Using Knowledge Graph Data Models to Solve Real Business Problems
A successful business today must possess the capacity to quickly glean valuable insights from massive amounts of data and information coming from diverse sources. The scale and speed at which companies are generating data and information, however, often makes this task seem overwhelming.
An Enterprise Knowledge Graph allows organizations to connect and show meaningful relationships between data regardless of type, format, size, or where it is located. This allows us to view and analyze an organization’s knowledge and data assets in a format that is understood by both humans and machines.
How much of your data and information is currently dispersed across business units, departments, systems, and knowledge domains? How many clicks or reports do you currently navigate to find an answer to a single business problem or get relevant results to your search?Below, I will share a selection of real business problems that are able to be tackled more efficiently by using knowledge graph data models, as well as examples of how we have used knowledge graphs to better serve our clients.
A vast amount of the data we create and work with is unstructured, in the form of emails, webpages, video files, financial reports, images, etc. Our own wide assessment of organizations finds that as much as 85% of an organization’s information exists in an unstructured form. Organizing all of these data proves to be a necessary undertaking for many large and small institutions in order to extract meaning and value from their organization’s information. One way we have found to make this manageable is through leveraging semantic models and technologies to automatically extract and classify unstructured text to make it machine readable/processable. This allows us to further relate this classified content with other data sources to be able to define relationships, understand patterns, and quickly obtain holistic insights on a given topic from varied sources, despite where the data and content lives (business units, departments, systems, and locations).
One of the largest supply chain clients we work with needed to provide its business users a way to obtain quick answers based on very large and varied data sets. The goal was to bring meaningful information and facts closer to the business to make funding and investment decisions. By extracting topics, places, people, etc. from a given file, we were able to develop an ontology to describe the key types of things business users were interested in and how they relate to each other. We mapped the various data sets to the ontology and leveraged semantic Natural Language Processing (NLP) capabilities to recognize user intent, link concepts, and dynamically generate the data queries that provide the response. This enabled non-technical users to uncover the answers to critical business questions such as:
By allowing organizations to collect, integrate, and identify user interest and intent, ontologies and knowledge graphs build the foundations forArtificial Intelligence (AI)to allow organizations to analyze different paths jointly, describe their connectivity from various angles, and discover hidden facts and relationships through inferences in related content that would have otherwise gone unnoticed.
What this means for our large engineering and manufacturing partner is that, by connecting internal data to analyze relationships and further mining external data sources (e.g. social media, news, help-desk, forums, etc.), they were able to gain a holistic view of products and services to influence operational decisions. Examples include the ability to:
Most organizations have accumulated vast amounts of structured and unstructured data that are not easy to share, use, or reuse among staff. This difficulty leads to diminished retention of institutional knowledge and rework.
