Organisations are developing and storing more institutional knowledge content than ever before. As a result, findability is an increasingly common issue. Employees struggle to find information within their silos, not to mention information created by colleagues in other silos.
Research indicates that 26% of an employee’s salary (£15,261.65) is spent searching for information*. This inefficiency is compounded by the time lost when people abandon their search and recreate the content instead.
As we continue to add new institutional knowledge to that pool of enterprise content, it becomes increasingly important to ensure the content can be retrieved when needed.
Giving employees fast access to the institutional knowledge they need
By layering machine-readable meaning and context on top of our institutional knowledge, we can enable systems to retrieve the content we need.
From raw data to insight: what is a semantic data layer?
This layer of context and meaning is a semantic data layer. It involves tagging content with metadata to provide systems with enough information about the content, to find, understand, and reuse the information more reliably.
Because the amount of meaning and context needed depends on what we want the content to be able to do, we can think of this semantic data layer in terms of a spectrum.
At one end of the spectrum, we see organisations that don’t control the terms that are used within the company at all:
- Individuals use terminology as they see fit.
- If people add metadata to their content at all, it’s as inconsistent as the individual users.
As a result, it’s hard for individuals to find content within their own silo. It may even be hard for them to find content they created themselves because the term they used when creating the document last year, isn’t the same search term they are using now.
At the other end of the spectrum are organisations that have invested in a knowledge graph, where employees can:
- Search in a single user interface.
- Get a unified view of different kinds of unconnected data sources.
- Get answers to complex questions instead of getting references to documents on a particular topic.
Unlocking fast access to information with a centralised taxonomy
The tipping point for organisations to improve findability is moving to a centrally managed taxonomy. At this point on the spectrum, because the terms used are managed centrally at organisation level, any metadata used to tag content is consistent across all silos.
This is also the point at which synonyms are systematically recorded in the taxonomy alongside preferred terms. As a result, employees can search using the terminology specific to their silo and still access related content created in other silos, regardless of the terms they use. This also makes it easier for people to collaborate across functions and teams.
Once a centrally managed taxonomy is in place, the preferred terms defined in the taxonomy are used to tag institutional knowledge content.
The outcome: greater collaboration, accuracy, and productivity
- Improved findability: A semantic data layer allows users to find the right content more easily by understanding the contextual meaning and intent behind search queries. This results in less time lost to searching, quicker decisions, and a boost in both productivity and employee morale.
- More relevant results: A centrally managed taxonomy facilitates consistent tagging, leading to more accurate and trustworthy search results.
- Enhanced collaboration: A centrally managed taxonomy connects business units, enabling knowledge to flow across the enterprise and reduces reliance on individuals for information.
Using context to future-proof your institutional knowledge
A centrally managed taxonomy and machine-readable context can greatly enhance the findability of institutional knowledge. By associating synonyms with preferred terms and layering consistent tagging on top of content, employees can find the relevant content they need, regardless of the terminology used across silos. In addition to improving findability, this approach fosters a more connected, collaborative, and efficient working environment.
Ready to find out what machine-readable context can do for your institutional knowledge? Get in touch today.
*Unlocking Information in the Knowledge Economy, OBRIZUM, 2023
The average annual cost of one employee searching for relevant information is £15,261.65. 26% of this person’s salary.
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