On March 24, Gary Hamel posted to his Wall Street Journal Management 2.0 blog “The Facebook Generation vs. the Fortune 500,” an entry in which he spelled out:

“12 work-relevant characteristics of online life. These are the post-bureaucratic realities that tomorrow’s employees will use as yardsticks in determining whether your company is “with it” or “past it.” In assembling this short list, I haven’t tried to catalog every salient feature of the Web’s social milieu, only those that are most at odds with the legacy practices found in large companies.”

I encourage you to read the post, which showcases Hamel’s enviable ability to distill a phenomenon and explain it to executives without oversimplifying. I don’t think I’ll be doing him any disservice if, in the interest of concision, I list the 12 characteristics here without including his explanations:

1. All ideas compete on equal footing
2. Contribution counts for more than credentials
3. Hierarchies are natural, not prescribed
4. Leaders serve rather than preside
5. Tasks are chosen, not assigned
6. Groups are self-defining and -organizing
7. Resources get attracted, not allocated
8. Power comes from sharing information, not hoarding it
9. Opinions compound and decisions are peer-reviewed
10. Users can veto most policy decisions
11. Intrinsic rewards matter most
12. Hackers are heroes

Hamel writes that “If your company hopes to attract the most creative and energetic members of Gen F, it will need to understand these Internet-derived expectations, and then reinvent its management practices accordingly.”

I’m not so sure about the ironclad “will need to” part. Back when there was a thriving finance industry many of my students, including some incredibly bright, talented, and ambitious young people, wanted desperately to work in it. They would have put up with paleolithic technology and caveman bosses (neither of which were rare) in order to be part of that sector. Hedge funds, investment banks, and private equity firms have had to make wrenching adjustments recently, but not because of the preferences of the millennials they employ or want to hire.

In general, though, I believe Hamel’s right: most organizations do need to take into account how millennials work, and how they think about hierarchy, expertise, collaboration, decision making, resource allocation, and many other aspects of organizational life. Gary has been a longtime advocate for reexamining these aspects, and for moving us past what he describes as the “mid-20th-century Weberian bureaucracy” that characterizes most large organizations today.

But how should companies do this? How should they reinvent their management practices? One school of thought argues for something like a corporate anti-neutron bomb –  one that leaves the people there but obliterates the existing structures, hierarchies, and edifices. A company taking this approach would try to become entirely Weblike, and to exhibit all twelve of the characteristics Hamel lists.

Of course, few if any organizations are actually going to do this, and based on my conversations with Gary I don’t think this is what he’s advocating. But a fully Weblike company is a useful strawman because it sets up the question “Why not?” –  why would it be bad (in addition to difficult) for a company to adopt these characteristics?

To answer this question, let’s take a look at a prototypical large corporation and concentrate on two of its employees: a brand new millennial hire, and an experienced, competent midlevel manager (the truths of Dilbert aside, such people do exist).

To me, it makes no sense at all to:

• Have these two compete on equal footing to get their proposed projects approved and funded
• Give their ideas equal weight
• Let the two of them (and all other employees) decide who should work for whom
• Let the newbie veto the graybeard’s decisions
• Let the millennial decide what he wants to work on all day, each day

Doing such things simply ignores the fact that the more senior employee has greater experience and institutional knowledge. It also ignores the fact that a predefined hierarchy, even an imperfect one, provides certainty and clarity over decision rights that are very difficult to replicate in a purely emegent or egalitarian structure (see the debate over inclusionism and deletionism in Wikipedia, or the great story in The Onion — “Marxists’ Apartment a Microcosm of Why Marxism Doesn’t Work.”).

The Web works in strange and wondrous ways, and has a lot to teach any of us who are interested in making companies work better. Enterprise 2.0, my shorthand for how companies can and should become more Weblike, is the subject of much of this blog’s content. And my work on Enterprise 2.0 tells me that adopting the 12 characteristics listed above is going way too far.

In fact, fully adopting any of them is, I think, overdoing it. A better idea than entirely replacing predefined corporate structures and practices with emergent ones is figuring out how to blend the two approaches to organizing work –  how to overlay emergent systems on predefined ones.

Another good idea is to reinvent current management practices not by replacing existing hierarchical routines with emergent ones, but rather by using emergent systems, communities, and processes to lead the way –  to show how existing practices can and should be changed. This could mean, for example, not having all ideas compete on equal footing, but instead ensuring that all ideas are open to scrutiny, commentary, and improvement, and that the ones that come from high up in the hierarchy aren’t treated as if they’re fully formed or free from error.

Here are some initial thoughts on how to start blending Hamel’s characteristics of online life into current management practices:

1. All ideas compete on equal footing. No ideas are above review or commentary; there are no sacred cows within the organization. For an example of this in action, see this blog post.
2. Contribution counts for more than credentials. Credentials are not necessary for making contributions. Bo Cowgill was working in customer service at Google when he proposed the creation of a internal prediction market for the company; he then became the ringleader of the team that built it.
3. Hierarchies are natural, not prescribed. Some hierarchies are allowed to form naturally.
4. Leaders serve rather than preside. Leaders expand their toolkit by using 2.0 technologies and participating in the resulting communities. They blog, tweet, join social networks, and use 2.0 technologies to show why they’ve ascended to high positions. Paul Levy, CEO of Boston’s Beth Israel hospital, is a prime example of such a leader.
5. Tasks are chosen, not assigned.
6. Groups are self-defining and -organizing. Just as with hierarchies, some tasks and groups are self-organizing.  Cisco decided on its current set of 20+ corporate priorities via a largely emergent process, and employees selected themselves into groups to work on them. All this, of course, was in addition to the ‘normal’ work of the company.
7. Resources get attracted, not allocated. This is a tough one. Current resource allocation processes are highly hierarchical. Even when initiatives arise from emergent work, they get funded officially from the top down. It’s hard to see how to effectively change this.  Ideas, anyone?
8. Power comes from sharing information, not hoarding it. One way to become powerful is to share information, refine and improve it, and/or use it to connect people with each other.
9. Opinions compound and decisions are peer-reviewed. Decisions are subject to peer scrutiny. In other words, the crowd has the ability to weigh in on the direction the company is taking. This is very different than giving all crowd members veto power, or even a vote. Enterprise 2.0 does not mean setting up a corporate democracy (even Wikipedia is not a democracy).
10. Users can veto most policy decisions. See #9. I think and hope that individuals will have greater voice within organizations in the future, but not greater veto power.
11. Intrinsic rewards matter most. Companies use 2.0 tools and approaches to tap into a wider mix of motivations –  both intrinsic and extrinsic.  One note here: it’s important not to confuse intrinsic vs. extrinsic with small vs. big, or monetary vs. non-monetary. Many of the traders in Google’s prediction market are extrinsically motivated. They want external rewards for good work, but seem to be much more interested in t-shirts than cash prizes.
12. Hackers are heroes. Dissenters are valued as long as they do two things: justify their arguments with logic and facts (or at least lay out how to test their hypotheses), and strive to be helpful to others and productive for the organization. “Everything sucks and this place is run by morons” is the stance of a sullen adolescent, not a courageous truth-teller.

What do you think of Hamel’s characteristics and my attempt to blend them with current organizational practices? Are we correct, at least on the right track, or badly kidding ourselves? And what have you observed about how millennials want to work, what technology makes possible, and how companies are adjusting to these trends? Leave a comment, please, and let us know.

This past week I rolled out a couple Enterprise 2.0-ish experiments in my MBA Class Managing in the Information Age. First, I attempted to use crowd wisdom to outsmart my students. Second, I let them form their own online crowd during a single class.

The previous week I had thrown down a challenge: any students that outpicked me in the men’s NCAA college basketball tournament (aka “March Madness”) would win freedom from cold calls for the rest of the semester. I use an Excel-based random cold call generator in class and my students absolutely hate it, so they had ample incentive to fill out an entry within the ESPN group I set up.

I told my students that there was plenty of help and advice about the tournament available online, as well as many, many freely available brackets completed by different flavors of expert. I also told of them none of this would do them much good, though, ’cause I was such an ardent college hoops fan that I would surely outpick them.

This was a baldfaced lie.  I haven’t watched a basketball game in years, and have no idea who’s any good these days. I’m certainly not better informed than the tournament organizers, who seed the 64 teams based on their expected performance. So for me, a smart strategy is to just pick the team seeded higher in each game, thereby taking advantage of all the intelligence baked into the tournament.

I also think this is a pretty smart strategy no matter how well informed you are. I wonder how many ‘experts’ predict the tournament’s results better each year than the seeds alone do. I bet it’s not many. I bet even fewer experts would have a track record over many years of outpredicting the seeds.

But I also thought it would be possible to do better than just picking the seeds by tapping into crowd wisdom –  seeing, in other words, if a crowd thought that the tournament organizers got it wrong in any cases. And my preferred way to do this is to look at prediction markets.

NewsFutures set up prediction markets for each of the first round games in the tournament. When I checked them shortly before the deadline for submitting a bracket to ESPN, I saw that they were predicting 3 upsets: #10 Maryland over #7 California, #9 Tennessee over #8 Oklahoma St., and #10 USC over #7 Boston College. In all other cases, the collective prediction of the NewsFutures traders was that the higher-seeded team (the one with the lower number) was more than 50% likely to win in the tournament’s first round.

So in all cases except the three listed above, I picked the higher seed. With one exception: #12 Arizona had, according to the market a 48% chance of beating #5 Utah, and I couldn’t pass up the chance to correctly call a big upset like that. So I went with Arizona.

In all rounds after the first I simply picked the higher seed. I could have set up my challenge to students so that we had to pick one round at a time. Setting it up this way would have let me use the markets established for later rounds, but I’m pretty sure my students would have caught on to this strategy before too long, and we would have all converged.  Maybe next year…

I don’t know any of the traders in the NewsFutures markets, and don’t know exactly how to interpret the data they provide on trading volume – the number of contracts held for each game (beyond knowing that more trading is better). I just have a lot of faith in prediction markets‘ ability to forecast real-world events, and wanted to put that faith to the test in a visible way.

None of my students guessed correctly that I had used prediction markets to make my picks. I asked them to describe their strategies; they relied on their knowledge, seeds, odds from Las Vegas, and hometown affiliations (a very bad strategy). None of them said that they’d used the markets as I had. The most intriguing strategy I heard was to use the results of the video game company EA Sports‘ simulated tournament to make picks.

The first round of the tournament is now over, and the markets correctly predicted the upset victories of Maryland and USC. In addition, #12 seed Arizona did win, so the markets did an excellent job of highlighting this possibility. They were wrong about Tennessee over Oklahoma St. There were seven additional first round upsets that the markets did not correctly predict.

Overall, my strategy of relying on the markets left me in significantly better shape –  two victories worth -  than would have been the case if I’d relied only on seeds.  I’m currently tied for fourth place among the 39 brackets submitted as part of my class, and in pretty good shape for the rest of the tournament; no one has more possible points remaining than I do. The bracket using results from the EA sports simulated tournament is tied with me at present, but has fewer possible points remaining.

My second experiment with emergent social software platforms was much simpler: I just told all my students to get Twitter accounts, then allowed them to tweet freely during class on Friday, March 20. I also told them to send one tweet using the hashtag #HBSMIA2009.

Twitter expert and celebrity and Pistachio Consulting founder Laura Fitton (@pistachio) is coming to class later this semester, will encourage students to tweet during class, and will also (I believe) display these their tweets on a screen throughout class. I found myself unwilling to take that last step, but did want to get my students comfortable with the practice of live tweeting, and also wanted to see what it felt like to teach class while that was going on.

So I told them that class on the 20th would be an exception to HBS’s standard ’screens down’ policy (i.e. no use of digital devices during class), and that they could tweet using whatever device they preferred.

I’ll ask my students what they thought about the experience, but I thought it was miserable. Class discussion limped along at well below its normal levels of engagement, interest, and insight. I thought it was due to my bad class plan, a comparatively weak case, and/or the fact that the 20th was the last day before spring break.

Any or all of these could have been part of the explanation, but I’m quite sure that another part was the tweeting that went on. When I reviewed students’ tweets after class, I found that a lot of them remarked on how difficult it was to pay attention to what was going on in the room and on their screens. And it was very clear that the screens won.

Speaking to an audience that’s tweeting away is now a fact of life at most technology conferences (as clearly evidenced by this year’s South by Southwest). Laura says she likes it, and I’m eager to learn from her why this is and how I can turn live tweeting to my advantage when speaking. So far it feels to me like trying to talk to people who all have TVs in front of them. I realize that live tweeting might be beneficial to some constituencies (like the tweeters’ followers), but it feels to me like a pure negative for speakers. We’re now competing for attention with a very compelling interactive activity.

I know this is the new reality of public speaking, and I know I’ve got to get good at it, but I’m not sure how I’m ever going to come to like it. And I know my classrooms are going to remain unwired. I want my students to concentrate on the discussion taking place in meatspace, not the ones in cyberspace.  I want to be clear: I like twitter a lot and use it a fair bit myself (follow me at @amcafee if you like), but I don’t like it in a classroom when a live discussion is (supposed to be) taking place.

Two questions to wind up this post. First, how could I have made better use of crowd wisdom and other available information to make better March Madness picks? And do you have any tips on how to be a good Twitter-assisted public speaker?  Leave a comment, please, and let us know.

On March 5 the New York Times carried an extraordinary opinion piece by Dr. Anne Armstrong-Coben, an assistant professor of pediatrics at Columbia. She takes a stand against the computerization of health care, writing that “In short, the computer depersonalizes medicine.” The core of her argument is that computers impede a doctor’s ability to do her job –  to interact with patients, figure out what the medical situation is, communicate this information to colleagues, decide on an appropriate course of action, and see that it’s carried out. She acknowledges that “The benefits [of computerization] may be real…” but immediately follows this with “…but we should not sacrifice too much for them.” She cautions that “The personal relationships we build in primary care must remain a priority, because they are integral to improved health outcomes.”

On March 10 the Times posted eight letters in response to her piece. By my count, 6 of them wholeheartedly endorse her views. I do not.

Dr Armstrong-Coben mentions two specific pieces of health IT –  the electronic medical record (EMR), or digital version of the classic medical chart, and the computerized physician order entry (CPOE) system used by a doctor in a hospital to order medications. And she has nothing good to say about either of them. Entering data into an EMR is much less convenient than writing on paper, and CPOE systems can generate errors. As she writes:

“A box clicked unintentionally is as detrimental as an order written illegibly — maybe worse because it looks official. It takes more effort and thought to write a prescription than to pull up a menu of medications and click a box. I have seen how choosing the wrong box can lead to the wrong drug being prescribed.”

I have to assume that as an experienced clinician she’s also seen how bad handwriting or a doctor’s ignorance about other prescriptions can lead to the wrong medicines being administered within a hospital. So which types of errors — computer-based or human-based –  are more common? A rigorous and thorough study, published in 1998 by David Bates, Lucian Leape, and their colleagues and conducted at Boston’s Brigham and Women’s hospital, compared medication errors before and after CPOE was introduced. The researchers found that preventable adverse drug events –  in other words, injuries stemming from medication errors — declined by 17 percent after CPOE was implemented.

These improvements are critically important because medical errors are both severe and dismayingly common. A 1995 study, also led by Leape and Bates, found that 6.5% of all patients admitted to two Boston hospitals suffered an injury during their stay, and that 28% of these injuries resulted from errors by health care providers. A third study found that 20% of all medical errors in hospitals — the largest category — were related to medication. This research also found that 13% of hospital injuries resulted in patient death.

When these are the facts, a 17% reduction in injury-causing errors becomes a big deal. As part of the homework for a case study that I wrote about CPOE introduction at a hospital I ask students to estimate how many deaths are likely to be averted if the application is deployed as successfully as was the case at the Brigham. A straightforward and conservative calculation reveals that the answer is about four deaths every year in that hospital alone. What responsible health care provider would resist such a technology?

It is absolutely true that current health IT is far from perfect; it can be difficult and confusing to use, hard to integrate smoothly into conversations and examinations, and programmed with bugs and/or bad medical information. But the perfect is the enemy of the good. I’ve never seen a perfect application or piece of hardware, but I’ve seen plenty that are on balance usable and beneficial. The technology so bad that it’s worse than no technology at all is an appealing bogeyman to some people, but a thankfully uncommon one in the real world. And CPOE systems and other health IT have come a long way since the landmark study was published in 1998.

Here’s a thought experiment: what if current state-of-the-art health IT, including EMRs and CPOE systems, suddenly appeared in tomorrow in every health care delivery facility in the country, along with sufficient training resources to get providers up to speed quickly with the new tools? What would be the impact on Americans’ health?

My strong belief is that health outcomes would improve quickly, substantially, and almost universally, and that the improvements would stick around over time. For one thing, many fewer people would die because of the kinds of preventable medication errors uncovered by Leape, Bates, and their colleagues. For another, it would be much more likely that thanks to EMRs all involved care givers would have access to the same information (and have access to it from wherever they are), and so make decisions and have conversations based on it.

In addition, patients themselves would have much more information about their own health. A paper chart-based world of medical care is an inconvenient one for patients. They have to ask their providers for copies, then cart them around as they move through our country’s fragmented health care system.

People’s willingness to do this, I’ve observed, is directly related to the severity of their health problems. Because I’ve been very fortunate with my health I can’t be bothered, and so don’t myself have any paper trail of my health and health care over time. The only information I do have is at patientgateway.org (a system sponsored by Massachusetts General Hospital and Partners HealthCare), which is populated by data from exactly the kinds of systems that Dr Armstrong-Coben disparages. She might feel inconvenienced by health IT, but I feel inconvenienced by health paper. And don’t my preferences matter when it comes to my health care?

I think that Google Health is a likely big deal because it gives me and all other patients a central repository for all the health data we accumulate over time, regardless of where it comes from, as long as it’s in digital form. I hope this effort takes off and goes in all kinds of directions, moving us as far as possible from a world where my health information sits in an assortment of hanging folders in offices I couldn’t find any more overseen by doctors whose names I don’t remember.

Unless I misread her badly, this is the world of health care that Dr Armstrong-Coben is advocating.  I advocate something very different: a health care system that’s a lot more wired. I don’t pretend for a minute that digitizing the American health care industry would solve all of its problems, and I certainly agree that some things, some of them important, would be lost or compromised. But other things, also important, would be improved and we would become a significantly healthier society.

Do you agree?  Leave a comment, please, and let us know what you believe about health IT and why you believe it.

Sunday, March 8 was my day of 100 tweets, brought on because of a bet lost to Amy Senger. I got varied reactions to the effort as a whole, from some quite nice compliments to some responses that were less pleasant.

I grouped my tweets into sets of 10 or 20 on a single topic. The topics themselves were unconnected and not the result of any deep thinking; they were just areas of interest that I thought could yield enough tweets.

Most passed without much comment, but two topics generated a fair bit of retweeting, replies, and discussion. These were “20 great poems available online,” and “10 things I’ve learned from teaching.”

Web and Twitter giant Tim O’Reilly picked up my list of twenty poems about halfway through and told his huge set of followers “Poetry lovers, check out @amcafee’s tweet series of 20 great poems available online. He started this about an hour ago.” There are evidently a ton of poetry lovers out there, because I started to get a lot more followers immediately after Tim’s message went out.

People volunteered their favorite poems, talked with each other about them, and expressed levels of love and enthusiasm for poetry that suprised and gratified me a whole lot (for a flavor of the discussion, see this set of search results).

Here’s the list of poems, along with my very terse commentary (brevity was imposed by Twitter’s 140 character limit). One note –  the punctuation and line breaks on some of these online versions are altered from the original, which is always a detraction. So if you like them at all, please find print versions of the poems; I assure you you’ll like them more.

1. “Al and Beth,” Lewis Simpson – http://bit.ly/3u3WxB – the American immigrant experience, distilled way down
2. “Blossom,” Mary Oliver – http://bit.ly/1CCHlN – limited myself to one Mary O. poem in this list. Did I choose well?
3. “The Illiterate,” William Meredith – http://bit.ly/Rvfb – captures the feeling of wonder at meeting a good person
4. “Rain,” Richard Tillinghast – http://bit.ly/6BX6 – just stunned by the imagery in this one
5. “You can Have it,” Philip Levine -  http://bit.ly/3kCtmE – for anyone who loves his brother

6. “Place of Pilgrimmage,” Jaroslav Seifert – http://bit.ly/10dhE – such a smart way to get across the glories of women
7. “What the Uneducated Old Woman Told Me,” Christopher Reid – http://bit.ly/QCZO – I find this one quite affecting
8. “Peeling an Orange,” Virginia Hamilton Adair – http://bit.ly/2U8Qvj – may we all have this moment
9. “The Goose,” Muriel Spark – http://bit.ly/4AV7Tc – advice and a worldview, all in 8 lines
10.”Optimistic Little Poem,” Hans Magnus Enzensberger – http://bit.ly/17szl – grudging admission from a Communist poet

11.”The Lover in Winter Plaineth for the Spring,” Anon. – http://bit.ly/IpkD – no way I was going to leave this one off the list
12.”Fern Hill,” Dylan Thomas – http://bit.ly/BgUY – has there ever been a better evocation of the joys of youth?
13.”Any Prince to Any Princess,” Adrian Henri – http://bit.ly/vHOc – for when you’ve screwed up, gentlemen
14.”A Tale Begun,” Wislawa Szymborska – http://bit.ly/JNMv6 – for pregnant friends, from a Nobel prize winner
15.”First Lesson,” Philip Booth – http://bit.ly/KMVn – on the full duty of fathers

16.”The Yak,” Hillaire Belloq – http://bit.ly/IJ04P – one on this list has to be just pure silly fun
17.”Having it Out with Melancholy,” Jane Kenyon – http://bit.ly/4q4CtR – Kenyon fought depression, hard
18.”Gravy,” Raymond Carver – http://bit.ly/OgHz – what Carver wrote after his brain cancer diagnosis
19.”The River Merchant’s Wife,” Li Po (translated by Ezra Pound) – http://bit.ly/17Cgt – there are many ways to fall in love
20.”Orpheus and Eurydice,” Czeslaw Milosz – http://bit.ly/3vod – I promise you, I will never love another poem more

The other list that generated some follow up was ten things I’ve learned from teaching:

1. Don’t be afraid of silence in the classroom
2. Ask clear questions
3. Trust your students
4. Be the person who most wants to be in the room
5. Start on time, end on time

6. Check your fly
7. Be more concerned with the destination than the journey
8. We get smarter via respectful disputation
9. It’s better to be well-rested than well-prepared
10.Most students appreciate being held to high standards

I honestly didn’t intend to convey any deep insights with this set, but Mark Gould found some anyway. He wrote a blog post keying off my list; look how much better his is:

1.Silence is not bad — so long as it signifies that people are thinking about what you are saying.
2.If you are clear what you want from people, you have to have understood it better, and they will know why it is important.
3.Internal consultancy is a kind of leadership — the organisation has trusted you to take it somewhere new, so you owe it to those you are leading to trust them too.
4.If you don’t care deeply about what you are doing (and show it), everyone will know, and take their cue from you.
5.At the most basic level, punctuality is respectful — but it also shows that you have made a plan and have stuck to it. If you can do that with the small things, people will believe that you can do the same with the big ones.
6.There is always something obvious to remember to do. Remember to do it, otherwise people will notice.
7.If there is agreement about what the outcome should be, that is what is most important. If you start to quibble about the route-plan, you run the risk that you lose internal clients along the way.
8.If there are differences of opinion, they only fester if left unspoken. Clearly-expressed alternative perspectives can lead to a much better outcome — be open to them.
9.Do the best preparation that you can, but an alert mind can overcome gaps in that preparation (and there will always be gaps).
10.Just because you have been asked to advise on something, don’t let the client (internal or otherwise) get away without doing their bit — the outcome will be better and will be better implemented if they engage properly.

I find his improvements kind of humbling, and further evidence of the value of getting ideas out there so they can be discussed and enhanced.  Thank you, Mark.

I experienced a net gain of a couple hundred followers over the course of the day, and boosted my Twitter Grader rating as high as 99.98(!). So I’d call the effort a success, but don’t worry – I’m not about to repeat it. As we learned, it’s a fair bit of work to produce or consume 100 tweets over the course of a single day.

I can easily imagine doing a few more lists, though, and/or continuing to highlight really good writing (poetry, prose, non-fiction, etc.) that I come across on the Web. The poems seem to have struck a chord with a lot of people, so let’s keep using Twitter to surface and talk about them. Sound good?

And if you have any comments about the day of 100 tweets, I’m eager to hear them.

As I wrote earlier, because Amy Senger was able to stay off Twitter for a week, I lost a bet and now have to be a highly active contributor for a day. So tomorrow (Sunday, March 8 ) I’ll tweet 100 times.

I’ve divided my tweets into groups of ten (in one case twenty) on the same topic. They’ll come throughout the day.

If you’re following me on Twitter now but have no desire to get bombarded by me like this, now would be a good time to unfollow. If, on the other hand you’re curious to see what the 100 will consist of, please sign up to follow me.

A much more normal tweeting pattern, which will still include #andyasks, will resume after tomorrow.

Enjoy the weekend!

My current print version of Information Week has an interesting cover story about “demand-signal management,” a highfalutin term for getting data about what’s selling, then using it to make decisions about manufacturing, replenishment, etc. The data come from the points of sale –  all the cash registers in all the all the stores in all the towns in all the world where products of interest are being sold –  and are voluminous. Think how many separate SKUs a supermarket or department store sells in a day, then think how many locations a large supermarket or department store chain has. And think how many days there are in a planning horizon. Finally, think of how many different supermarket or department store chains a large supplier deals with.

Old-school forecasting methods ignored this flood of data and based their projections on order and shipment data (rather than sales data). Demand-signal management (DSM) holds out the promise of improving results — better matching supply with demand in a fast-changing world — by incorporating sales information (which is, let’s face it, the most accurate signal about what’s selling) into forecasting algorithms and processes.

I started reading the article because of my geek interests in IT and supply chain management, but stayed interested because of a much broader interest (also geeky) in technology’s  influence on the design of organizations.

One of the most fundamental organizational design issues is the allocation of decision rights — in other words, answering the question “Who should get to make which decisions here?”  Some organizations are highly centralized; most important decisions are made at the top, and everyone else just executes on them. Many franchise restaurants work this way — headquarters decides on menu items and prices; the layouts, equipment, and technology for each location; the locations themselves; and most other important aspects of the business. Local managers and staff have relatively little freedom by design.

Other organizations are heavily decentralized, giving great autonomy to people far from headquarters. My brother used to work for Population Services International (PSI), a nonprofit devoted to improving public health. He spent several years in charge of their programs in Madagascar, and he had almost total freedom to decide what those programs should be. He was constrained only by his imagination, initiative, and funding; the PSI base in Washington didn’t want to dictate terms or tie his hands.

PSI took this approach because they believed that the most important knowledge about how to improve health in a given country could only be obtained by immersion in that country. As I wrote a while back, knowledge can be divided into two types: general knowledge (GK), which is easy to describe digitally and zip around the world, and specific knowledge (SK), which is hard to get out of people’s heads or codify at all. For any particular decision, the relevant knowledge for making it correctly is the sum of the general and specific knowledge about the situation.

PSI believes that specific knowledge overwhelms general knowledge in its sector: public health in the developing world. Starbucks believes, in contrast, that in its industry the general knowledge it possesses about how to locate a shop and make lattes dominates the specific knowledge of local managers and employees.

One of the basic trends I’ve been discussing with my MBA class this semester is IT’s power to convert specific knowledge into general knowledge. Companies today gather huge amounts of data over time and analyze it. This work converts qualitative intuition and experience (SK) into quantitative results and conclusions (GK). In addition, Enterprise 2.0 tools when they’re working well do a great job of harnessing and displaying collective intelligence. This is a crystal-clear transfer of specific knowledge into general knowledge.

So as I was reading along about demand-signal management I thought I knew where the IWeek article was heading. It would wind up by talking about how smart companies were centralizing their forecasting and planning activities to take advantage of the mass of point of sale data, and also centralizing subsequent decisions about replenishment, pricing, promotions, shipments, etc. I thought an article about a significant increase in general knowledge for retailers and their suppliers would also be an article about how these businesses were becoming more centralized.

Nope. The companies profiled, including Coty Fragrance, Best Buy, Goodyear, and Food Lion appeared to have a strong preference for having decision rights low in their organizations in the wake of DSM.  According to the article, for example, at Goodyear:

“More than 100 channel, brand, and category managers use the data for forecasting and planning, and sales reps access basic reports…”

At first I thought these companies were missing something. Then I realized I was. In fact, I was forgetting an excellent discussion we’d had in class about whether salespeople were surrendering a huge amount if they entered details about customer contacts into their company’s CRM system. This is clearly a conversion of SK to GK. Before CRM, only the salespeople themselves knew where exactly where they went, who they talked to, and what the substance and results of the conversation were. If they enter contact data into the system their bosses, their colleagues, and their replacements all have access to this information.

Some students in class argued that whatever the benefits of CRM might be to salespeople, they couldn’t outweigh the fact that the system turned their SK into GK. Others, though, maintained that even though this SK –> GK conversion did take place, it wasn’t that big a deal for two reasons. First, CRM didn’t and couldn’t come close to capturing all a salesperson’s SK, or even the most important bits of it. These people amass knowledge about customers and their companies –  who’s amenable to a hard sell and who’s not, who seems to have stopped paying their bills, who’s about to jump ship to a competitor, what kind of body language works for each person, etc. –  that’s hard to capture in a database, and often even hard to articulate at all.

Second, knowing things is only part of the job for most knowledge workers. Another big part is communicating effectively with other people, and communication is a subtle art. I could study the transcripts of a master salesperon’s interactions with customers to the point that I’d know what words to say, but I assure you that I’d still be a horrible salesperson. In their excellent book The New Division of Labor Frank Levy and Richard J. Murnane describe how ‘complex communication’ is one of the things that people just do better than computers, and so one of the things that will prevent machines from taking over all the jobs any time soon.

As it turns out, effective supply chain management involves a great deal of both specific knowledge and complex communication, even after DSM is in place. As the IWeek article states,

“Demand-signal analysis may provide earlier and more granular insights, but the problems manufacturers and retailers are trying to solve have been around for a long time. It’s essential to have people steeped in industry knowledge leading this effort.”

The companies described in the article are thus making a very smart choice by being decentralized in this regard, even after snazzy new technology becomes available. Examples like this one educate me, and make me more cautious about assuming any broad trend toward either centralization or decentralization as we continue to inject more and more technology into every industry in the economy.

I’m confident that because of IT we’re in the middle of a period of broad and deep exploration about the ‘best’ design for a given organization, and a time of increased experimentation with allocating decision rights. And I’m not sure about where we’ll end up.

Are you?  Do you think technology is generally leading us to either greater centralization or decentralization of organizations?  Leave a comment, please, and tell us what you think and why you think so.