Emerging technologies have been significantly impacting businesses and reshaping various industries in many different ways. With everything from automation and artificial Intelligence (AI) to data analytics and Internet of Things (IoT), businesses must adapt and embrace these technologies responsibly to leverage their full potential effectively.

By combining AI capabilities such as natural language processing, knowledge graphs, entity recognition, and personalization, semantic search engines can better understand user intent, analyze the semantics of content and deliver more accurate and contextually relevant search results. This collaboration enables users to find information more efficiently and enhances their overall search experience.

Leveraging technologies like big data analytics, organizations can enhance decision-making, gain a competitive advantage, and drive overall business success. Businesses that are open to embracing emerging technologies often gain a competitive advantage, improved efficiency, and enhanced customer experiences. It’s essential for business leaders to carefully evaluate the potential benefits and risks and develop a well-thought-out strategy for technology adoption to stay relevant in today’s fast-paced business landscape.

Content without access, however, is relatively worthless. Enterprise search is how an organization brings information to the surface, in any format and from anywhere inside the company.

A custom taxonomy enables faster search, and a full analytics suite, applied to the same data, makes it possible to perform analysis, statistical forecasting, and maintenance.

Taxonomies provide consistency in terms and categories to enable findability in content. This is true regardless of the subject.

A taxonomy is typically a controlled vocabulary with a hierarchical structure, with the understanding that there are different definitions of hierarchy. Taxonomies are often displayed as tree structures. Controlled vocabularies are used to ensure consistent indexing. They do not necessarily have any structure or relationships between terms within the list.

While these emerging technologies offer numerous benefits, they also present challenges, such as privacy concerns, cybersecurity risks, and potential job displacement.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.