Introduction
Data lakes, data warehouse, RDBMs, NOSQL, SharePoint, and Excel, today’s enterprises have an overabundance of data stored across different organization and technologies. However, the promise of big data’s ability to provide insights and revolutionize analytics has not fully materialized. In this post, we’ll take a look at why that is and how to achieve valuable insights into your data.
Most companies today are data driven and there has been an explosion in volume driven by accessible and affordable storage. This is along with digitization of data and an explosion in IOT. Despite all this, many companies struggle with finding a return on investment. What drives this unrealized potential is not the lack of data or the storage/access technology but rather the lack of knowledge. This where Knowledge Graphs play a role in helping enterprises realize value in their data.
Knowledge Graphs Empower AI Capabilities
But what is a Knowledge Graph? A Knowledge Graph connects data across different sources (structured and unstructured) and provides a semantic enriched structure that enables discovery, insight and empowers AI capabilities. It can also be viewed as a network of objects with semantic and functional relationships between the different connected objects/things. The more relationships created, the more context the data objects/things have, which then provides a bigger picture of the whole situation, helping users make informed decisions with connections that they may have never found. Although a knowledge graph relies on a graph database (technology) to store and process data, it is the data, connectivity and ontology that transforms a graph database with properties to a knowledge graph.
For example, an object node that has the name PAM has little meaning to a computer or an algorithm (and most individuals). There is no context to associate PAM with an infection or what relationships that infection may have with propagation mechanisms or preventive measures. A knowledge graph resolves this by labelling the PAM node as an infection; and by associating the node to an infection ontology an algorithm can start to understand the PAM entity in context with other node types (e.g., propagation mechanism, medication, preventive measures) that may also be in the knowledge graph. In summary a knowledge graph understands real-world entities and their relationships to one another.’
The Key Benefits of Knowledge Graphs
Combine Disparate Data Silos: Knowledge Graphs help to combine disparate silos of data, giving an overview of all the organization’s knowledge – not only departmentally but also across departments and global organizations.
Bring Together Structured and Unstructured Data: Knowledge Graph technology means being able to connect different types of data in meaningful ways and supporting richer data services than most knowledge management systems. In addition, any graph can be linked to other graphs as well as relational databases. Organizations will then use this technology to extract and discover deeper and more subtle patterns with the help of AI and Machine Learning technology.
Make Better Decisions by Finding Things Faster: Knowledge Graph technology can help provide enriched and in-depth search results, helping to provide relevant facts and contextualized answers to specific questions. Knowledge Graphs can do this because of its networks of “things” and facts that belong to these “things”. “Things” can be any business objects or attributes and facets of these business objects, such as: projects, products, employees or their skills.
Data Standards and Interoperability: Knowledge Graphs are compliant with W3C standards, allowing for the re-use of publicly available industry graphs and ontologies (e.g., FIBO, CHEBI, ESCO, etc.), as well as the ISO standard for multilingual thesauri.
AI Enablement: Data from unstructured data sources up to highly structured data, can be harmonized and linked so that the resulting higher data quality can be used for additional tasks, such as machine learning (ML). Knowledge Graphs are the linking engine for the management of enterprise data and a driver for new approaches in Artificial Intelligence
Knowledge Use Cases – Value Across Verticals
Pharmaceutical Industry: Boehringer Ingelheim uses the extensive capabilities of Knowledge Graphs to provide a unified view of all their research activities.
Telecommunications: A global telecom company benefits from the power of Enterprise Knowledge Graphs, helping to generate chatbots based on semi-structured documents
Government: A large Australian governmental organization provides trusted health information for their citizens by using several standard industry Knowledge Graphs (such as MeSH and DBPedia etc.). The governmental health platform (Healthdirect Australia) links more than 200 trusted medical information sources that help to enrich search results and provide accurate answers.
IT & IT Services: A large IT services enterprise uses Enterprise Knowledge Graphs to help them link all unstructured (legal) documents to their structured data; helping the enterprise to intelligently evaluate risks that are often hidden in common legal documents in an automated manner.
Digital Twins and Internet of Things. The Internet of Things (IoT), considered as a graph, can become the basis of a comprehensive model of physical environments that captures relevant aspects of their intertwined structural, spatial, and behavioral dependencies. It can support the context-rich delivery of data for network-based monitoring, provide insight into customer pain points, and control of these environments with and extension to cyber-physical systems (CPS). Examples of this application are electric utilities with their extensive interconnectivity (wired and wireless), cyber security mandates and rich digital information (asset and customer)
Better Understanding of the Individual. Whether as a human resource tool or a customer service enabler a Knowledge Graph centered on the individual can connect data from across multiple sources (training, reviews, purchases, returns) and enable insights and recommendations for individuals as well as organizations.
Incorporating Knowledge Graphs In Your Organization
If one or more of the following scenarios sound familiar, then a Knowledge Graph can provide value:
There are communication breakdowns
across domains, as your departments have different views on things,
across organizations, as different departments have their own language, and
because the nomenclature has changed, and things today are named differently than a couple of years ago.
Getting the answer from existing systems is time consuming or fails because:
there are so many systems, but they do not talk to each other,
they all have different data models, and you need help to translate between them,
data and information reside in multiple sources structured and unstructured (Excel, SharePoint, Word, Power Point, PDFs, CRMs, intranet sites) with no defined connection,
you need experts to help wrangle the answers out of your systems, and
you always use Google instead of internal tools to find things.
You often keep wondering if you are missing insights:
because you have documentation that relies on subject matter experts to infer meaning and insights,
or your artifacts sometimes have obscure or inconsistent statements that are open for interpretation,
or making the connections across domains, documents, and individuals is challenging or not feasible.
About Cedrus: Knowledge Graphs are among a number of tools that Cedrus Digital utilizes in the AI transformation journey for companies of all sizes. If you’d like more information on Knowledge Graphs – including technology, life cycle, and how it can add value to your organization, please feel free to contact us
Martin Cardenas
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