Boyle and the Pyx

We find the origins of modern science with Robert Boyle. He gave us a literary technology based on public demonstrations with expensive apparatus: the air pump. Elements of Boyle's approach are inherent in an earlier tradition. The Trial of the Pyx has been conducted at least since 1279. A grab sample of coins are collected during the minting process and locked in the Pyx. At regular intervals a sub-sample of these coins are publicly tested by members of the Worshipful Company of Goldsmiths using pre-established exemplars of quality and standard dimensions. Here we have all the elements of Boyle's science: experts, public performance, abstract standards, and exemplars. We also have some pretty impressive scientific method that well predates Bacon.

Stigler, Stephen M. (1977). Eight centuries of sampling inspection: The trial of the Pyx. Joural of the American Statistical Association. 72(359): 493-500.

"Know How"... It's not Scottish

I have severe reservations about the Economist Intelligence Unit white paper, "Know how: Managing knowledge for competitive advantage":

1. Conflation of Business Intelligence (BI) and Knowledge Management (KM). I would argue that these two technologies/strategies represent very different things. BI is essentially the technology and practice of integrating and interpreting transaction data. The base of BI is always numbers collected in some manner. The base of KM is something different but we're not sure what this something is. Indeed, articulating the base of KM is a key aspect of the current problem facing decision makers.
2. The importance of accuracy. In terms of knowledge management, "accuracy" is a loaded word. I find it remarkable that as the volume of data available to decision makers increases--just look at the data storage market--they are struggling to find the right information. Pundits have claimed that the quality of information has to be improved: it needs to be more accurate or be of higher quality. Metaphorically, this argument assumes that there is a very valuable information needle contained within a haystack of data. A more appropriate description may be that there is a large pile of needles, some of which are better than others. A manufacturer would create standards and quality initiatives to sort the right ones from the wrong ones. Knowledge management is the informational cousin of this sorting process; it focusses on the process, not on the information itself.
3. Schlumberger. It's completely unsurprising that Schlumberger would meet with KM success. The cause of that success may be less related to the actual initiative than the underlying culture. Schlumberger has always excelled at extracting and using information. Bowker describes how they essentially established a grid of classification and organization that could be imposed upon any geographic location in the world. The corporate culture would certainly support similar initiatives within Schlumberger's own social location.
4. Sarbanes-Oxley. What's good for the investor may not be good for the information user. SOX has forced public companies to be excessively diligent about both the data they collect and the way in which they collect the data. Section 404 ensures that executives have fully auditable processes in place for collecting and using data. Unfortunately, it doesn't help them separate needles from hay. Furthermore, SOX has been bolted on to existing standards for financial reporting. The affordances and constraints of these standards shape the ways in which public companies actually "know" their environment.
5. Collecting tactical data. That's the problem with the tacit: it's tacit. Fingertip knowledge inherently evades classification and description. Some tasks--such as sales or carpentry--involve a high level of performance that can only be honed through doing. Best practices may assist employees in this performance but companies pay little attention to the ways in which things get done. Instead, they focus on just getting them done. Few organizations support the required documentary practices of anthropologists or sociologists. Concepts such as epistemology, methodology, and ontology have little place in the modern organization.
6. Information requirements. It's a funny thing to pre-establish information requirements. Information inherently leads to other requirements resulting in a vicious cycle of data needs. How do people know what information they need to answer questions they don't yet have? A better approach may be to ask what questions need answered. It may seem old fashioned, but the whole notion of hypothesis, test, and proove is quite valuable. The questions for researchers and executives concern how we create hypotheses and how we create proof (i.e., testimony, community, oligarchy, apparatus, classification, etc.).
7. Mobile delivery. The issue of mobile technology and KM bothers me. And I don't know why. Mobiles have crucial affordance i.e., delivery documents directly and ubiquitously to the user. The problem concerns what people actually do with the information on their mobile. It may be available but it is not useful. Individuals cannot use mobile devices as part of negotiation; people can't share concepts and symbols communaly with a mobile; an individual doesn't use a mobile to write a paper or prepare a report--better tools exist for that kind of thing. So do people do with a mobile: make calls, get their email...
   

Wishlist for OpenOffice.org

Tools are crucial. I've become very conscious of the tools of early engineers. While craftsmen had many of the hand-tools that we're now familiar with (with the notable exception of the screwdriver) engineers were stuck with the basics: the compass, dividers, rulers, and the pen. The role of these tools is evident in the early literature on ballistics. Despite the artistic rendering of parabolic trajectories, early scientist/engineers insisted on using Aristotelian ideas of flat trajectories. This bias was reinforced by their Euclidean tools: rulers for trajectories and compasses to draw circular transitions.

I have the same problems; I'm constrained by my tools. Of course, the tools that I use are primarily for word processing, document production, and research. I use a variety of different applications. I still haven't found the perfect open-source equivalents for my needs. OpenOffice.org is good. But it's not perfect. Specifically, I want to see two features (although they're very big features!):

1. Integrated citation management. It would be great if Writer had an onboard feature that worked like EndNote or ProCite. Personally, I think that cite-while-you-write is crucial. The feature would have to support various customizable citation templates. It should also enable Z39.50 for integration with other IR products. Priorities for integration include Web of Science, JSTOR, MedLine, and OCLC.
2. Stats. The analytics tool pack for Excel is okay. I think that Calc can do much better. I imagine a scenario where the user selects a particular template for Calc that results in a four page workbook. The first page support data definitions, the second supports data entry, the third shows analysis output, and the fourth shows an ongoing syntax log. Ideally, the feature set will be close to rudimentary SPSS. Some of the base functionality could come from the SalStat product.