Business leaders know the value of their data and are keenly aware that it holds the answers to their most pressing business questions. The insights to improve decision-making and enhance business performance they require, however, aren’t easy to elicit. Hence the widespread interest in machine learning. Knowledge graphs can help an organisation get machine learning to a useful production status and out of the lab.
Defined by The Turing Institute, the UK’s national institute for data science and AI, as the best way to “encode knowledge to use at scale in open, evolving, decentralised systems,” knowledge graphs are a big trend in the data science world. They provide a non-disruptive insight layer on top of a complex data resource. They drive intelligence into data to significantly enhance its value, but without changing any of the existing data infrastructures.
The real magic of knowledge graphs comes into play when they support AI and machine learning, uncovering patterns and anomalies. A decisioning knowledge graph surfaces data trends to augment analytics, machine learning, and data science initiatives. Given such power, it’s not surprising that Gartner recently stated that as many as one in two inquiries it gets on the topic of AI involves how to use graph technology. But how best to exploit its potential?
Knowledge graph: a journey data science teams are increasingly taking
In practice, knowledge graph use cases divide into two groups: actioning and decisions. The actioning graph’s aim is to drive action by providing assurance or insight. Data actioning graphs automate processes for better outcomes by providing data assurance, discovery, and insight. They include examples like data lineage, data provenance, data governance, compliance, and risk management. And moving from an actioning graph to a sophisticated decision graph fueling AI and machine learning is a journey data science teams are increasingly starting to take.
Knowledge graphs are already proving their worth. UBS, the multinational investment bank and financial services company based in Switzerland, built a detailed data lineage and governance tool that offers deep transparency into the data flows that feed its risk reporting mechanisms to meet financial compliance regulations.
Using an actioning knowledge graph, UBS built its data lineage and data governance tool called the Group Data Dictionary (GDD). With the GDD, UBS can track information as it flows through the enterprise, monitor its quality, discover errors, and trace them to the source, minimising damage and reducing data duplication.
UBS workflows and auditing capabilities are mostly built on relational technology, so synchronisation of the knowledge graph with the underlying legacy systems is essential. The GDD achieves this by constantly collecting metadata from the systems it tracks.
In the life sciences sector, Boston Scientific is a global medical device company that develops and manufactures a wide range of innovative diagnostic and treatment medical products, including pacemakers and artificial heart valves. The company has an integrated supply chain from raw materials to complex devices that includes development, design, manufacturing, and sales. Predicting and preventing device failures early in the process is crucial. However, the company had difficulty pinpointing the root cause of defects, limiting its ability to prevent future problems.
Using a decision knowledge graph, Boston Scientific was able to apply graph analytics to their supply chain and act on that to improve product reliability. The starting point was a knowledge graph that included parts, finished products, and failures. Using graph queries, Boston Scientific can quickly reveal subcomponents’ complex relationships and trace any failures to relevant parts.
The company can now identify previously unknown vulnerabilities by adding graph algorithms to rank parts based on their proximity to failures and match other components based on similarity. Since results can be automatically written back to their decisioning knowledge graph, Boston Scientific continues to enhance its data and improve product reliability.
Finally, NASA has tapped into graph technology to maximise intelligence from past mission experience. Its team has built a knowledge graph-enhanced application to comb through millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, and IT logs. As a result, an old breakthrough from the 1960s Apollo era resolved an issue in its 21st Century Orion spacecraft development that saved a million dollars of taxpayer money by heading off the need for two years of unnecessary work.
Accurate and actionable interpretations of complex data
Knowledge graphs provide data provenance and tracking to graph data discovery and analytics for efficient AI data sourcing. They’re also great for adding in the context which delivers better AI reasoning and more rapid decision-making. Exploiting the power of knowledge graphs, data scientists can maximise their investment in AI and machine learning and see real business benefits from more accurate and actionable interpretations of complex data.
Maya Natarajan is Sr. Director of Product Marketing at graph data platform leader Neo4j
