I recently pointed to Bob Sutton's new blog as a good source of insight into the world of effective organizations.  One of his recent posts, Crappy People versus Crappy Systems, offers an excellent case in point. The entire post is well worth your time, but here is the essence:

 The worst part about focusing on keeping out crappy people, however, is that it reflects a belief system that “the people make the place.” The implication is that, once you hire great people and get rid of the bad ones, your work is pretty much done. Yet if you look at large scale studies in everything from automobile industry to the airline industry, or look at Diane Vaughn’s fantastic book on the space shuttle Challenger explosion and the well-crafted report written by the Columbia Accident Investigation Board , the evidence is clear: The “rule of law crappy systems” trumps the “rule of crappy people.”

Sure, people matter a lot, but as my colleague Jeff Pfeffer puts it, some systems are so badly designed that when smart people with a great track record join them, it seems as if a “brain vacuum” is applied, and they turn incompetent. Jeff often jokes that this is what happens to many business school deans, and indeed, these jobs have so many competing and conflicting demands that they are often impossible to do well.

Bob Sutton: Crappy People versus Crappy Systems.

I've worked in a number of organizations that do an excellent job of hiring great people, including successful startups. Sutton's post finally puts a finger on my central frustration in these organizations; they too often tolerate crappy systems that pull down the performance and potential of the great people they manage to attract.

I suspect this is partly a function of the wrong design emphasis. When you know in advance that your organizational systems must work regardless of your ability to attract the best and the brightest, you invest the time and energy to make those systems robust. If you go down the "hire the best" path, you give yourself license to under-invest in systems. Perhaps more harmfully, you don't take the time to design the organizational systems that might actually amplify the quality and capabilities of a superior workforce.

One of my colleagues at work recently asked which bloggers I might recommend that also deal with the future of work and the changes technology continues to elicit in organizations. His question was well-timed as there are several fine thinkers who have taken to blogging in the last several months that have much to add to this ongoing conversation.

I've previously mentioned John Sviokla (Sviokla's Context) and Espen Andersen (Applied Abstractions) who were both colleagues at Harvard. There are three other academics/ex-academics who I find particularly cogent on the topic of managing and leading knowledge-based organizations.

David Maister created and taught a course on managing service-based operations during my MBA days; an area that has since grown to become one of the major organizing themes of the curriculum there. Since then, David has gone on to become one of the pre-eminent consultants to professional services organizations. He is blogging at Passion, People and Principles. Although his ostensible focus in on services organizations, the challenges they face make them a laboratory for the kinds of knowledge work issues that all organizations will face. David is also the author of several of the best books on consulting and professional services, including The Trusted Advisor and True Professionalism : The Courage to Care About Your People, Your Clients, and Your Career.

Another Harvard blogger is Andrew McAfee who teaches in the technology and operations management group, which has become the home of the most robust thinking about these topics at Harvard. His blog title, The Impact of Information Technology (IT) on Business and Their Leaders, lacks a bit in the snappy department, but the content is first rate. Recently, he has been leading the charge to map out and define the notion of Enterprise 2.0 in places as diverse as the Sloan Management Review and wikipedia.

Over on the West Coast at Stanford, we have Bob Sutton the author of several excellent books, most recently  Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Management. His blog is Work Matters. Sutton has been collaborating with Jeff Pfeffer, also of Stanford, to promote the practice of evidence-based management, which, like evidence-based medicine,

means finding the best evidence that you can, facing those facts, and acting on those facts – rather than doing what everyone else does, what you have always done, or what you thought was true. It isn’t an excuse for inaction. Leaders of organizations must have the courage to act on the best facts they have right now, and the humility to change what they do as better information is found. [Evidence-Based Management]

My latest column at Enterprise Systems Journal appears today. In it I take a look at the notion of developing an ongoing learning agenda by focusing on the boundaries of your ignorance. One key graf:

The late Isaac Asimov once observed that "the most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but 'that’s funny'." What piques your curiosity is an excellent indicator of where your learning energies ought to be focused. Curiosity is an edge phenomenon where new inputs have enough structure and content from your perspective to emerge as something more than background noise and chaos, yet are not so well-defined as to be immediately classifiable. Becoming more mindful of the terminology, issues, and phenomenon that are separating themselves from background noise helps identify topics you should consider investing learning time in. [IT Learning at the Boundaries of Your Ignorance]

I started thinking about "boundaries of ignorance" and "circles of knowledge" while putting together a presentation on learning and knowledge management. I began with the simple notion of learning as expanding your circle of knowledge and quickly hit on the notion that expanding my circle of knowledge was simultaneously expanding the boundaries of my ignorance. The more things I learned, the more things I became aware of that I didn't know.

In my teens, that manifested itself as reading everything I could lay my hands on in the quest for the "one right answer." I wasn't as smart as Alan Kay to realize early on that books had limits or could be wrong. I was so engrossed in the world that books opened up for me that it took quite a while to grasp their limits. My dad used to say that he could always tell when I had finished a book by my fervent belief in some new world view. In retrospect, I suspect my ignorance was growing faster than my knowledge. But I was more focused on the inside of the circle than on its contact with the rest of knowledge.

My first cut at visualizing this image was along the following lines:

In this version, learning can be viewed as either expanding your circle of knowledge or as increasing your boundary of ignorance.  So, the more you learn the more you know, but also the more you know that you don't know.  Depending on your temperament, this can be either encouraging or discouraging to your efforts to continue learning.

Formal schooling focuses attention on the inside of the circle and keeps you carefully inside the boundaries. The credentialing system of education looks backward at what you are supposed to have learned. On the plus side, a good school environment helps keep you from falling off the edge into material you are unable to understand or appreciate. I can remember trying to read various books on philosophy in my wildly eclectic romps through the public library during my high school days. All fields have their professional vocabulary and one purpose of schools is to introduce you to that vocabulary in a coherent order. While we are hard-wired to learn spoken languages simply by being immersed in them, I don't think the same strategy would work as well for learning calculus or modern European history.

The danger of formal schooling (even when well done) is too much focus on what you already know. If you don't push yourself out to the boundaries, you seriously limit your opportunities for significant new learning. Formal schooling tends to overly protect you from failure and, therefore, from opportunities for deeper learning. Granted, I've come to appreciate the importance of failure in real learning courtesy of my work with Roger Schank. The more important learning becomes as an ongoing career development activity, the more you have to deal with not knowing. This can become a real challenge as you advance in your career and as you become recognized for your expertise.

Over time and as you get farther away from your school days, your circle of knowledge starts to get spiky:

You become more expert and informed on certain topics at the expense of others. The nice, well-rounded, circle that might have characterized the end of a classical liberal arts education has been replaced with the distinctive profile of an expert in some particular domain.

If you assume that you do, in fact, need to continue to learn, regardless of your current level of expertise, is there some way to use this notion of the "boundary of ignorance" to guide ongoing learning? For an individual topic,

Monitoring your curiosity consists of becoming aware of terms, tools, topics, and techniques that you are encountering in your environment, yet are not part of your current knowledge and skills. As these become visible to you, the next step is to cluster and chunk that material into a learning agenda; a sequence of topics ranging from the nearly familiar to the barely recognized. [IT Learning at the Boundaries of Your Ignorance]

 In addition to tuning into the language of a topic, you can also start to identify the experts and authorities who are working in the domain.

In general, your learning agenda is not likely to be a single topic. Instead, you will be pushing out along multiple dimensions. It might be helpful to visualize that process in terms of progress along several learning vectors. For example, I might group my learning activities along the following dimensions:

This larger picture of learning would help assess what kind of balance I was striking across topics and whether that balance was suitable. 

What you are left with at this point is a map for what you want to learn based on the edges of what you know now coupled with what captures your curiosity. What comes next is the effort to learn topic by topic and to fit that learning into the demands of performing.

In one of my columns at Enterprise Systems Journal, I started to explore a nagging concern about why organizations have realized less of the potential of technology to support knowledge work than they could. In a nutshell, my hypothesis is that most organizations have not thought through what organizational roles need to be created to best leverage the technology. In my column I made the argument that we need the knowledge economy equivalent of tool-and-die makers. You can find the full column at ESJ:

Tool-and-Die Makers in a Knowledge Economy

The full potential of tools to support knowledge work remains unrealized

Given the near total independence that most knowledge workers have in organizations, they have been largely left to their own devices in figuring out how to take best advantage of the technology tools we have made available.  That leads to a great deal of wasted potential. Here's the way I described it in the column:

Applying the tool-and-die maker strategy, knowledge organizations should identify individuals particularly adept at applying tool and technology features to simplifying their own work and give them a new goal of improving the productivity and effectiveness of their knowledge-work colleagues. The knowledge work of these “toolsmiths” would be to understand the knowledge work of others and apply Taylor’s principles of scientific management; to observe how knowledge workers currently worked and to identify, design, and deploy new tools and techniques to make it possible to perform the same work with less effort or produce better-quality deliverables on demand

Essentially, we are missing an opportunity for knowledge work productivity by not taking full advantage of designing organizational roles to take full advantage of the strengths and weaknesses of individual knowledge workers.

Earlier this year I wrote a column for the Enterrpise Systems Journal on the linkage between knowledge management efforts and innovation. You can find the column at:

Get Better at Reinventing the Wheel

To succeed with knowledge management, organizations should focus on getting better at reinventing the wheel instead of avoiding it.

The rant that provoked this column was in response to the frequent justification of knowledge management efforts on the grounds that "we don't want to reinvent the wheel," which I finally got tired of hearing. It's one of those phrases that sounds like it means something useful until you actually take a look at it.

First, equating "knowledge" with "wheels" gets you on the wrong track by confusing knowledge with something vaguely product-like. I can't think of many knowledge work processes where you could simply take a piece of finished work from elsewhere in the organization and drop it in place. At the very least, you need to understand the current situation and the available knowledge work "thing" well enough to come up with a way to adapt or apply the old thing in a new situation. Any attempt to sidestep that process is guaranteed to lead to trouble. Don't encourage it by laziness in comparing knowledge work deliverables to wheels.

Second, if you are really doing knowledge work, then your customer, be it someone above you in the organizational food chain or a paying customer, is not interested in and will not pay for yesterday's answer. You need to divine their unique perspective and explicitly connect your knowledge work deliverable to that unique situation. The value of having organized access to prior knowledge work deliverables lies in improving the quality and the speed of applying that knowledge as an input to the process at hand, not as a deliverable.

Some time back I came across the following quote in The 80/20 Principle : The Secret of Achieving More With Less by Richard Koch, which I've been pondering ever since for its implications for knowledge work and knowledge workers:

There are only four types of officer. First, there are the lazy, stupid ones. Leave them alone, they do no harm…Second, there are the hard- working, intelligent ones. They make excellent staff officers, ensuring that every detail is properly considered. Third, there are the hard- working, stupid ones. These people are a menace and must be fired at once. They create irrelevant work for everybody. Finally, there are the intelligent, lazy ones. They are suited for the highest office.

General Erich Von Manstein (1887-1973) on the German Officer Corps

You can also map this quote into the following matrix representation:

One implication certainly is that you want to keep the average IQ up in your organization (setting aside all the limits on accurately measuring or assessing something as complex as intelligence for the moment). My own theory is that it also suggests that you want to keep your organization relatively small to maintain some degree of control over that average IQ. You may also want to keep the distribution of IQ in your organization as tight as possible.

The laziness/diligence dimension is the more interesting of the two in the context of knowledge work organizations. Common organizational practice is biased in favor of diligence, while laziness doesn't get the respect it deserves. Granted, the appearance of blogs such as Slacker Manager is a hopeful sign, as is the recent spate of activity and commentary around the importance of innovation and creative thinking for knowledge based organizations. But our Puritan/Calvinist heritage still dominates reward and evaluation systems. Regardless of the actual importance of thought and reflection to long-term organizational success, you are better off looking busy than looking like you are thinking. Even organizations that exist to promote reflective thought (e.g., universities, research institutes, think tanks) fall into the trap of encouraging diligence at the expense of reflection/laziness.

I don't yet have a fully workable solution to the problem of carving out sufficient and appropriate time for thinking and reflection. More often than not, it gets relegated to plane-time, travel-time, and after-hours time; essentially bypassing the organizational problem. I've found that mind-mapping, either by hand on on the computer, is one form of thinking that can be done in public without triggering unwanted negative perceptions.  Setting aside time to maintain some form of journal (whether in the form of a blog or more private diary) is another thinking/reflecting discipline that is both productive and not immediately threatening to the activity police.

Here are some questions I think are worth exploring in this context.

1. What alternate terms than diligence and laziness could we use to better frame the issue?
2. How important is it to carve out times and places to engage in visible laziness within organizations?
3. Is this a problem that needs to be solved at the organizational level? For which types of organization?
4. What barriers to innovation, if any, does a bias toward diligence create?

Any takers?

I'm in the midst of reading John Thackara's excellent In the Bubble: Designing in a Complex World, which will eventually warrant a full review.  Today, I want to pick up on one particular observation Thackara makes on the role of learning in organizations. He makes the point that

...the most important skills of all are so-called metacognitive skills--an understanding of guiding principles, of what really matters, and the abilility to filter out the growing flood of stuff that does not. "We need to be able to formulate new questions, " [Howard] Gardner argues..."and not just rely on tasks or problems posed by others. We need the ability to learn in new ways, to evaluate our own progress, to be able to transfer knowledge from one context to another." [pp.136-37, In the Bubble]

What makes this particularly important is that virtually no corporate training and precious little other training/education addresses these metacognitive skill issues. That's partly because developing good metacognitive learning skills diminishes the relative importance of the training department. If you become adept at identifying and monitoring your own learning, you may well conclude that the organization's curriculum bears little relationship to your needs.  

As individuals, we need to assess whether our learning mix includes sufficient attention to the metacognitive. Do we have a good sense of how we learn? How well can we identify gaps in our skills or knowledge and map those to useful learning opportunities? How accurately can we monitor our level of mastery of a new skill?

If we have the appropriate organizational influence, we should be asking similar questions of our organizational training and development programs.

I've been exploring the role of deliverables in understanding and improving knowledge work for a while. In January, I took another shot at articulating the link in a column in the Enterprise Systems Journal putting deliverables at the center of the challenge of improving knowledge work.

Knowledge work does not produce standardized, well-defined outputs. Instead, the value of its outputs depends on how well they match the unique needs of their users. No one is interested in a spreadsheet full of someone else's data; no teacher is likely to value a copy of a paper you've submitted to another class. Understanding what aspects and features of a knowledge work product are most valuable to its intended user is key to focusing efforts on producing the desired deliverable. [The Fundamental Secret to Improving Knowledge Work - ESJ].

Our experience in industrial settings encourages us to look at the output as something that is already well-defined and well-understood. We focus on process changes that will produce the output more quickly or more cost-effectively. When we are doing knowledge work, we do better to focus on the deliverable longer and more mindfully. At a minimum we need to understand the user's definition of quality, the balance between uniqueness and uniformity that will meet this level of quality, and the conditions that must be met to declare the work done. 

Dan Bricklin nicely summarizes most of the nice things I would have said in calling your attention to John Sviokla's new blog (Sviokla's Context). I think I can rightly take some credit for persuading John to add his voice and thinking to the mix. John and I first met twenty plus years ago at the Harvard Business School. John was just finishing his DBA (Doctor of Business Administration, not Data Base Administrator - this was HBS's original version of a Ph.D. in business that explicitly emphasized interdisciplinary thinking) as I was starting work on mine. He joined the faculty there and I worked as his research assistant for a while.

When HBS foolishly chose not to offer him tenure ten years later, I persuaded him to join me at Diamond, where he ended up becoming my boss again. Calling John "quite bright" is along the lines of describing Tom Brady as a "pretty good quarterback." If you are at all interested in how technology and strategy fit together, John is someone you would best pay attention to.

John Sviokla's blog. As part of my work as a DiamondCluster Fellow I've spent a lot of time talking with their vice-chairman and Global Managing Director of Innovation and Research John Sviokla and listening to his presentations. We've also produced a few episodes of a podcast together. Prior to DiamondCluster (a consulting firm that merges technology and strategy consulting) John was a professor at Harvard Business School (not when I was there as I recall). He's quite bright and helps me understand big businesses and organizations.

John has recently started blogging at a somewhat regular pace (a new long post every day or so). Given the disclaimer that I have a financial interest in DiamondCluster, that I do consulting for them, that I talked with John about his blog a few weeks ago as this was starting, and that he pointed to me today when writing about Motorola wikis, etc., etc., I have to tell you this because I think I'd be doing my readers a disservice if I didn't: John Sviokla's new blog is really worth reading. He covers technology and business in a way that will help people in both worlds. He brings interesting perspectives that remind you of those moments in business school when after 60 minutes of discussing a case in class it all starts making sense -- there's a way of looking at things I hadn't considered.

His blog is "Sviokla's Context" and it has an RSS feed.

John is trying to add his voice to the blogosphere. I think it's a welcome addition. A nice sign of the times as a blog may be pushing aside the white paper at a major consulting firm. [Dan Bricklin on John Sviokla]

Another excellent resource courtesy of James Robertson at Column Two. Good mental models are especially relevant in knowledge work arenas where so much of what we do tends to be invisible. This checklist should help you improve the models you make, whether for your own use or for broader consumption.

List of features of models

Idiagram has published an excellent list of features that all conceptual models should share. To quote:

Broadly speaking we use the term 'model' to refer to any structured knowledge that accurately reflects and enables us to make sense of the world. Models exist both internally as 'mental models' and externally as 'cognitive artifacts'. Cognitive artifacts can take many forms: written texts, spoken stories, graphs, diagrams, pictures, videos, spreadsheets, equations, computer-simulations, etc. While these different kinds of models vary greatly in their form and function, they all share certain desirable properties.

[Thanks to Mark Schenk.][Column Two - List of Feastures of Models]

Back in December I wrote a column for the Enterprise Systems Journal on the notion of triangulation as a key data collection and analysis strategy that is increasingly relevant in an economy characterized by information abundance. My central point was that:

In organizational (and other) settings where you are attempting to make sense of—or draw useful inferences from—a multitude of noisy and conflicting sources, the principles of triangulation offer a workable strategy for developing useful insights in a finite and manageable amount of time.

In navigation, the more widely and evenly dispersed your sightings, the more precisely you can fix your position. Focus your data collection on identifying and targeting multiple sources of input that represent divergent, and possibly conflicting, perspectives. Within an organization, for example, work with supporters and opponents, both active and passive, of a proposed reorganization or systems deployment to develop an implementation strategy. When evaluating and selecting a new application, seek out a wider assortment of potential references, vendors, and analysts. [Trust, Verify, and Triangulate]

Since that column, I've watched several of the recurring discussions (e..g Doc Searls, Eric Norlin, and Kent Newsome) about the changing relations between MSM (Main Stream Media) and new media forms such as blogs. Thinking about the contrasts between information collection and analysis strategies sheds some light on this debate. We used to live in a world with a handful of authoritative sources we learned to trust. With a bit more sophistication we added verify'to trust. Both those strategies work in a world of small numbers of sources, but breakdown in the world of multiple, conflicting, and contradictory sources. Triangulation then emerges as a viable alternative.

Tags: triangulation, gatekeepers

This past weekend I gave a seminar at DePaul University on the topic of "Knowledge worker effectiveness in organizations" as part of the Master's Program in Applied Technology (MAAT). As I was heading out of the house Saturday morning, I decided to grab some of the key books that I thought were important if you were interested in becoming a better knowledge worker.  It provoked some interesting discussion and I promised the students that I would send them a bibliography of the books I had brought along.

This is certainly my own idiosyncratic view, but it may be useful to others, if only as a starting point for discussion. Certainly, if you want to improve your skills as a knowledge worker, you are pretty much confined to some form of self-directed learning strategy.  I added a couple of titles I didn't see as I was going out the door and decided to limit my suggestions to 25 titles and focus on books that were focused on the needs of the individual rather than the organization. I suspect that you could complete this reading in less than a year if you chose to.

Although I didn't do so on Saturday, I spent a little extra time to organize and categorize the list. I also imposed some sense of the order that I would recommend to attack these titles over time. As far as I can tell, most still appear to be in print or obtainable on-line. The links here go to Amazon.

Paul Graham's latest essay is getting some play including within the David Allen, Getting Things Done, world where I came across it. Frankly, I didn't find it one of Graham's better efforts and you'd probably be better off sticking with Allen's insights about life and work. I'd boil down Graham's take as "stay focused on what's really important and let the little stuff slide in order to do that." I don't have the luxury to hire a personal assistant to let me do that and I'm confident that my wife wouldn't let me get away with it either. One of the reasons I keep sticking with Allen's approach every time I fall off, is that Allen gets the reality of both the important stuff and the nitty-gritty reality of day-to-day errands that still have to get done.

On the other hand, Graham also points to another essay by computer scientist and Turing Award winner, Richard Hamming that has much more importance to any of us who want to accomplish something significant in the knowledge work that we do.

Hamming's essay, You and Your Research, dates to 1986, but is still packed with insight about how to think about your work and problems worth tackling when you have significant discretion about what problems to work on. That's the defining characteristic of knowledge workers and there's precious little guidance to draw on. Just a few sample quotes to pique your interest:

What Bode was saying was this: ``Knowledge and productivity are like compound interest.'' Given two people of approximately the same ability and one person who works ten percent more than the other, the latter will more than twice outproduce the former. The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity - it is very much like compound interest. I don't want to give you a rate, but it is a very high rate.
...

Another trait, it took me a while to notice. I noticed the following facts about people who work with the door open or the door closed. I notice that if you have the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don't know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. Now I cannot prove the cause and effect sequence because you might say, ``The closed door is symbolic of a closed mind.'' I don't know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing - not much, but enough that they miss fame.
...

After quite a while of thinking I decided, ``No, I should be in the mass production of a variable product. I should be concerned with all of next year's problems, not just the one in front of my face.'' By changing the question I still got the same kind of results or better, but I changed things and did important work. I attacked the major problem - How do I conquer machines and do all of next year's problems when I don't know what they are going to be?
....

...I suggest that by altering the problem, by looking at the thing differently, you can make a great deal of difference in your final productivity because you can either do it in such a fashion that people can indeed build on what you've done, or you can do it in such a fashion that the next person has to essentially duplicate again what you've done. It isn't just a matter of the job, it's the way you write the report, the way you write the paper, the whole attitude. It's just as easy to do a broad, general job as one very special case. And it's much more satisfying and rewarding!

Great essay on procrastination.

Jack Holt e-mailed me a link to Paul Graham's essay on procrastination. It's great, couldn't agree more. Thanks, Jack, and Paul.

Comments (1)

Comments on this Entry:

(Pascal Venier on Jan 1, 2006 10:29 AM) An old classic ... in praise of procrastination is Stanford Philosopher short essay from 1995 on "Structured procrastination": http://www-csli.stanford.edu/~john/procrastination.html

[David Allen] 

You must read ...

The New York Times Magazine, December 4, "Coach Leach Goes Deep, Very Deep." By Michael Lewis (author of Liar's Poker, Moneyball, etc.).

You simply don't beat NEBRASKA 70-10. And a lightly regarded QB doesn't pass for 643 yards against Kansas State—before being pulled early in the 4th quarter. And you sure as hell don't do all this in Division 1-A with a coach who topped out as a bench-rider during his junior year in high school in Cody, WY. [tompeters! ]

The article is a feature piece on the unorthodox coaching strategies and success of Mike Leach, head football coach at Texas Tech. It's a very different riff on the relationship between strategy, leadership, and talent than you usually find. Leach and Texas Tech don't get first or second crack at the best talent. Not when you you've got UT and Texas A&M to compete with for starters. Here's how one NFL Coach summed it up:

Schwartz had an N.F.L. coach's perspective on talent, and from his point of view, the players Leach was using to rack up points and yards were no talent at all. None of them had been identified by N.F.L. scouts or even college recruiters as first-rate material. Coming out of high school, most of them had only one or two offers from midrange schools. Sonny Cumbie hadn't even been offered a scholarship; he was just invited to show up for football practice at Texas Tech. Either the market for quarterbacks was screwy - that is, the schools with the recruiting edge, and N.F.L. scouts, were missing big talent - or (much more likely, in Schwartz's view) Leach was finding new and better ways to extract value from his players. "They weren't scoring all these touchdowns because they had the best players," Schwartz told me recently. "They were doing it because they were smarter. Leach had found a way to make it work."

"Never mistake a clear view for a short distance."
                                                                       Paul Saffo

Last month I had an opportunity to listen to Paul Saffo of the Institute for the Future speak at the CIO Magazine CIO|06 The Year Ahead conference in Phoenix. I was there as part of CIO's Enterprise Value Award Process Review Board and as a facilitator for several of the breakout sessions. Paul was the MC for the 3-day event and his opening talk offered his rules for forecasting. They're worth having handy if you find yourself in a position to have to make some bets on what might happen next.

Before sharing his rules, Saffo made the point that he thinks of himself as a forecaster not a futurist. In his categories, a futurist is an advocate for a particular future, while a forecaster is an observer trying to understand and bound the uncertainties generated by events and trying to frame the choices that might influence the outcomes. Saffo used the following image (actually his image was much nicer - this is from my notes, but you get the idea).

Rule 1. Know when not to make a forecast.  Saffo made pointed reference here to Apple's famous Knowledge Navigator concept video in contrast with Doug Engelbart's Demo Video from 1967. I think what Saffo was driving at was the distinction between setting out a vision that will drive inventors and innovators on the one hand and recognizing that a salient event has occurred that opens up uncertainties that you ought to factor in to your planning.

Rule 2. Overnight successes come out of twenty years of failure. If you're not paying attention, you're going to be surprised a lot. This is where Saffo began to offer his take on the role of S-curve kinds of phenomena and how to account for them in your planning processes. Two points that I took away here. One is that there early stages of these curves is when you typically have the most leverage, if you can find a curve that will make it to the knee. Nothing terribly new there. The second, which I hadn't thought about as much, was the difference in planning errors depending on where you were in the curve. I'm used to thinking only in terms of the tendency to overestimate how fast things will happen in the early stages of development. I've been less tuned in to the equally likely tendency to underestimate speed and demand changes past the tipping point. BTW, one of Saffo's specific observations relative to this rule was that he's paying more attention to Robotics as potentially the next big thing.

Rule 3. Look back twice as far as forward. Another quick bit of capsule advice about how to think smarter when you are dealing with exponential/logistics curve phenomena. This is a rule of thumb that captures the essential error in our tendency to think in linear terms about power laws. The change you've lived through in the last 10 years is a predictor of what you are likely to experience in the next 5. Douglas Adams captured this most memorably in his 1999 essay "How to stop worrying and love the internet."

Alan Kay has talked about this in the context of why we've had more success at dealing with smallpox than with AIDS. If you are dealing with something that is operating on exponential terms, then the rate of growth matters as much or more than the slope at any instant in time. Given our tendency to project on a linear basis our tendency to over or under predict actually depends greatly on when/where you make that projection. With smallpox, the growth rate/infection rate is so fast that by the time you make any projection you are likely to be over predicting. With a slow growing epidemic such as AIDS, early stage linear projections will under predict. The corollary, of course, is that the surprise factor in slow-growing exponential phenomena is much higher.

Rule 4. Hunt for prodromes. Learned a new word. For you non-medical types, a prodrome or prodroma is an early symptom or leading indicator. This is William Gibson's observation that the "future is already here, it's just unevenly distributed."

Rule 5. Be indifferent. Don't confuse your desire for a particular outcome with its likelihood.

Rule 6. Tell a story or, better, draw a map. Trying to package your insights into a story (or scenario if you need to justify your consulting rates) helps reveal gaps, risks, and opportunities present in the events you are trying to understand. It can also help you get a better grasp on the potential wild cards. Saffo was more keen on the value of trying to find a way to capture your insights into something more graphical/visual. The value there is that those representations can help you highlight important relationships more easily and they raise the possibility of revealing 'whitespace' where you might find important opportunities to exploit or risks to minimize.

Rule 7. Prove yourself wrong. The essential wisdom of the scientific method. Understand and resist the natural human tendencies to believe. Be careful not to rely on a single element of strong information. Look for lots of pieces of weak information that collectively reinforce your insights. Your search for strong information should be for that one piece of evidence that proves you wrong. Look for the one thing that will make you look stupid if someone else brings it up after you've gone public.

It was a well spent morning listening to Paul, as was the opportunity to interact during the breaks.

Technorati Tags : paul+saffo, forecasting, s-curve, predictions2006

One of the blogs I've been reading on a provisional basis recently is "Inside Higher Ed." It provides an interesting contrast to the Chronicle of Higher Ed's Wired Campus blog. Both offer valuable perspective on the life of knowledge work and knowledge workers that goes well beyond their specific focus on the world of higher education.

In a column from November, Scott McLemee reflects on a 1959 essay by the sociologist C. Wright Mills "On Intellectual Craftsmanship." You can get your hands on a copy by buying a copy of Mills's The Sociological Imagination. At the core of Mills's recommendations is the notion of maintaining a file or journal, which ought to sound quite familiar. His description is worth sharing at length:

In such a file as I am going to describe, there is joined personal experience and professional activities, studies underway and studies planned. In this file, you, as an intellectual craftsman, will try to get together what you are doing intellectually and what you are experiencing as a person. Here you will not be afraid to use your experience and relate it to directly to various work in progress. By serving as a check on repetitious work, your file also enables you to conserve your energy. It also encourages you to capture 'fringe-thoughts': various ideas which may be by-products of everyday life, snatches of conversation overheard on the street, or, for that matter, dreams. Once noted, these may lead to more systematic thinking, as well as lend intellectual relevance to more directed experiences.

You will have often noticed how carefully accomplished thinkers treat their own minds, how closely they observe their development and organize their experience. The reason they treasure their smallest experience is that, in the course of a lifetime, modern man has so very little personal experience and yet experience is so important as a source of original intellectual work. To be able to trust yet be skeptical of your own experience, I have come to believe, is one mark of the mature workman. This ambiguous confidence is indispensable to originality in any intellectual pursuit, and the file is one way by which you can develop and justify such confidence.

The primary value of today's tools and technologies for  blogging, wikis, and the like is that they eliminate technical and usability barriers to maintaining and investing in the kind of long-lived knowledge asset that Mills is describing. Secondarily, these tools make it easier and more productive to engage in the kind of active reflection and learning Mills talks about.

What the tools don't do is provide the discipline and support structures to help you keep at the long-term investment in becoming a better knowledge worker. Or provide a nice, neat ROI argument that you can bring to your CIO or CEO.

Over the summer I wrote a column for the Enterprise Systems Journal that I neglected to point to at the time. The broad point I was trying to work out was that for all the recent attention to issues of innovation and design, the focus has been on addressing the needs of the organization.

Design thinking and design skill are equally, if not more, pertinent to individual knowledge workers. My wrap up there was:

Design is a talent, but it is also a skill, and whatever talent we were graced with, the skill can be developed. Few of us will rival MacGyver (few of us have a scriptwriter and props department handy either), but we can learn to start looking at the world around us as potential resources with more possible uses than intended. We can start to see opportunities to make small changes that will lead to a better fit between our resources and our problems. [ESJ: Design as a Signature Skill]

This is part of a more general trend of organizations needing to deal with how to strike a new balance between execution and design. In the last century, that balance was one person thinking design for every hundred to thousand doing execution. Today, that ratio needs to be much closer to one to one. Moreover, that balance will often have to be managed within each of us as knowledge workers. Perhaps that is one factor that accounts for the relative strength of small organizations versus large ones.

This is a piece I ran on my own blog before Future Tense launched. It struck me that it was worth sharing here to see how this group might react. Enjoy!

I’ve been thinking about the knowledge work as craft idea for a while now. Maybe it was the prospect of Revenge of the Sith opening, or just the slow percolating of ideas, but apprenticeship has been on my mind the last couple of weeks. I'm still working through this, but wanted to see what others might think.

In a craft economy, your craft was your birthright and you learned it through long apprenticeship. One strength of the industrial revolution was to define jobs and skills that could be taught more rapidly and systematically than craft apprenticing practices. This led to a host of cultural consequences that culminated in the social mobility that characterized the U.S. economy for so many years.

Marc has always done superb work and this is no exception. Full of ideas you can adapt to all kinds of design problems. It is also an excellent example of what you can do with presentation materials if you are willing and able to take the time (and are as talented as Marc). As design becomes a more integral part of management practice, this is the kind of practical material you like to have nearby.

This presentation made by Marc Rettig and Aradhana Goel is one of the finest examples of using down-to-earth methods and practices to create engaging user experiences. [PDF file: 7.5MB]]

 Some interesting point-counterpoint on the relative merits of organizational scale, but I can't help but smile at the notion the 80+ employees constitutes "big." To me the more interesting question here is how low we've been able to drive the scale of micro-businesses such as 37Signals who are able to have impact and presence far beyond their size because they are able to operate within the largely open ecosystem that is the internet.

Clearly annoyed by all the attention on small teams, Mena Trott goes on the record to defend big (relatively speaking). I especially enjoyed her comments because she’s in a unique situation — she’s seen Six Apart go from 2 in a bedroom to 80+ spread all over the world. Her perspective is valuable and respectable. And her passion is clear.

...

[Signal vs. Noise]

One of the most profoundly important (and disturbing) things about the Internet is that fundamentally no one is in charge. One of the individuals responsible for that design is David Reed, a computer scientist from MIT.

As far back as Jethro and Moses in Exodus, we've applied hierarchy to bring complexity under control. Many have characterized Jethro as the world's first management consultant. One of the reasons that hierarchy works so well in organizational settings is that is addresses the problem of information overload on managers, where middle managers serve to consolidate and route information through the hierarchy.

However, computers are not people and hierarchy is not the only, or necessarily the best, solution to information management problems. Reed, along with J.H.Salzer and D.D. Clark, wrote a seminal paper in the early days of the design of ARPANET and TCP/IP called  "End-to-End Arguments in System Design" that laid out the reasons that hierarchical solutions were a bad idea in designing a network of the scale and complexity envisioned for the ARPANET. Those design insights were baked into the basic architecture of TCP/IP and are one of the core reasons that the Internet has grown as widely and rapidly as it has. If you hope to understand how the net and network thinking in general will continue to impact the future of work, this had better be one of your starting points. "End-to-End Arguments" is a pretty technical paper, although it is manageable; you might find  "The end of End-to-End?," also by Reed, a better starting point.

More recently, David has been exploring other notions about how markets and technology interact in ways that don't necessarily mesh with our default assumptions. In particular he's done interesting work on why eBay and other internet companies have thrived but handing significant power over to their customers with the notion of Group Forming Networks.

Currently, David is back at MIT at the Media Lab leading a research program on Communications Futures. A good starting point for this work is the program on Viral Communications (pdf) David is doing with Andy Lippman of the Media Lab.

Like other thinkers, the value of looking at what David is up to is twofold. First, the ideas themselves are powerful. Second, watching how someone smart tackles problems can give you insights into how you might tackle other problems more productively.

Interesting article in this morning's NY Times on computer science education and efforts to develop a richer and deeper perspective on how technology skill connects to other skills and needs inside organizations. It strikes me as another case example of a broader trend to find a new balance between specialization and general skills in organizations:

Edward D. Lazowska, a professor at the University of Washington, points to students like Mr. Michelson as computer science success stories. The real value of the discipline, Mr. Lazowska said, is less in acquiring a skill with technology tools - the usual definition of computer literacy - than in teaching students to manage complexity; to navigate and assess information; to master modeling and abstraction; and to think analytically in terms of algorithms, or step-by-step procedures. [A Techie, Absolutely, and More, NY Times]

Doc Searls points to an excellent essay by Paul Graham on What Business Can Learn from Open Source.  It's full of thought-provoking observations. Here's just one sample:

The third big lesson we can learn from open source and blogging is that ideas can bubble up from the bottom, instead of flowing down from the top. Open source and blogging both work bottom-up: people make what they want, and the the best stuff prevails.

Does this sound familiar? It's the principle of a market economy. Ironically, though open source and blogs are done for free, those worlds resemble market economies, while most companies, for all their talk about the value of free markets, are run internally like communist states.

There are two forces that together steer design: ideas about what to do next, and the enforcement of quality. In the channel era, both flowed down from the top. For example, newspaper editors assigned stories to reporters, then edited what they wrote.

Open source and blogging show us things don't have to work that way. Ideas and even the enforcement of quality can flow bottom-up. And in both cases the results are not merely acceptable, but better. For example, open source software is more reliable precisely because it's open source; anyone can find mistakes.[ Paul Graham]