Everything that can be measured, will be.

I was introduced to the concept that “everything that can be digital, will be” in  Nicholas Negroponte‘s 1995 book Being Digital.

While a read of that book today will be humorous for the pantheon of companies that self-immolated in the big dot com blowout of the early naughts, the concepts introduced have pretty much all held.

Lately, I’ve been mulling over a corollary to “everything that can be digital, will be” that is making itself apparent: Everything that can be measured, will be. As human interactions become increasingly digital, there are–by default–data points being generated on all of our interactions.

Social technologies are accelerating the availability of data and the kinds of data available to measure.

Measured: physical presence

Mobile technologies make it fairly simple to measure where someone is using GPS and WiFi. As people are typically more guarded about their location, the incentives required to get people to volunteer that data are trickier.

However, arbitrary social incentives (such as Foursquare’s badges and “mayor” status) and real social incentives (knowing where your friends are so you can join them) break down the barrier to volunteering location.

In addition, commercial endeavors like deal services provide a “rational” reason to volunteer physical location for measurement.

Measured: relationships

Facebook, Twitter and any other social networks out there combine these data points to measure our “social graph.” Our relationships are being measured. There’s even a metric: “friends” or “followers.”

(As an aside, the above video is from 2006–any changes in social media since then?)

Measured: sentiment

As we volunteer information on sites like Twitter, our sentiment about a variety of topics can be measured. Since people tend to like to have their tastes and preferences about some things known, voluntary expressions of sentiment are not especially difficult to gather.

Existing methods of sentiment analysis are certainly rough around the edges. But an entire field, text analysis, is embracing this problem. I would expect our ability to measure sentiment in a meaningful fashion will increase significantly in the coming years.

Efforts are under way in medium to large organizations to quantify and qualify sentiment analysis into meaningful metrics.

The disruption of being digital

There were a variety of responses in the late 90s, as industries began to realize the disruption potential of statements like “everything that can be digital, will be.”

Some organizations stuck their head in the sand and waited for the fad to blow over. Some organizations blindly thrashed about with digital technologies until they either learned new skills or expired. Some began to make use of legal power while others worked to innovate around legal power.

That first wave of the web, getting everything that could be digital to actually be digital, was disruptive. It left more than one large, well-established industries in disarray.

As organizations and industries have adapted to being digital, new skills have been learned. New language, technology and strategies have been added to the skillsets of employees and business leaders.

However, this “digital revolution” was primarily a workflow and distribution disruption. Learning how to create things for a digital world and how to distribute things for a digital world.

The disruption of measure

I suspect that, like being digital, there will be disruption associated with being measured. There will be new skillsets that will allow organizations to prosper or continue long enough to adapt to changing competitive pressures. These skillsets will not be limited to departmental silos, specific layers of corporate hierarchy, or outside vendors.

Just as all people engaged in business today need at least a foundational level of internet understanding, we’ll see the requirement of a foundational level of measurement understanding.

The nature of the disruption posed by a “measurement revolution” requires more than simply learning new processes and manipulating new toys and infrastructure. Learning to understand data and derive meaning from it is a more involved task that requires a different level of cognition.

It will also require different human skills for living in an environment of constant change and overwhelming specific data.