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The ability to measure the effectiveness of your Knowledge Management (KM) program and the initiatives that are essential to its success has been a challenge for all organizations executing a KM Program. Capturing the appropriate metrics are essential to measuring the right aspects of your KM Program. The right metrics will facilitate a clear and correct communication of the health of the KM program to your organization’s leadership. In this post, I will identify metrics (or measurements) of four key initiatives of most KM Programs.

“Nearly half of HR and business leaders who were surveyed believe many of their core HR functions will be automated by 2022.” (Source: Harris Poll).

As we enter the new year, KM Institute begins our 17th year of offering the flagship Certified Knowledge Manager program.  Well over 8,000 students have now taken our courses globally.  We have expanded our global reach with public and private courses conducted in West Africa, Europe, India and the Middle East, in addition to the United States.  New partnerships are forming in these regions as well as South America and South Africa.

Information Architecture is an enabler for Big Data Analytics. You may be asking why I would say this, or how does IA enable Big Data Analytics? We need to remember that Big Data includes all data (i.e., Unstructured, Semi-structured, and Structured). The primary characteristics of Big Data (Volume, Velocity, and Variety) are a challenge to your existing architecture and how you will effectively, efficiently and economically process data to achieve operational efficiencies.

When is a company both a brand-new startup and an established and mature company at the same time?

In November 2016, Johnson Controls spun out its auto seating business, creating a new, wholly independent company called Adient.  Adient was born as a fully mature company, with established clients, operations, and market share.  But Adient was also born with a strong desire create a new corporate culture of information sharing and collaboration.

Ensuring the adoption of new knowledge management programs, systems, and tools requires thorough planning well in advance of actually launching a new initiative. It also takes an agile approach to designing your solution so that you can adapt what you deliver based on what your employees truly need to help them get their job done.

In this presentation, you’ll learn how to develop, refine, and execute the following critical plans, which will ultimately maximize employee engagement with the “new way of doing things.”

Effective knowledge sharing is the lifeblood to knowledge-based organizations. High-performing organizations are ones that have mastered the art of empowering their newer employees with the knowledge and experience of the more-tenured, highlighting new ways of thinking and doing, and fostering a broad set of avenues for knowledge to flow up, down, and sideways throughout the organization.

Take a moment to remember how you felt the last time you were surprised at work with an announcement of a big change. No, really – pause for a moment and think about it.

In part one I examined the connection of KM and AI and how this connection has led the way for cognitive computing; while in part two I examined those industries that will or are soon to be disrupted by Cognitive Computing.  In this post I will examine those technologies that will lead in the disruption brought to many industries by the way of cognitive computing.

Similar to the old adage, “you can lead a horse to water, but you can’t make him drink,” you can deliver a solution that uses the most cutting-edge technology and beautiful design, but you can’t guarantee that your stakeholders will embrace it. This blog offers practical tips on how to maximize and measure user adoption to ensure that your new tool or process is fully embraced by those for whom you’ve designed it.