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October 30, 2006

Nitwits and Nitpicks in the Garden of Good and Evil

In the rankings of the safest and most dangerous American cities compiled by Morgan Quitno Press, St. Louis ranked as the most dangerous city in the country.

The ranking, being released Monday, came as the city was still celebrating Friday's World Series victory at the new Busch Stadium.

The second most dangerous city was... Detroit.

Of course.

Scott Morgan, president of Morgan Quitno Press, a private research and publishing company specializing in state and city reference books, said he was "not surprised."

Aside from Morgan failing to show any evidence that he'd been on the planet for the last week, say for example, in St. Louis, as were a bunch of guys from Detroit, just what in the hell did he mean by that? Bitch.

Source: http://news.yahoo.com/s/ap/20061030/ap_on_re_us/city_crime_list_4

Posted by Malcolm Ryder at 6:34 AM | Comments (0) | TrackBack

October 29, 2006

Changing Performance

Although quality of execution ("QOE") is not the same as "performance", in the minds of many managers QOE's Deming Cycle has long ago taken up permanent residence as the basis for performance improvement.

In that view, the mantra of PLAN DO CHECK ACT (i.e., design, execute, measure, and adjust) is a huge reminder that while the "activity" half of work (PLAN DO) is always to be attended formally and closely, the "achievement" half (CHECK, ACT) is not just a "gimme".

From that perspective, managers have a better view of how to make things work not just well but, due to the repetition of the cycle, also with continuous improvement.

Yet forty years after the cycle's debut, the challenge of ever-increasing organizational complexity makes the effectiveness of this advice harder and harder to realize. For management solution-builders like CEO Jonathan Becher of Pilot Software, a reinterpretation of management focus seemed necessary and timely enough to even build his company around. Becher's model -- MOTIVATE, MANAGE, MONITOR, MEASURE -- shifted emphasis from the "Plan / Do" of Deming to a sensitivity about how communication brings workers into the realm of reliable support for the Plan. As a result, purposecould become more consistently followed by execution.

Both of those men's approaches convey value through completeness in a scripted sequence of management influences. Yet both must be grasped within an even larger context of what more completely accounts for "performance".

What really controls the generation of events and their results is the interrelationship of internal forces in the organization; and what is often overlooked is the degree to which those connections are leverage points that are vulnerable to unscripted change. The following illustration's high-level view exposes these points of leverage:

In representing a cycle, part of how this picture works is of course how it positions the leverage points in question, which are Interpretation, Participation, Examination and Prioritization. Here, they take up spots in between the more conventional subjects of management attention.

Each of the "new" items significantly constrains the influence of managed strategy, planning, execution and evaluation. It is relatively easy to grasp that defects, omissions, exceptions or errors at any one of these four constraining points will potentially delay, disrupt or at worst cripple the cycle, despite attention to the more standard concerns. Yet all it takes to introduce those interruptions is competition from some recognized alternative -- in goals, methods or needs. Given that, can we say that we're managing things if we aren't attending to the four constraints on an explicit and sustained basis?

Sometimes the alternative comes from outside of the manager's field of view; sometimes, from deeply within.

For example, as now seen, a Plan is not a transcription of a Strategy; instead, it is more nearly a transcription of an interpretation of strategy. Or said better, interpretation is a precedent of the plan. People don't automatically interpret things the same way; and naturally, politics can play a heavy hand in which interpretation may have the best shot at prevailing. The point is, do we know why people are interpreting things the way they do?

And consider the phenomenon of a second opinion; if interpretation imposes a competing sense of credibility, opportunity or belief on the strategy, the prior anticipated plan will again likely risk being changed.

Further along in the cycle, at the point of Participation, a deeper look at people is also due.

Participation is perhaps the "intermediary" point that ordinarily gets the most attention. But what often gets overlooked in that attention is the distinction between productivity management and change management -- with the big question being how we know that people will really adopt and execute the plan.

These days it is still relevant and popular to understand productivity from the viewpoint of running healthy "systems". And most typical in our thinking about that is the mantra of "people/process/technology". That describes the three dimensions of the systems that we think are both useful and manageable -- plus it offers the encouraging claim that technology will make things more likely do-able. But the catch is: people have to want it to. If they don't want it to, a lot can change. (As noted frequently elsewhere in Archestra discussions, the People/Process/Technology mantra is essentially flawed and should be replaced with the mantra of People/Events/Technology, further superceded by Assignments/Processes/Configurations. But for now we'll leave that alone, and just take advantage of the focus on people.)

What about change? Underneath Deming's "DO", and between Becher's "MOTIVATE" and "MANAGE", people decide what they are going to actually bring to the party. They make the decision as a result of comparing what they are being offered as "next" versus "now". This will not just be a simple comparison of better versus worse, with "now" being the benchmark; "now" may not even be clearly good or bad.

Instead, the comparison will be about whether being involved as requested (for example, by the plan) is a difference that the person can prefer. So what in particular is getting compared?

Both "now" and "next" present possible but alternate realities that elaborate, in detail, the general picture below:

For the individual person, the issue is to reconcile how they already are now with how they are going to be next. Any part of the above cycle that changes -- whether that be expectations, intent, or observed effect -- can introduce a new preference, dissuasion, or even some cognitive dissonance such as pitting their desires against their ideas or against their ethics.

In this second picture, as in the first, the points of influence are interrelated by position. To the point, Acceptance is always preceded by Expectations. Then, when Acceptance is followed by Intent, people really arrive as drivers of the activity that eventually will be studied for determining performance.

What managers need to know is that the Expectations segment of the cycle is affected by both Awareness and Acceptance. If either of those changes, expectations change too. Likewise, intent will be sensitive to both Acceptance and Actualization -- so managers have to provide corresponding opportunity that moves Intent to real action. These sound a lot more like issues of leadership that managers must fit in.

Meanwhile, in the end, the individual's mentality about his/her requested role must track back beneficially to the participation needed in the point between planning and execution. That's what the two illustrations together reveal.

Posted by Malcolm Ryder at 6:37 AM | Comments (0) | TrackBack

October 28, 2006

Execution as Strategy in the Garden of Good and Evil

And that's how you do that trick.

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Now, for those of you with short attention spans:


Widely cited as the greatest defensive player of all time, it's The Wizard of Oz.
Talking points:
- In the matter of winning games, Ozzie Smith generally accounted for a greater differential in his team's runs and the opponent's runs than did his numerous teamates who were paid mainly to swing.
- For the opponent, playing against Ozzie, which meant letting your fat payroll spend a lot of time being ineffective, was one of the most expensive things they had to do each year.
- Ozzie was so good that his salary could have been composed mainly of a fixed number of dollars per percentage points that he lowered his own pitcher's ERA and the opposing hitter's batting average, and he still would have usually been a multimillionaire.
- If you feel compelled to determine the exact number of dollars, get a life.

Concept: Walt Jockety, more or less.
Photos: Google Images thumbnailed these, and I copied them here. Who took the pictures? Beats me. If you know, send me the name and proof, and I'll market them with proper copyright. On the other hand, if you can't tell me who is the actual photographer, regardless of who owns the copyright, then don't bug me.

Posted by Malcolm Ryder at 8:42 PM | Comments (0) | TrackBack

October 21, 2006

Death, Taxes, and the Sure Thing in the Garden of Good and Evil

For St. Louis, clearly it was all over just two days before the end of the regular season. Then it was all over again, the day before the first round. Every one knew it. And it was really all over heading into New York. A complete certainty.

But the worst team good enough to make the baseball playoffs is in the World Series, because it kept beating teams that were better but not good enough.


The Cards have played every single pitch as if anything can happen. Leads are not leads, whether runs or games, except in the moment before the next pitch. The only thing that can absolutely go wrong, they believe, is that you're behind when the rules say there will be no more pitches. Baseball's beautiful certainty is that the reason a team wins a game is because they actually played it.


Concept: Monty Python and the Holy Grail: "I'm not dead yet."
Photo: moi.

Posted by Malcolm Ryder at 6:01 PM | Comments (0) | TrackBack

October 18, 2006

A Beautiful Mind

Decision-making under uncertainty is the usual line item in the list of key management challenges, but the flip side is no less worth studying too. Our colleague Bruce MacEwen at Adam Smith, Esq. composed an excellent survey of the question, "How Do You Decide When To Decide?"

My immediate observation is a flashback on the old saying, "a work of art is never finished, rather only stopped..." This points directly at an underlying work dynamic of continuous consideration and reconstruction, in which potential components of "meaning" are arriving, examined, and accepted for their relevance to realizing an overall design. In art, the "stopping" question is, has enough of the design been realized to meet the purpose of the design?

In management, the two biggest fears about decisions are typically that they will be either counterproductive or inconsequential. Cutting right to the chase, being counterproductive devalues the investment of assets and resources, and being inconsequential devalues the credibility of the decision maker. This makes the manager's only "acceptable" output one in which there is a related effect that desirably differentiates the future conditions from the past. Here, the "design" at stake is this set of future conditions. But too often observers mistake the wrong thing as the design: decision-makers and their observers focus on some measurable "state" instead of on the new conditions that allow their desired state to occur. As a result, they too easily fail to determine whether the difference made by the decision is the "right" difference until they see the desired result (if they ever do). In looking for the ultimate desired after-effect of the decision, they fail to actually grasp the value of the decision itself. No wonder decisions remain mysterious to many observers.

What must instead be understood is that decisions don't create the target state; instead, decisions enable the conditions that allow or create the target state.

To be fair, a proven business approach to establishing this perspective is to require "justifications" as part of the decision-making process. The justifications call out the terms by which the desired ultimate outcome is reasonable to expect from the circumstances that will prevail if the proposed decision is made. This degree of foresight is wonderful if there is the time to develop it, and MacEwen's article points out the value of prior experience and rehearsal as a critical path to the competency for rapid envisioning.

That's a lesson every good football quarterback or basketball point guard has already learned -- and their practice is a great one to consider here. When using a pass to improve field position, what they are doing in their on-the-field work is envisioning multiple possible future states, setting certain events in motion towards those states, and in the heat of the subsequent flow of moments they examine incoming "data" and make reactive adjustments to raise the probability of the "still most feasible" desireable state to its maximum. The final pass, also presumably caught, locks in the conditions for that state.

How do they get that competency to an effective level? By having to do it again and again. But the bigger point is this: what causes them to decide is a moment at which they recognize a high correspondence of probable conditions to the prescribed conditions they expect will be necessary to their goal. Put simply, they compare "reality" to their design.

At this point, another major factor kicks in -- the issue of whether the correspondence seen is "as good as it will get"... Here, I'm always reminded of the the saying "perfect is the enemy of good enough" and it points to another common but correctable mistake. The mistake here is in failing to evaluate the design independently of evaluating the means of execution. To dramatize the fault in this, consider that perfect execution of a bad design is probably pointless. The exception to that is those occasions in which perfect execution is necessary for actually determining in the first place whether the design is even valid (i.e., "good") for its own purpose. A normal management scenario, however, recognizes the difference between "testing" and "live production", even if it sometimes has to be producing "live" but experimentally.

Even with that exception included, the most important point is that decisions are made in two different dimensions: one in which a design is accepted, and another in which execution of the design occurs. Some decisions may improve execution, but others may actually improve (and/or replace) the design. If execution is already good enough to realize a great design, then execution really is good enough -- and the decision to have that level of execution is probably a pretty good one. The real challenge here is to have great designs in the first place.

In science, Thomas Kuhn explained the phenomenon of "The Structure of Scientific Revolutions", an ongoing story of how a current theory holds sway until evidence (and politics!) forces it to change or be supplanted, sometimes over a span of decades. In another story, Malcolm Gladwell talks about "The Tipping Point" as an event in which the speed of applying evidence against theory is almost literally electric and instantaneously completed, thanks to the availability of previous experience stored in the mind.These interesting stories about decision-making are not intended to portray clairvoyance, yet they both directly describe the importance of having foresight in the form of a design. The practical lesson: commit to a design, and decisions will follow.

Posted by Malcolm Ryder at 9:13 AM | Comments (0) | TrackBack

October 17, 2006

Where's the "System" in Managed Knowledge?

Swimming around in phrases like "knowledge management" is easy until it comes time to actually do the management. The abundance of innovations for promoting the transfer of knowledge from point A to point B is exciting and often dramatically leaps over old barriers to practicality. But just getting knowledge moved around more readily is no guarantee that any significant achievement results from the activity. By comparison, if our wariness of email's excesses is any indication, we gave up on that kind of naivete long ago.

Yet, getting past the romance and denial in the "knowledge management" label is something most users of managed knowledge would like to leave on someone else's To Do list.

If that To Do list belongs to you, a major first step is to note and remember that knowledge exists in an environment, not in a container, and knowledge users roam all over the environment doing spontaneous and therefore frequently unpredictable things. The idea of transferring knowledge from A to B is about hitting moving targets with items you first have to catch.

To start catching the important items, it helps to categorize them according to why they are useful to you and to the presumed recipients. The users themselves also should be categorized according to what we can expect them to do about what they receive. Otherwise, the constant risk is that you don't get the right thing to the right user at the right time, meaning that the effort is somewhat wastful and, what's worse, discrediting to further efforts.

Taking the idea that the recipients should get what they actually need, the high-level perspective on managing knowledge has to do mainly with context-sensitivity. In pursuit of repeatable, optimal, reliable context-sensitivity, the mechanics of managing knowledge have to take care of combining a knowledge "supply chain" capability with a knowledge "value chain" capability. In the picture below, this combination is represented so that the reasons why anyone should care are exposed at the different points between "not knowledge" (lower left) and "full knowledge" (upper right).

In this scenario, most users are "consumers" and hang out at the upper right. Wandering leftward brings more and more responsibility to be a "producer" -- taking what is there and driving it right-ward, or at least making it highly available and suitable for that. Going from left to right, the "Who Cares" factor goes higher and higher. Having an idea is fine, but packaging it makes it more valuable, and targeting it to a matching audience tops out its probable value.

Meanwhile, going from the bottom of the layout to the top similarly sets up responsibilities and value-generation. Here the most important thing is to handle the transformation of data first into information and then on into knowledge. We already have various repositories that we think of as systems for separately managing the three things . That is, we can point at a data base, at an information base, and at a knowledge base. But often users don't distinguish them from each other, counting on a wild array of tools for search and analysis to force transformations (i.e., "interpretations") from whatever base the user starts with.

If knowledge is really being managed, then instead the different responsibilities of the repositories won't be indifferently violated. For example, what should a database contribute to knowledge transfer? At most, a focus on (i.e., selection of facts according to) a defined subject. Heading upward, an information base should emphasize the ability to associate the data with the circumstantial need. A knowledge base emphasizes the perspective of the user who is actually in that circumstance.

If knowledge is systematically managed, there will be clarity about what kind of value is being generated at the different points in the process of making, saving and delivering it, and users will not hop around randomly in this production environment but instead be guided through it appropriately in real-time as they work.

Posted by Malcolm Ryder at 12:33 PM | Comments (0) | TrackBack

October 16, 2006

Harmonic Convergence

Welcome to the harmonic convergence of new-wave KM where, in the (flawed) holy triumvirate of People/Process/Technology, it's sometimes hard to tell whether the people are processing technology, or whether its the technology that is processing the people.

By now, it is already mainstream to think of blending blogs, wikis, search, and business intelligence into a hyperethos of "smarts". IT industry writers such as Michael Vizard at Ziff Davis go so far as to say, "This means that companies for the first time might actually be able to figure out who knows what, and when, within their organizations in real time."

Academics haven't changed their outlook a bit, however. Serious researchers rightly refuse to confuse data, information, and knowledge, understanding that crucial transformations must occur in order to get from data to info, and from info to knowledge. Taking that as a cue, and as the only one that we need, our understanding of a "process" for managing knowledge clearly embraces an expectation that systems will be developed to power and control those transformations.

Is it just me, or do you agree that the Card Catalog is still where the bar got set in knowledge management systems? When I was five years old, I learned how to use it and it worked for forty years virtually unchanged. It even withstood, without blinking, the unmeasured nuclear explosion of bad poetry brought on by the advent of the home word processor. I sometimes wonder why "management" people are so eager to dismiss the discipline of library science and insist on hot dates with so-called "self-managing" or "organic" content.

That is, let's not forget that the user-community for knowledge is not homogenous, and even unscientifically it can be confidently separated into roles like managers, knowledge workers and line workers. In a business, these roles are distinct, are actually held to some standards of accountability, and a lot of what passes as "process" exists mainly to avoid having these roles waste time. These aren't per se "knowledge management" processes -- they're work management processes. Knowledge isn't supposed to be magical; it's supposed to be practical. How does it get that way?

Interestingly, the card catalog is brutally efficient. It just doesn't work well until you learn how to use it. But when you've learned, it works for two key reasons: one, it has built in accounting; and two, everything it knows about is formally published. Let's see: if you were paying someone out of your pocket to be responsible for assuring those two characteristics, do you think you might call them a "knowledge manager"?

I'd sure hate to go into a library where the catalog was missing, the shelves had all fallen over, and anyone could arrive and poke around adding, moving, or removing whatever other materials they wanted. Unless, of course, I was there for recreation. Oh wait, that's why we surf the web!

But surfing on my boss's dime is a little different, don't you think? If newer and newer technology is going to bring about maturity in new-wave KM, the technology is going to have to get primarily focussed on making management processes better, and only secondarily on making more and more spontaneous content sources continuously reachable. In a business, the purpose of KM is to integrate two proceses, content management and work management , in order to improve the content of work! Until then, the convergence of the technology innovations is going to continue increasing the dissonance instead of the harmony, the noise instead of the signal.

Posted by Malcolm Ryder at 9:08 AM | Comments (0) | TrackBack

October 13, 2006

How to measure IT's contribution


Flashback from CFO: Magazine for Senior Financial Executives, Spring, 2005 by Malcolm Ryder

Regarding the notion of estimating how much revenue to allocate to every kind of corporate resource in proportion to each respective resource's contribution ("Revenue Is What Matters," Letters, Fall 2004), I have to wonder what purpose there is to that.

Not being an economist myself allows, perhaps, my view on this matter to spawn a useful question. Namely, without a definition of "contribution" there is no logic to the presumed "proportion," and don't we already know from real life that contribution means impact and that impact is defined by the system of measurement?

What most of us in IT and everyone in science have learned is that we can't talk about impact without talking about complexity, which means talking about interdependencies and frequently about the obscure order found within apparent chaos. If at Company X a $50 spreadsheet program in the hands of a $100K--per-year employee results in a discovery that generates $10 million in revenue, that's a great trick. And yet even if Company Y copies the same set of "resources," it probably won't get the same results.

The reason why accountants have not set or proved the so-called value of IT is because value is not generated by resources but instead by dynamics, and accountants don't measure dynamics. I agree that what is needed is a look at the answers already found in other disciplines.

For example, meteorologists measure systems and motion, such as high pressure, low pressure, and temperature, and from that they can attribute daily and even hourly impact to real causes instead of merely to gases. Likewise, coaches who actually know how to coach can tell you that the influence of the most talented player on the team can turn the team into a loser, where a much lesser talent can influence the team to win and so gets put on the field.
So the key is to break free of the notion of "resource" that is rooted in a concern for corporate property and learn to see that the elemental dynamics of situations are the real resources.

For most companies, the closest they come to this awareness now is their understanding that some company assets, like people and technology, must service something that they call processes, with processes representing the company's hypotheses of desirable dynamics. Logically, then, the process is the closest they come to defining the resource that should claim some of the credit for the revenue. What does the process cost, and how well is it managed?

Malcolm Ryder/Chief Strategist/Renovance LLP/
COPYRIGHT 2005 CFO Publishing Corp.
COPYRIGHT 2005 Gale Group

Posted by Malcolm Ryder at 9:56 PM | Comments (0) | TrackBack

October 9, 2006

The Knowledge Tax

Here in the knowledge economy, life can be very taxing.

Doing research involves looking for:
- things that you don't know exist,
- to get reliable information that is new to you
- about things that you want to know.

So from the start, you have pretty significant uncertainty at the basis of your effort. Moreso, when researching concepts, it's often unclear as to whether the information, terminology, context, etc. that other people have used will allow you to readily recognize the same thing that you were looking for, when it is right under your nose.

So some kind of map is needed to guide you to the right needles in the haystack of research.

A taxonomy is a "custom dictionary" of categories. It tells you which particular terms are going to be used as the "working labels" for certain groups of ideas, for how those groups are related to each other, and therefore for how you might confidently categorize examples of those ideas. So we use the taxonomy (categories) to zero in on examples. Taxonomies create the aisles and shelves in the grocery store of knowledge.

But then there is also ontology. An ontology is more like a belief system -- in this case a set of beliefs about how things "really are" and why they are that way. Ontologies are heavy duty because they do a lot of editing in the same way that we think of "design" shaping things up.

What's interesting about ontologies versus taxonomies is that when a professional taxonomy gets imposed on a cultural ontology, there's a big struggle to see which one of them is going to re-arrange the other more. You don't get arguments about whether a chair is a chair, but you get arguments of another kind.

A taxonomy might look at a space and say "You know, this could easily include, or could be, a playroom."
In the same space, an ontology might not say "there's no such thing as a playroom," BUT it might say "this is a chapel; we don't acknowledge playrooms here; there is no playroom."

Brutally oversimplified, and relative to each other, taxonomies are more data-based, and ontologies are more point-of-view-based. Taxonomies don't care who is looking; ontologies do.

The Free On-line Dictionary of Computing (Denis Howe) says, "Formally, an ontology is the statement of a logical theory..." but it goes on to emphasize that the logic is something shared, like the shorthand lingo of an established social group, which has decided what qualities of something matter the most and can categorize them that way. Our favorite example of this is from an essay by Jorge Luis Borges, cited by Joe Celko in Intelligent Enterprise magazine, called "The Analytical Language of John Wilkins. In it, Borges presents a Chinese Encyclopaedia named "Celestial Emporium of Benevolent Knowledge", which categorized all animals in this way (note various copyrights for Borges and Intelligent Enterprise):

- those that belong to the Emperor
- embalmed ones
- those that are trained
- suckling pigs
- mermaids
- fabulous ones
- those drawn with a very fine camel's hair brush
- others
- those that have just broken a flower vase, and
- those that resemble flies from a distance.

On the other hand, in a taxonomy, there is some presumed (and presumably demonstrable) set of what everyone insists on calling "natural" relationships. More surely, the main distinction is that the relationships respected are "scientifically systematic". The facts in question are the ones that dispassionately glue the classifications to each other and to their sub and sub-sub classes, and would be seen the same way by all scientifically careful eyes.

Oh well, imagine trying to get credit for proving anything, without a taxonomy to stick up for you. It's the price you pay for a better job in the knowledge economy. But cheer up: there's always marketing and its genius of turning taxonomy into ontology... a well-tested way to try to get your money's worth.

Posted by Malcolm Ryder at 4:05 PM | Comments (0) | TrackBack