Knowledge graphs are on the rise at enterprises that seek more effective ways to connect the dots between the data world and the business world. Paired with complementary AI technologies such as machine learning and natural language processing, knowledge graphs are enabling new opportunities for leveraging data and quickly becoming a fundamental component of modern data systems.
At Data Summit Connect, a free 3-day series of data-focused webinars, David Newman, strategic planning manager, senior vice president, Innovation Group, Innovation R&D, Wells Fargo Bank, and Jans Aasman, CEO, Franz Inc., provided a close look at how to use these technologies for game-changing results.
To watch the video of David Newman's presentation, go here.
To watch the video of Jans Aasman's presentation, go here.
In a presentation, titled "Knowledge Graphs and AI: The Future of Enterprise Data," Newman described the operational capabilities and benefits of knowledge graph technology and discussed how ontologies are the way forward to better represent enterprise definitions and data.
Newman described how knowledge graph technology can provide a layer of “knowledge” over legacy data structures to obtain maximum understanding of data inventory and provide the foundational building blocks for powerful data catalogs and hubs. Knowledge graph technology also positions organizations to better support Customer 360, risk management, regulatory compliance, technology asset management and many other initiatives.
Newman acknowledged that relational database technology will be around for a long time into the future but noted that knowledge graphs can provide additional tools that are highly effective for certain use cases.
According to Newman, some of the situations in which the use of an enterprise knowledge graph should be considered are:
- when we want a better data catalog capability
- when we need better data quality rules and metrics
- when the organization of the data is a network of relationships
- when disparate data exists across multiple silos that must be integrated and harmonized using a common data model
- when data must be classified into categories within a taxonomy
- when data that is highly variable and newly captured or streamed must be rapidly linked to existing data
- when new insights and relationships among data must be discovered, inferred, or predicted
- when data must be captured and stored in a structured form for analysts and memory
In Aasman's presentation, titled "Entity Event Knowledge Graphs for Data-Centric Organizations," he explained how entity-event knowledge graphs are critical for the future of analytics and data access in the enterprise and the importance of being able to rely on a single data infrastructure.
A knowledge graph system fuses and integrates data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from "entities" (e.g., Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.
Aasman walked attendees through the creation of an entity event model using AllegroGraph that was created for a company that needed to simplify and speed information access for its call center agents to avoid them having to sift through numerous databases while on calls, as well as a model that was developed and tested in production with Einstein Medical College and Montefiore Hospital in New York to help with analysis of the data of more than 2 million patients going back years.
The Entity-Event Data Model that Aasman presented puts core entities of interest at the center and then collects several layers of knowledge related to the entity as "events." Using this novel data model approach, organizations gain a holistic view of customers, patients, students, or important entities and the ability to discover deep connections, uncover new patterns, and attain explainable results.
According to Aasman, to data-centric organizations that are adopting knowledge graphs value the use of an enterprise ontology and taxonomy as a shared model of the objects and words that are important to the business, see the value eliminating silos and using one data infrastructure for all their applications, and also value the ability to connect everything that happens to and with their patients/customers/products as events off the core entity.
Data Summit Connect, a free 3-day webinar series runs through Thursday, June 11.
To access the program and register, go to www.dbta.com/DataSummit/2020/Registration.aspx.