction.
Cross-functional collaboration
To ensure success, it is important to start with clear visible support by executive management. The lynchpin to success requires involvement, 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.
Use Case Tree development
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.
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 trust with data quality, traceability and explainability of AI solutions. It combines unified resolvable identity of entities and relationships, with precise meaning, quality validation, and data lineage promoting a shift 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 considerably simplifying the operating governance model.
AI readiness
The AI-ready data component means incorporating context with the data. Gartner points this out by placing the knowledge graph at the center of their emerging radar for AI — and noting that it necessitates a shift from the traditional ETL mindset to a new ECL (extract, contextualize, and load) orientation. To ensure meaningful data connections even further, Gartner advises enterprises to leverage semantic metadata as the core for facilitating data connections.
Conclusion
The Graph CoE is a critical step in transforming siloed, fragmented graph deployments into an enterprise-wide platform. A well-structured CoE is the catalyst of AI and data driven innovation that facilitates semantic data integration with improved operational efficiency, contextualized AI, and enhances data findability, interoperability, reusability and data sharing.
The road to becoming a AI-native knowledge organization starts by building a Graph COE that fuses semantics with AI technology is a critical investment for organizations to enable AI across operations, processes and workflows.
