Getting better at the craft of knowledge work

drafting CADHad lunch with my friend Buzz Bruggeman, CEO of ActiveWords, this week. Got a chance to look at some of the improvements in the pipeline. Not quite enough to persuade me to move back to Windows, but I do wish there was something as clever and powerful for OS X.

It led me to thinking about what makes some people more effective leveraging their tools and environment. Most of the advice about personal technology seems to focus on micro-productivity; how to tweak the settings of some application or how to clean up your inbox.

ActiveWords, for example, sits between your keyboard and the rest of your system. The simple use case is text expansion; type “sy” and get an email signature. Micro-productivity. If you’re a particularly slow or inaccurate typist and send enough email, perhaps the savings add up to justify the cost and the learning curve.

Watching an expert user like Buzz is more interesting. With a handful of keystrokes, he fired up a browser, loaded the New York Times website, grabbed an article, started a new email to me, dropped In the link, and sent it off. Nothing that you couldn’t do with a some mouse clicks and menu choices, so what’s the big deal? I’m a pretty fair touch typist; how much time can you really expect to save with this kind of tool? Isn’t this just a little bit more micro-productivity?

There’s something deeper going on here. What Buzz has done is transform his computer from a collection of individual power tools into a workshop for doing better knowledge work. It’s less about the tools and more about how you apply them collectively to accomplish the work at hand.

How do you study knowledge work with an eye toward turning out better end results?

We know how to do this for repetitive, essentially clerical, work. That’s the stuff of the systems analysis practices that built payroll systems, airline reservation systems, and inventory control systems. Building newer systems to take orders for books, electronics, and groceries still fall into the realm of routine systems analysis for routine work.

Most of this, however, isn’t really knowledge work; it’s factory work where the raw material happens to be data rather than steel. So the lessons and practices of industrial engineering apply.

What differentiates knowledge work from other work is that knowledge work seeks to create a unique result of acceptable quality. It is the logic of craft. One differentiator of craft is skill in employing the tools of the craft. Watching Buzz work was a reminder that craft skill is about how well you employ the tools at your disposal.

How do we bring that craft sensibility into our digital workshops? How do we create an environment that encourages and enables us to create quality work?

The way that Buzz employs ActiveWords smooths transitions and interactions between bigger tools. It also shifts attention away from the specifics of individual tools and towards the work product being created.

Consider email–a constant thorn for most of us. You can treat each email as a unique entity worthy of a unique response. You can perform an 80/20 analysis on your incoming email flow, build a half dozen boilerplate responses, program a bot to filter your inbox, and hope that your filters don’t send the wrong boilerplate to your boss.

Or, there is a third way. You can perform that 80/20 analysis at a more granular level to discover that 95% of your emails are best treated as a hybrid mix of pure boilerplate, formulaic paragraphs that combine boilerplate and a bit of personalization, and a sprinkling of pure custom response. Then you can craft a mini-flow of tools and data to turn out those emails and reduce your ongoing workload.

I can visualize how this might work. The tools are an element, but I’m more intrigued by how to be more systematic about exploring and examining work practices and crafting effective support for knowledge work.

Have others been contemplating similar questions? Who’s doing interesting things worth exploring?

Review: Filters Against Folly

Filters against follyFilters Against Folly: How To Survive Despite Economists, Ecologists, and the Merely Eloquent Garrett Hardin

You never know which books and ideas are going to stick with you. I first read Filters Against Folly in the early 1990s. Once a month, the group I was with met for lunch and discussed a book we thought might be interesting. I wish I could remember how this book got on the list. I’ve given away multiple copies and continue to find its approach relevant.

Some of the specific examples are dated and I think Hardin goes too far in some of his later arguments. What has stuck with me, however, is the value of the perspective Hardin adopts and the process he advocates.

We live in a world that depends on experts and expertise. At the same time, whatever expertise we possess, we are ignorant and un-expert about far more. Today, we seem to be operating in the worst stages of what Isaac Asimov described in the following observation:

There is a cult of ignorance in the United States, and there has always been. The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that ‘my ignorance is just as good as your knowledge’.

Hardin offers a practical way out of this dilemma. We need not simply defer to expertise, nor reject it out of hand. Rather than focus on the experts, Hardin shifts our attention to the arguments that experts make and three basic filters anyone can apply to evaluate those arguments.

Hardin’s fundamental insight is that as lay persons our responsibility is to serve as a counterweight to expert advocacy; the expert argues for “why” while the rest of us argue for “why not?” It is our role to “think it possible you may be mistaken.”

The filters are organized around three deceptively simple questions:

  • What are the words?
  • What are the numbers?
  • And then what?

When looking at the language in advocacy arguments, the key trick is to look for language designed to end or cut off discussion or analysis. Of course, in today’s environment, it might seem that most language is deployed to cut off thinking rather than promote it. Hardin offers up a provocative array of examples of thought-stopping rather than thought-provoking language.

Shifting to numbers, Hardin does not expect us all to become statisticians or data analysts but he does think we’re all capable of some basic facility to recognize the more obvious traps hidden in expert numbers. That includes numerate traps laid inside expert language. In Hardins estimation “the numerate temperament is one that habitually looks for approximate dimensions, ratios, proportions, and rates of change in trying to grasp what is going on in the world.” Both zero and infinity hide inside literate arguments that ought to be numerate.

The Delaney Amendment, for example, forbids any substance in the human food supply if that substance can be shown to cause cancer at any level. That’s a literate argument hiding zero where it causes problems. The numerate perspective recognizes that our ability to measure  improves over time; what was undetectable in 1958 when the Delaney Amendment was passed is routinely measurable today. The question ought to be what dosage of an item represents a risk and is that risk a reasonable or unreasonable risk to take on?

Hardin’s final question “and then what?” is an ecological or systems filter. In systems terms we can never do merely one thing. Whatever intervention we make in a system will have a series of effects, some intended, some not. The responsible thing to do is to make the effort to identify potentially consequential effects and evaluate them collectively.

To be effective in holding experts to account, we must learn to apply all three of these filters in parallel. For example, labeling something as an “externality” in economics is an attempt to use language to treat an effect as a variable with a value of zero in the analysis.

For a small book, Filters Against Folly offers a wealth of insight into how each of us might be a better citizen. The questions we face are too important to be left in the hands of experts, no matter how expert.

A closer look at integration across organizations: thinking about coupling

Railcar CouplingWhen people ask me why I did something so strange as to leave a perfectly good career and get a Ph.D., the story I tell is this.

I designed and built information systems meant to improve the processes or the decision making of large organizations. I was troubled that organizational staff and managers routinely ignored the systems I created and continued running their departments and organizations pretty much as they always had. Either my designs were flawed or users were stupid (I’ll leave it to you to guess which hypothesis I favored).

I talked my way into a program—which involved explaining away aspects of my transcripts—and began hanging out with smarter people who were exploring similar questions about how organizations, systems, and technology fit together. This is the beauty of doctoral study; no one pretends to have the answers, everyone is trying to figure stuff out and, mostly, everyone wants to help you make progress.

This smart group led me toward the branch of organization theory and development that treated organizations as complex, designed, systems in their own right. The early days of organizational behavior and design as a discipline sought the “one best way” to organize. Paul Lawrence and Jay Lorsch of the Harvard Business School opened a different path; organizations should be designed to fit into and take advantage of the environments they operated within. In their seminal 1967 work, Organization and Environment, they made the case that effective organizations struck and maintained a balance between differentiation and integration. Where did you carve the organization into pieces and how did you fit the pieces together? Management’s responsibility was to make those decisions and to keep an eye on the environment to ensure that the balance points still made sense.

Two things make that managerial balancing responsibility far more difficult. One, the rate of change in the environment. Moderate pendulum swings have been replaced with what can feel like life inside a pinball machine. Two, the role of technology as an integrating mechanism that now spans internal and organizational boundaries.

Set the rate of change issue to the side; it’s well known even if not well addressed.

The technology links knitting organizations together were not something carefully contemplated in Lawrence and Lorsch’s work. Integration, in their formulation, was the task of managers in conversation with one another to identify and reconcile the nature of the work to be done. It was not something buried in the algorithms and data structures of the systems built to coordinate the activities of different functional departments—logistics, production, distribution, marketing, sales, and their kin—comprising the organization as a whole. Change in one function must now be carefully implemented and orchestrated with changes in all the other functions in the chain.

Electronic commerce further complicates this integration challenge. Now the information systems in discrete organizations must learn to talk to one another. The managers at the boundaries who could once negotiate and smooth working relationships have been displaced by code. Points of friction between organizational processes that could be resolved with a phone call now require coordinating modifications to multiple information systems. That couples organizations more tightly to one another and makes change slower and more difficult to execute, regardless of the willingness and commitment of the parties involved.

An example of how this coupling comes into play surfaced in in the early days of electronic data interchange. A grocery chain in the Southwestern United States agreed to connect their inventory and purchasing systems with Proctor & Gamble’s sales and distribution systems. P&G could monitor the grocery chain’s inventory of Pampers and automatically send a replenishment order. In order to make those systems talk to one another, P&G was issued a reserved block of purchase order numbers in the chain’s purchasing systems. Otherwise, replenishment orders from P&G were rejected at the chain’s distribution center receiving docks because they didn’t have valid purchase order numbers in their systems.

Now, these information systems in two separate organizations are intertwined. If the grocery chain upgrades to a new purchasing system and changes the format of their purchase order numbers, P&G’s sales department and IT department are both affected. Multiple that by all of your trading partners and even the simplest change becomes complex. Decisions about strategic relationships stumble over incompatibilities between coding systems.

We spend so much attention to the differentiation side of the equation that we overlook the importance of integration. Half a century ago, we had insights into why that was ill-advised. Maybe we’re overdue to take a closer look at integration.

Crumbling pyramids; knowledge work, leverage, and technology

PyramidThe consulting pyramid model needs to take its place alongside the monuments that gave it its name as a pretty but now obsolete structure. Making your living selling expertise by the hour is inherently self-limiting; you have to find a source of leverage other than the number of hours you can work or the hourly rate you can charge.

The default strategy in the professional services world—consulting, lawyering, auditing, and the like—has been to collect a set of apprentices and junior staff who will trade a portion of their hourly rates for the privilege of learning from you. It’s a reasonable tradeoff, a nice racket, and has supported the lifestyles of many a senior partner.

The last 25 years of technology development has eroded the foundational assumptions of how productive and effective knowledge work is best done. In the process the balance between learning and performing that made the leverage model make economic sense for both professional services firms and their clients has been upended. The failure to recognize this shift means that firms, their staffs, and their clients are all working harder and realizing less value than they could.

There are two elements to this erosion. The first is the challenge that today’s technologically mediated work environment imposes of making knowledge work difficult to observe. I’ve written about this problem of observable work elsewhere. In professional services, much of the apprenticeship activity is predicated on the ability of the more junior staff to watch and learn. If it’s hard to watch, then it’s hard to learn.

The second element is the increased productivity of the individual knowledge worker that technology enables. This may seem paradoxical. Why should the level of productivity be a challenge to the basic leverage model? Because leverage depends on being able to identify and carve out meaningful chunks of work that can be delegated to an apprentice.

It’s my hypothesis that changes in individual productivity clash with finding appropriate chunks to delegate. Often, the junior apprentice work was a mixture of necessary busy work with time and opportunity to inspect and understand what was going on and offer suggestions for options and improvements.

If technology eliminates or greatly reduces the necessary busy work, then the apprenticeship tasks begin to look a great deal more like training and development. The more training-like the task appears, the more difficult it becomes to charge for that person’s time and the more difficult it becomes to place them in the field where they must be to obtain the knowledge and experience they need.

The old cliche in professional services work is that the pyramid consists of “finders, minders, and grinders.” Built into this cliche is a set of assumptions about work processes anchored in a world of paper and manual analyses. That world is long gone, but we still haven’t updated our assumptions.