by Joe Campbell, RAVN
10 March 2020
Knowledge management (KM) has always been an essential aspect of a well-run enterprise, but there isn’t just one type. Different styles of KM – and even regional variations – have emerged over the years.
In the beginning, there was traditional KM. This was focused on organizing knowledge within the enterprise, mapping out processes, and documenting best practices.
In the past decade or so, businesses entered an “innovation era,” bringing on board specialist teams and process improvement experts, creating innovation hubs, and willingly embracing AI and other cutting-edge technologies.
Out of this innovation era, we have seen the emergence of a new type of KM that we might call analytical KM. Analytical KM taps into the data that is naturally created as an output of these innovative processes that have been put in place to enable data-driven decision-making.
Analytical KM in action
Consider an organization that manages a property portfolio. Traditional KM would map out the process and suggest innovative ways of executing that process, potentially using existing knowledge to remove some of the negotiations involved.
Analytical KM, meanwhile, would use AI to review and extract data points from the property leases to identify opportunities. If the leases are in a high rise, and 50 of the leases are coming up for renewal in the next 3 months, the organization can take a proactive role in either continuing or discounting those leases based on what the data is showing. That’s the kind of data-driven decision-making analytical KM enables.
This same data-driven approach can be used to efficiently tackle the upcoming LIBOR rate change. Many organizations have hundreds – or thousands – of contracts that are tied in some way to LIBOR. Using AI enables these organizations to find all of the contracts that are tied to the LIBOR rate and calculate the potential impact, so that the organization can take appropriate next steps.
The act of collecting know-how also reveals the difference between traditional and analytical KM. The traditional KM approach is to collect this know-how by asking people within the organization to state their areas of expertise or sending out email requests for the best example of a particular type of document. Analytical KM uses AI to automatically surface experts in a particular specialty area or to find the best example of a knowledge document housed within a document management system.
Knowledge graphs are at the heart of this analytical process. A knowledge graph is a technology that links data together, looking at the connections between different data points – for example, what projects a particular person within an organization tends to spend most of their billable time on (which can identify expertise), or how many different people have downloaded and used a particular contract or template (which can identify the best knowledge document to use).
If this concept sounds familiar, it’s because companies like Netflix and Amazon use knowledge graphs every day to curate and make recommendations based on the connections between different pieces of data. Consider it analytical KM on an industrial scale.
Regional differences, similar goals
In addition to these different styles of KM, there are slight regional variations. In the UK, knowledge management is dominated by creating content and know-how, whereas the United States is more technology and search focused. Put another way, the UK tends to actively document knowledge within the organization, whereas the U.S. tends to search and uncover knowledge within the organization.
In all cases, however, the goal is the same: to actively use knowledge to improve the way the organization conducts business. Data-driven decision-making will become the norm over the next 3-5 years, which means that analytical KM will only grow in importance. Embracing this new approach to knowledge management will allow businesses of all types to better use the data within the organization to uncover insights that drive change, create efficiency, and identify opportunity.
Joe Campbell is product manager for iManage RAVN, an artificial intelligence platform that powers a number of applications to automatically organize, discover and summarize documents.