Blogs como herramienta empresarial - 2ndo Encuentra Nacional de Bloggeros
by Luis Benitez
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La presentación que hice como parte del 2ndo Encuentro Nacional de Blogueros, en Caguas, PR el 8 de mayo de 2010. La presentación duró 40 minutos.
La presentación que hice como parte del 2ndo Encuentro Nacional de Blogueros, en Caguas, PR el 8 de mayo de 2010. La presentación duró 40 minutos.
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IBM
This means that we need to give employees a voice and way to express their ideas, develop them and then the organization can capitalize on them.
The adage led us to wonder if there might be an equation that captures the effect of knowledge management, combining the power of knowledge with the power of a knowledge-sharing network.
We start with two familiar ideas.
The first is Metcalfe's Law (described in C. Shapiro and H.R. Varian, Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, Boston, 1998).
If there are n people in a network, and the value of the network to each of them is proportional to the number of other users, then the total value of the network (to all users) is proportional to n • (n - 1) = n2- n ≈ n2, for large n.
The second is knowledge is power.
Let's first see what happens if we assume knowledge accumulates additively in a knowledge-sharing network. In this case, if each person in a network knows k facts that are disjoint from those known by all others, and if this knowledge (sets of facts) is shared among s members of the network, then each member knows k + (s - 1) • k = s • k total facts.
If knowledge is shared among all members, then s = n and each member knows n • k total facts. Therefore, the "power" of the network p = n2 • k. This is roughly Metcalfe's law with a knowledge multiplier.
However, the above equation does not account for the capacity of the network to generate new knowledge.
To address this, let's see what happens if we assume knowledge accumulates multiplicatively. Then, in the case where each member shares knowledge with s other members, each member knows ks. As a result, the power of the network p = n • ks.
However, this is an overestimate because it does not account for overlap in the knowledge of each individual member. If we assume that the overlap is proportional to the number of members, we get p = n • ks / n or p = ks