#globaleconomy
Knowledge Asset Management (KAM) is a 21st Century business process that curates and valuates the knowledge base of a community within the Global Village.
Photo by Harry Cunningham on Unsplash
In the era of the Industrial Park, the value of a business rested primarily in material assets. Although intangible assets, such as brand recognition and market share, played a role in determining the worth of the business, there was no objective way to measure that worth accurately. It was lumped under "goodwill" and priced at whatever number might satisfy a typical buyer in the marketplace.
This obsolete pricing method, although it is still a Generally Acceptable Accounting Practice, simply will not work in the Global Village. Now the potential buyer may be miles apart from the seller in both distance and culture. Because there is currently no easy way to measure the value of the contributions of knowledge workers who now populate the Industrial Park, their value is often dismissed in a sale which pleases owners and shareholders but leaves these workers holding an empty bag once filled with Pension Funds, Income Streams, and Achievable Dreams.
A primary initiative of Global Village residents is to introduce and promote the principles of KAM as a lifeline to save the displaced Industrial Park workers from drowning in this sea of change. KAM extends the notion of "good will" into an accounting structure that uses Generally Acceptable Accounting Practices (GAAP) to attribute real value to abstract assets such as the tacit knowledge of an Individual.
KAM valuation and reporting is of particular relevance to knowledge practitioners whose lifeline is the potential value of their intellectual property, social media presence and the tacit knowledge of the workforce.
Global Village Residents are dedicated to
serving developers worldwide, enabling them to build exceptional
data-driven products that measure everything that matters: software
applications, industrial equipment, financial markets, blockchain
activity, consumer behavior, machine learning models, climate change,
and more. Analyzing this data across the time dimension (“time-series
data”) enables developers to understand what is happening right now, how
that is changing, and why that is changing.
