(Lead) Customers Codify & Create Knowledge

There’s an emerging trend that’s worth calling your attention to and amplifying. It’s when customers with specialized knowledge, passionate interests, and/or unique access to “ground truth” information capture and codify it in order to share it with their colleagues. I’ve noticed lead customers capturing, codifying, and sharing knowledge in a variety of disciplines recently:

• Doctors codify algorithms for drug dosages.

• Scientists and engineers capture, encode, and model data feeds from the environment.

• Design engineers capture and maintain company-specific practices and manufacturing machine-specific tolerances.

• Consumers who care about where their money is going categorize and monitor their cash flow.

Doctors Codify Context-Specific Formulations. One of the best stories I’ve heard recently was told to me by Dr. Nat Sims from Mass General Hospital. It was also referenced in a story on lead user innovation in the New York Times. Nat is a cardiac anesthesiologist. He explained that formulating and administering exactly the right combinations of today’s sophisticated drugs has become increasingly complex. Doctors make judgment calls in real time in the operating room. Well-trained and knowledgeable professionals follow those instructions and add their expertise to ensure safe and appropriate drug administration. Every group of specialist practitioners develops a set of best practices for mixing drugs for different types of patients in different circumstances. This becomes part of the implicit knowledgebase of the specialists in each hospital’s practice. As Nat and his colleagues invented a much more automated drug formulation and delivery system, they realized that the best people to create and maintain the algorithms for drug administration were the expert practitioners themselves. Nat’s contribution as a lead user in the invention of the system is noteworthy. But equally impressive was the organizational accomplishment of convincing a group of busy doctors, nurses, and pharmacists to codify and maintain their applied expertise in a set of algorithms and after action reviews. I asked Nat whether these drug formulations were being shared across medical institutions yet. He is hopeful that cross-organizational knowledge sharing will be a logical next step in the evolution.

Environmental Engineers Can Model Complex Ecosystems. Recently I’ve been interviewing engineers who are engaged in “green engineering” projects in a variety of disciplines, including water quality management, emission reduction, and energy conservation. One of the patterns that has struck me as I learn about how they do their jobs is the way in which they create and share complex ecosystem models. These engineers gather multiple streams of real-time data in parallel from a large variety of sensors: chemistry, temperature, pressure, density, humidity, weather, and so on. They start with hypotheses of how all of these elements interact, and they use the real-time data feeds and their ability to analyze the data to see new patterns, perfect their models, experiment with controls, and learn from feedback. The quality of the ecosystem models that it’s possible to create with today’s distributed sensors and low-cost computing platforms is better than anything that’s been possible heretofore. As these instrumented systems continue to run and continue to gather data—whether it’s the water quality of a lake in Argentina, an industrial air conditioning system in Malaysia, or a hydrogen powered locomotive moving freight cars in a port—the environmental models get better and better, and the patterns of the data being gathered create a virtuous learning loop for the practitioners, giving them insights that nobody has ever been able to discern before now.

Design Engineers Create and Share Design Metadata. The software tools that design engineers use to do their jobs have become very sophisticated. Whether they are designing bicycle parts, microchips, or industrial conveyor systems, today’s engineers typically use sophisticated design and modeling software to create and modify their designs. In the past, each design phase was discrete: as you moved from conceptual design to detailed design to manufacturing designs, you often took the work your colleagues had done, converted that into a format you could use (often by working with their outputs to create your inputs), and created the next design iteration. Important assumptions and constraints were traditionally captured in design notebooks and as annotations and data sets that accompanied the actual designs. Today’s engineering design tools are increasingly integrated. You can move relatively seamlessly from one design stage to the next, transferring many of your annotations and assumptions along with the electronic designs themselves.

What we’re seeing now is that each group of engineers in a specific company or project team is capturing and documenting their metadata and assumptions in ways that make it much easier to reuse that information and to retain the assumptions in a living, rather than artifact format. The annotations and assumptions are not only company-specific or client-specific. There may even be specific instructions for the specific manufacturing tool that will be used by a sub-subcontractor on a different continent in a different culture. When you ask these engineers whether they’d like to be able to reuse and to learn from each others’ data, they all agree that it would be an incredible time saving to know a priori what knowledge had been gained from the last similar project, machine tool, and/or component usage.

Consumers Are Codifying and Sharing Data. On the consumer front, I’ve noticed a mini-stampede taking place towards the use of social networking tools like Geezeo that enable consumers to categorize their spending and to analyze their investments. In the past, managing our personal finances has been an extremely private undertaking. As individuals, we may have used Quicken to categorize our spending and/or a personal portfolio tracker to monitor our various pots of invested money, savings, and the value of our assets (real estate, paintings, antiques, etc.). Now there’s a newer set of Web-based tools that provide similar capabilities—you can automatically categorize your monthly spending, and/or you can aggregate the assets from your various accounts to get a consolidated picture. What’s different about these new tools is that they are shared. As you categorize your spending, you get to see how your spending compares to that of others in the same socio-economic group. You can compare the allocations of your portfolio with that of others. All of this aggregation is anonymous. But it’s valuable. Valuable to each individual to see how their habits compare to others. And, of course, immensely valuable to the service providers who are able to aggregate and see patterns that enable them to make tailored offers.