In today’s world, data is generated continuously by various systems of companies. However, even after investing billions of dollars in digital transformation, many companies struggle to use their data to improve their services and gain competitive advantage. Artificial Intelligence (AI) was introduced to help companies make sense of their data, but many have failed in their AI initiatives. This is because companies’ data infrastructure is not ready for AI. In order to benefit from AI, companies need to build an ontology, which is a comprehensive characterization of the architecture of all their data.
An ontology is a consistent representation of data and data relationships within a business, a model of all the elements that go into and connect various information systems. This includes products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content, and data of all types. The ontology is the master knowledge scaffolding of the organization. Without a consistent, thoughtful approach to developing, applying, and evolving an ontology, AI systems can only develop in a piecemeal, fragmented way.
Creating an ontology requires a cohesive approach that brings together all of a company’s data. This enables AI systems to be smart enough to make an impact. An ontology is at the heart of the information design of the AI-powered enterprise, an investment that will continue to pay off as AI becomes more pervasive.
An example of a company that developed an ontology is Applied Materials. The company worked with semiconductor manufacturers to fix problems that slow or halt production at semiconductor plants. Technicians spent up to 40% of their time searching for answers, and they tended to hedge their bets by stocking their service vehicles with a variety of costly components, tying up tens of millions of dollars in inventory. To solve this problem, Applied Materials needed a way to organize the diverse sources of information available to the technicians and a way to integrate them into a single interface.
The ontology created for Applied Materials included all the multiple vocabularies, relationships, and hierarchies in all the systems the technicians used. The ontology defined relationships for the short names one system used to refer to a part and the stock number another system used to refer to the same part. The company parsed and classified troubleshooting documents with “text analytics,” a method that learns from a model document, then extracts information from similar documents, and makes all the knowledge accessible with a common language. Once the ontology was created, Applied Materials incorporated it into multiple systems and processes, applied it to existing documents, incorporated it into workflows for new documents and solutions, and connected it to ERP and digital asset management systems. The resulting system reduced the time technicians spent searching for information by half, and Applied Materials estimated that the value of that savings was tens of millions of dollars per year.
Building an ontology may seem like a daunting task, but the benefits are worth it. Companies can start by identifying the data they have, its structure, and where it’s located. They can then create a data map that shows how the data is related to each other. After this, they can begin to build their ontology by defining terms, relationships, and hierarchies. It’s also important to make sure the ontology is accessible to everyone in the organization and to keep it up to date.
Conclusion
Building an ontology is crucial for companies that want to benefit from AI. It enables AI systems to be smart enough to make an impact and provides a consistent, efficient experience for employees. Creating an ontology may seem like a daunting task, but the benefits are worth it in the long run. Companies that invest in building an ontology will be able to use their data to improve customer service, reduce costs, and gain a competitive advantage.
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