October 18, 2005
Knowledge Management Lives!
Good news for knowledge management fans. In the latest Bain & Company survey of management tools, knowledge management has prospered somewhat. It’s not quite as popular as, say, strategic planning (the perennially most popular management tool in the Bain survey), but it’s moving up. 54% of companies in the survey said they used knowledge management, which was exactly the mean level of usage for 25 different management tools. It’s more likely to be used than such tools as EVA, loyalty management, and Six Sigma. Knowledge management was below the mean in satisfaction levels among companies that have used it, but its satisfaction level is improving over the 2003 survey, when it had the lowest satisfaction scores of any tool. Loyalty management has replaced KM at the bottom of the satisfaction ranks—and that’s still a good idea—so I’m not worried about knowledge. You can download the full text of the study at www.bain.com/management_tools/home.asp
Further evidence of KM’s long lifespan comes from a “bibliometric” study by Mike Koenig, a professor at Long Island University, and Len Ponzi, a former doctoral student of his. They note that unlike a lot of management fads, the use of the term “knowledge management” in print media has not fallen over time but continues to rise—at least by 2003, when their study ended. Most new management notions have a brief lifespan, but KM keeps growing. Larry Prusak and I suggested that this was the case in our book What’s the Big Idea, but it’s nice to have some data about it.
October 14, 2005
6 Common Attributes of Knowledge Work and Knowledge Workers
The fact that knowledge workers primarily rely on their brains in their jobs rather than their bodies means that they have some attributes in common. These aren't terribly surprising, and they all follow from a few basic principles and observations, but they need to be stated. Most derive from the fact that knowledge work is less structured and perhaps less structurable than administrative or production work.
The basic principles and observations follow below:
1. Knowledge Workers like Autonomy
One important aspect of knowledge workers is that they don't like to be told what to do. Thinking for a living engenders thinking for oneself. Knowledge workers are paid for their education, experience, and expertise, so it is not surprising that they often take offense when someone else rides roughshod over their intellectual territory.
Of course, knowledge workers don't like for their work to be ignored, and there are some things they like to be told, such as the broader significance and implications of their tasks and jobs. But they'd generally like the details to be left to them.
This autonomy is in part a natural result of the nature of knowledge work. Since it's difficult to tell whether a knowledge worker is actually thinking at any given moment, supervisors pretty much have to take their word for it.
The knowledge worker also knows the circumstances in which he or she thinks best. If a computer programmer tells the boss that he is most productive working from 8PM to 4AM, smart bosses would try to facilitate that arrangement. The outputs of knowledge work are also difficult to specify in great detail, so that is generally left up to the worker.
2. Specifying the detailed steps and flow of knowledge-intensive processes is less valuable and more difficult than for other types of work.
This is a corollary of my first generalization about knowledge work.
Knowledge workers don't like to be told what to do, and they also don't like see their jobs reduced to a series of boxes and arrows.
Typically, when we want to improve performance we begin by breaking down the structure of the task into its constituent elements. This has been the case at least since Frederick Taylor's day, if not before. The idea is that when broken into piece-parts, knowledge work processes can be more easily followed and measured, and unnecessary steps eliminated altogether.
However, this approach often doesn't work very well for knowledge work and workers.
In my experience, knowledge workers will often resist describing the steps they follow in their work. The more complex and knowledge-intensive the work, the more likely this will be true. Perhaps there are so many variations that describing the typical flow of work is impossible. Knowledge work also often involves a high degree of iterative collaboration among knowledge workers, and this may be difficult to describe or model.
Even if you can get a knowledge worker to describe his or her work process, it may not be a very helpful description. First, the work flow may not be very similar to another worker's description of the supposedly same process.
Secondly, the steps may seem maddeningly inefficient: "First I come up with an idea. Then I think about it for a while. Then I talk to my lab partner about it. Then I think about the reactions she's given me." Such a process would be anathema to a stopwatch-packing Taylorist, but it's often how knowledge workers - particularly those involved in knowledge creation activities - work.
3. "You can observe a lot by watching." (Lawrence Peter Berra)
A natural follow-on to the previous attribute of knowledge workers is that if you can't get them to describe their work in detail, you have to observe it in detail. Systematic observation - also known as "shadowing" or "ethnography" - is often a way to better understand how knowledge workers do their work.
4. Knowledge workers usually have good reasons for doing what they do.
In the days of business process reengineering, we assumed that smart analysts could quickly figure out better ways of doing work. This was, in fact, often true. Nobody had ever thought about many administrative and operational processes before, and improvements were easily identified.
It's not so easy with knowledge work, which is one of the reasons why we have to observe it closely. Knowledge workers have typically thought about why and how they do their work, and have made many of the obvious improvements to it. There is probably a reason behind almost everything they undertake (or at minimum a logical rationalization).
If improvements are going to be identified, it's probably only after serious and deep study.
5. Commitment matters.
In the industrial economy, one could do a job with one's body even when the brain and heart weren't committed to the job.
But this isn't the case for knowledge work. It's unlikely that you'll get great performance out of a knowledge worker if he or she isn't mentally and emotionally committed to the job.
This fact has a number of ramifications. Chief among them is that knowledge workers need some say in what they work on and how they do it.
There is nothing that limits commitment like being told what to work on by someone else. This factor is behind, for example, the famous 3M approach of giving researchers 15% of their time to work on something they think is important to the company.
Obviously knowledge workers are generally willing to do some things that others ask (or even tell) them to do, but a degree of voluntarism helps a lot.
6. Knowledge workers value their knowledge, and don't share it easily.
Knowledge is all that knowledge workers have - it's the tool of their trade, the means of their production.
It's therefore natural that they would have difficulty relinquishing or sharing it in such a way that their own jobs might be threatened.
In the early days of knowledge management, when companies were beginning to talk about sharing knowledge within and across organizations, I used to say, "Sharing knowledge is an unnatural act."
I also mentioned that, "Of course, unnatural acts are committed every day."
Companies just needed to put the necessary incentives and assurances in place to ensure that people were willing to share their knowledge.
But that's the subject of another post, or you could read my book.Posted by Tom Davenport at 04:09 PM | Permalink | Comments (1) | TrackBacks (0)
October 11, 2005
I just wrote a short piece for CIO on business analytics.
One of the question I ask is - how do you know when you're there? In the article, I point to several indicators that a company is competing on analytics:
* The CEO has an analytical background. Harrah's Loveman was a business school professor and has an MIT PhD. Amazon's Bezos was an A-plus student in electrical engineering and computer science at Princeton. When the CEO or vice chair of a company is a rocket scientist, it's a good bet that there will be other scientists on the payroll.
* Nobody's asking about the ROI for each little initiative. What's at stake in analytical competition is not an application, but a corporate strategy. If the analytical activities are succeeding, they will be manifested not in ROI calculations, but in revenue and profits.
* The company is very successful. Certainly there are industries (for example, U.S. domestic airlines) where a lot of analytics don't seem to be the critical success factor. It isn't with Southwest. But the great majority of highly analytical companies that we studied are leaders in their industries and making lots of money.
As more analytically trained managers enter the workforce, it's likely that analytical competition will become more common and intense. However, this capability can't be developed overnight.
Most companies took at least five years to develop their analytical capabilities sufficiently to compete on that basis, and a couple of very successful companies (including Procter & Gamble and Mars) had been pursuing analytics for several decades. Assembling the right data, finding and using the right tools, and developing the right relationships between analysts and decision-makers all take time. Therefore, it makes sense to start pulling them together now.
History seems to be on the side of the numbers.Posted by Tom Davenport at 10:31 AM | Permalink | TrackBacks (0)