Understanding the Graph Center of Excellence

This article discusses what, why, and how organizations should focus on building the Graph COE and its associated payback for their data management practices

May 17, 2024 7 mins. read Brandon RichardsSumit PalSumit PalMike AtkinMike Atkin

There is a confluence of activity – including generative AI models, digital twins, and shared ledger capabilities – that are having a profound impact on enterprises. Industry analyst reports place contextualized information and graph technologies at the center of their impact radar for emerging technologies.

Acknowledging the significance of how these critical enablers define, contextualize, and constrain data for consistency and trust, is all part of the maturity process for today’s enterprise. It is also beginning to shine light on the emergence of the Graph Center of Excellence (CoE) as an important contributor to achieving strategic objectives. 

Why do enterprises need Graph COE?

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, interoperability 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 semantics 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

What is Graph COE?

The Graph CoE is 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 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. 

Key Elements

There are many elements that make up a well-structured Graph CoE. However, there are some key elements we want to point out.

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 (cost containment, process automation, flexible query, regulatory compliance, governance simplification) and prioritizing use cases (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 semantic layer is built using software, data, and knowledge engineering principles that emphasize simplicity, interoperability, and reusability. When combining resolvable identity with a 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. 

Next Steps

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 and/or taxonomists 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. 

Before jumping on the AI bandwagon, organizations need to be data-, people- and technology- ready. The AI-ready data component means 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. 

To Wrap It Up

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. 

 

Article's content

General Manager APAC region at Ontotext

Brandon has spent the last eight years helping hundreds of enterprises across APAC on their graph technology journey at both Ontotext and Neo4j. Prior to that, he spent 6 years at Oracle working with strategic accounts and founded and managed a commercial real estate strategy firm in Silicon Valley for over 4 years. He is currently based in Kuala Lumpur, Malaysia.

Sumit Pal

Sumit Pal

Strategic Technology Director at Ontotext

Sumit Pal is an Ex-Gartner VP Analyst in Data Management & Analytics space. Sumit has more than 30 years of experience in the data and Software Industry in various roles spanning companies from startups to enterprise organizations in building, managing and guiding teams and building scalable software systems across the stack from middle tier, data layer, analytics and UI using Big Data, NoSQL, DB Internals, Data Warehousing, Data Modeling, Data Science and middle tier.

Mike Atkin

Mike Atkin

Managing Director at Content Strategies LLC

Michael Atkin has been an analyst and advocate for data management since 1985. His experience spans from the foundations of the information industry to the adoption of semantic technology. He has served as an advisor to financial institutions, global regulators, publishers, consulting firms and technology companies.