A new benchmarking study released at this week’s The Knowledge Graph Conference illustrates how large corporates are starting to embrace semantic standards and knowledge graph technology. The term knowledge graph refers to a set of standards and capabilities that help companies align data across disparate repositories. Adoption is growing because addressing the problem of data inconsistency is a prerequisite for achieving AI-powered applications that can help companies get more value from their content, save money on data reconciliation, and usher in a new era of business innovation.
Until recently knowledge graphs were considered mainly the domain of academics and engineers who worked primarily for tech companies like Google, Facebook, LinkedIn, and Amazon. That is clearly changing. Some 500 people attended this year’s conference, including executives from Goldman Sachs, Novartis, Morgan Stanley, AstraZeneca and Wells Fargo.
“We are seeing a dramatic increase in corporate use cases,” says conference speaker Michael Atkin, Managing Director of Content Strategies, a specialist consultancy that advises companies on data management, and principal author of the benchmarking study. Atkin is also a co-founder of the Enterprise Knowledge Graph Foundation, which focuses on building the market for graph-related applications.
The benchmarking report highlights how enhancing decision-making capabilities and supporting business innovation are the clear drivers for the uptake of knowledge graphs. While business value is the goal, the report says that most existing use cases are focused on core data management objectives such as data harmonization, content integration, entity resolution and lineage traceability. That is because knowledge graphs aim to solve the data incongruence problem, which is one of the biggest operational headaches for corporates, says Atkin. “Corporates suffer from technology fragmentation and as a result have a lot of data that doesn’t align across the organization. Doing the hard work to fix this data incongruence reality is a pre-requisite for realizing business value,” he says.
Because they are based on mature standards, knowledge graphs have the capacity to fix identification management issues and resolve meaning across sources. In addition, because the data is not linked to schemas, firms are able to extract information, perform scenario-based analysis and link up processes across their organization, says Atkin. “The good news is that these problems are solvable,” he says. “The technology works as advertised.” The big challenge is overcoming organizational inertia and dealing with the fear of new approaches. “This is an issue of corporate culture,” says Atkin. “Getting this solvability message to senior management is our biggest challenge.”
So how do knowledge graphs concretely help business? Graphs help corporates organize the information from disparate data sources to facilitate intelligent search, says Atkin. They make data understandable in business terms rather than in cryptic codes that are only understood by a handful of specialized personnel. And they speed the goal of digital transformation that encompasses all data points as well as the relationships between data elements.
“The message for senior management is that it is possible to harmonize data throughout the organization and that graphs offer an added dimension of context which informs everything from initial data discovery to flexible analytics,” says Atkin. “In short, graphs give corporates the ability to ask business questions and get business answers rather than focusing their energy on restructuring data and managing complex database joins.”
Knowledge graph adoption is still in its emerging stage according to the report’s survey of 240 companies in multiple sectors. Some 50% of responders said they are in the process of conducting pilots or proof of concepts primarily for limited use cases. Beyond those that are conducting knowledge graph experiments, some 35% of companies surveyed have developed extensible/scalable platforms based in reusable architecture using expanded design principles. These are the companies that can take advantage of concept reuse, structural quality assurance and flexible query that define the benefits of knowledge graph adoption.
Atkin says the gap between maturity levels is significant. “Making the leap from experimental use cases to operationalizing the knowledge graph to feed other use cases is difficult and not to be underestimated,” he says.
Companies in the more advanced stages of rolling out knowledge graphs tend to be large conglomerates in areas such as defense, life sciences and consumer goods, says Atkin. “Companies embracing graphs like J&J, Merck, Bosch, Raytheon and the U.S. Department of Defense are rolling this out as their underlying infrastructure for data,” says Atkin.
Integration, harmonization, entity resolution, inventory, lineage and identity management make up about 60% of primary use cases, according to the report. These represent the underlying data management infrastructure for companies and are essential “data fabric” components of the organization. Business applications such as data discovery, financial reporting and customer profiling are built from this underlying data fabric and represent just under 40% of use cases.
Several barriers must be overcome before the industry really takes off. The report says overcoming organizational inertia, navigating the skills gap, dealing with budgetary constraints and governance maturity are still major inhibitors to knowledge graph adoption.
The most important challenges are those related to the “business narrative, including convincing executive leadership about the value proposition, defining implementation requirements using empirical metrics and having pragmatic discussions on what is really required to implement knowledge graphs,” the report says.
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