learning – Looking Forward

October 5, 2017

The Rebirth Of The Learning Organization

Among the more ambitious and expansive CEOs, there’s a special kind of holy grail – transforming their organizations into learning organizations. Jack Welch, former and famous CEO of GE, put it this way in the 1990s:

“an organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive business advantage.”

The Business Dictionary defines a learning organization as an

“Organization that acquires knowledge and innovates fast enough to survive and thrive in a rapidly changing environment. Learning organizations (1) create a culture that encourages and supports continuous employee learning, critical thinking, and risk taking with new ideas, (2) allow mistakes, and value employee contributions, (3) learn from experience and experiment, and (4) disseminate the new knowledge throughout the organization for incorporation into day-to-day activities.”

Or as Peter Senge, one of the founders of the learning organization movement, has famously said in 1990:

“A learning organization is an organization that is continually expanding its capacity to create its future.”

As you can see, this dream started more than 25 years ago.

By the first decade of this century, however, Mark Smith wrote:

“[W]hile there has been a lot of talk about learning organizations it is very difficult to identify real-life examples.”

The companion in thought of the learning organization movement was the knowledge management movement. Its goal was to capture, organize and distribute knowledge among the members of an organization.

But, in his 2014 paper, “A Synthesis of Knowledge Management Failure Factors”, Alan Frost was already conducting autopsies for the failure of knowledge management initiatives in many organizations.

In some ways, this reminds me of the first great wave of Artificial Intelligence in the 1980s when a lot of effort went into trying to codify the knowledge of experts into expert systems by extensively questioning them about their decision processes. It turns out that it is hard to do that.

Often experts – the knowledgeable ones – can’t really articulate their decision rules and to make matters worse those rules are at times probabilistic. Like other humans, experts often seek to develop rubrics to simplify a problem, which unfortunately can limit their ability to continue to observe what’s happening. Human perception, in general, in an imperfect instrument.

Thus, even if an organization is successful in widely distributing the knowledge developed by its staff, it may be just propagating ideas that are, at best, only partly true.

All of these factors, and many others, has slowed down the march to the dream of learning organizations.

But now we are possibly at the beginning of a rebirth of the learning organization.

What’s different today? Analytics and big data make the process of organizational learning much easier and better. Indeed, it is perhaps time to add analytics as a sixth discipline to Senge’s Five Disciplines of a learning organization.

After all, it’s not that people don’t know things that are important to an organization’s success – it’s just that they don’t know everything that is important and they can’t or won’t keep up with the torrent of new data vying for their attention.

The traditional gathering of the human knowledge combined with the continuously improving analytics models can achieve the dream so nicely stated by the executives and visionaries of twenty-five years ago.

For example, instead of trying to interview experts at length to capture their knowledge, today, someone in analytics would prefer to review the thousands of cases where the characteristics of the case, the decision by an expert and outcome was known. Then some kind of machine learning would search for the underlying patterns. In that way, the expert’s tacit understanding of the world would arise out of the analytics.

Nor does this cut out experts in the knowledge acquisition process. It just changes their role from being a memoirist. Instead, the experts can help kick off the building of the model and even assist in interpreting the results of the analytics.

Once the learning has begun, there is still much to learn (no pun intended) from the pioneers of this field. While they had great difficulty obtaining the knowledge – feeding the learning organization – they knew the rules of distributing that knowledge and making it useful. That is a lesson that today’s folks specializing in analytics could learn. Among these:

The importance of organizational culture
Leadership interest and support – especially for open discussion and experiment
Measurement (which would certainly provide grist for the analytics mill)
Systems thinking

For an illustration, see “Evidence in the learning organization” from the National Institutes of Health, in which these issues are focused on the medical profession.

If a marriage of the learning organization and knowledge management movements with the newer focus on analytics takes place, then all of those fields will improve and benefit.

June 6, 2017

The Next Level: Communities That Learn

This week is the annual summit of the Intelligent Community Forum, where I’m Senior Fellow. Although there are workshops and meetings of the more than 140 intelligent communities from every continent, the events that draws the most attention are the discussions with the Top7 of the year and the ultimate winner.

These intelligent communities are leaders in using information technology and broadband communications for community and economic development. They represent the next level up from those cities which label themselves “smart” because of their purchases of products from various tech companies to manage the infrastructure of their cities – like street light management.

But intelligent communities should not to be satisfied with merely going
beyond vendor-driven “smart city” talk and they should instead
ascend

to the next level – create a community that is always
learning.

For a bit of background, consider the efforts over the last two decades to create learning organizations – companies, non-profits and government agencies that are trying to continuously learn what’s happening in their markets or service areas.

The same idea applies to less structured organizations, like the community of people who live and/or work in a city.

It’s worth noting, that unlike many of the big data projects in cities, this is not a top-down exercise by experts. It’s about everyone engaging in the process of learning new insights about where they live and work. That volunteer effort also makes it feasible for cash-starved local governments to consider initiating this kind of project.

In this sense, this is another manifestation of the citizen science movement around the world. Zooniverse, with more than a million volunteer citizen scientists, is probably the best example. Think Zooniverse for urban big data.

There are other examples in which people collect and analyze data. Geo-Wiki’s motto is “Engaging Citizens in Environmental Monitoring.” There’s also the Air Quality Egg, a “community-led air quality sensing network that gives people a way to participate in the conversation about air quality.”

Similarly, there’s the Smart Citizen project to create “open source technology for citizens political participation in smarter cities”, that was developed in Fab Lab Barcelona. The Sensible City Lab at MIT has even equipped a car for environmental and traffic safety sensing.

Drones are already used for environmental sensing in rural areas, but as they become a bit safer and their flight times (i.e., batteries) get better, they will be able to stay up longer for real time data collection in cities. A small company in Quebec City, DroneXperts, is already making use of drones in urban areas.

Indeed, as each day goes by, there is more and more data about life in our communities that could be part of this citizen science effort — and not just environmental data.

Obviously, a city’s own data, on all kinds of topics and from all kinds of data collection sensors, is a part of the mix. City government’s even have information they are not aware of. Placemeter, for example, can use “public video feeds and computer vision algorithms to create a real-time data layer about places, streets, and neighborhoods.”

There are non-governmental sources of data, like Waze’s Connected Citizens exchange for automobile traffic are also available.

Sentiment
data from social media feeds is another source. Even data from
individual residents could be made available (on an anonymous basis)
from their various personal tracking devices, like Fitbit. For
background, see John Lynch’s talk a year ago on “From Quantified Self to Quantified City”.

Naturally,
all of this data about a community can be an exciting part of public
school classes on science, math and even social studies and the arts.
Learning will become more relevant to the students since they will be
focused on the place in which they live. Students could communicate and
collaborate with each other in the same or separate classrooms or across
the country and the world.

The Cities of Learning
projects that started in Chicago a couple of years ago, which were
primarily about opening up cultural and intellectual institutions
outside of the classroom for K-12 students, were good, but different
from this idea.

So “communities that learn” is not just for students. It is a way for adult residents to achieve Jane Jacob’s vision
of a vibrant, democratic community, but with much more powerful and
insightful 21st century means than were available to her and her
neighbors decades ago.

January 5, 2016

Analytics And Organizational Change

The accumulation of data is increasing fast – from wearables, the widespread deployment of sensors in physical locations and the ever increasing use of the Internet by people.

And someone somehow has to figure it all out. As a result, data scientists are in demand and analytics is a hot new field of study. On top of long standing statistical methods, there has been impressive progress recently in machine learning, artificial intelligence and new computer system architectures.

Yet, the use of analytics itself has not had as great an impact on many organizations as the data scientists have hoped. Some of the failures of analytics were really failures of implementation.

Perhaps the most public of these was the great Netflix million-dollar prize for a new recommendation engine. From a purely technical viewpoint, the winning team did exactly what was asked for – create a significantly better engine than the one Netflix had been using. Nevertheless, Netflix ended up not using their work. That’s an issue of implementation and integrating the product of analytics into an organization.

Being able to predict behavior or even generate new insights from all this data is one thing. As with Netflix, having people and organizations actually use that knowledge is another. Like many other new technologies, adoption is as much a question of managing change as it is developing the technology itself.

This surely bothers some data scientists. After all, they have a better mousetrap – why aren’t people using it? Being able to think quantitatively, they can prepare quite convincing business cases with impressive ROI statistics and yet even that isn’t enough to get executives to budge. But changing an organization isn’t simple no matter how good your arguments.

Despite this background, there has been very little overlap between the courses that prepare data scientists and the courses that prepare change agents in organizations.

Later this year, I’ll be doing something to help align these two fields to improve the success of both. I’ll be teaching an online course on analytics and leading change. It will be part of Columbia University’s new executive graduate program in Applied Analytics.

We’ll be reviewing what is known about successfully introducing changes into an organization from the classics on the subject that were written as much as twenty years ago to more recent research. The course will, of course, help its students understand how to get an analytics initiative started. More important, it will focus on how to sustain, over the long run, both analytics and the changes it informs.

Thinking about the long run, there are three facets of the relationship between analytics and organizational change.

The use of analytics as a part of everyday decision making and the rules of operation in a business – this is the obvious first thing everyone thinks of.
The use of analytics to help better implement the changes that its insights imply – a kind of a meta-analysis.
The continuing interaction between analytics and change to finally achieve the long desired goal of an organization that learns how to continually optimize itself – this is something of great strategic value to any business.

As the course develops, I’ll be posting more about each topic.