The terms “findability” and “discoverability” may seem to refer to similar things. However, these concepts are very different, and both are key outcomes that should be considered in any comprehensive knowledge management strategy.
Content without access is relatively worthless. Enterprise search is how an organization helps people seek the information they need, in any format, from anywhere inside their company. This includes databases, document management systems or even information on paper. It’s about getting the right information at the right time.
Findability is the ease with which information can be found. It means that users can easily find content or information they assume is present on a website. A good knowledge management strategy also promotes discoverability, which involves making sure that new content or information can be found, even if the user doesn’t know that it exists yet.
Searching for any type of information can be frustrating if you are unsure where to start or how to define your query. This is true in search engines and public databases.
While not a small task, online searching does make it easier. Searching is more than just typing something into a search box and getting a result. It’s more about discovering things about a topic that you didn’t necessarily know you were looking for.
One way to ensure findability is with a custom taxonomy. Taxonomies exist in every industry from science to information management to healthcare. Taxonomies provide consistency in terms and categories to enable findability in content. This is true regardless of the subject.
And if you already have a taxonomy? Whether you constructed your taxonomy six months ago or six years ago, it might be time for a change. As your taxonomy decays, it can exact a painful tax on your organization’s brand consistency, efficiency and making content findable.
For these types of projects, professionals should look for an experienced builder of solid standards-based taxonomies to associate content for appropriate machine-assisted indexing.
Most organizations have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. 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 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
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.