Obtaining Information from Events and Results, the limitations (a draft)

The purpose of information technology is to capture the result of an event. The result is represented or embodied, in general, in a transaction or a report. For heuristic purposes here, I would include business intelligence, semantic data, and sensor data. Again, this is only a heuristic statement. The data may be minute, “big,” or a meaningful semantic and graphs. Whether data sources are large amounts, submitted to massive parallel processing, and analyzed with NEW statistical procedures those may result in trivial reports such as the sheer raw number of “tweets.” Users, databases, other machines, sensors are even for minute events, consumers and producers of results. A result belongs to data curators, data stewards, DBAs, developers, and testers.

An economics of information can be seen as the effort and expense required to capture a result of an event. The economic costs versus benefits of information are measured by the significance and meaning, and values of results. The economics of data, seen in a simple way compares the probability that the benefits exceed the costs Good or bad, true or false, a representations of results can come from any size system. Designation of “data at rest” or “data in motion” is a distinction without a difference. Any transactional result is an instantaneous report, and a report is a persistent, but not necessarily permanent and can be seen as representation of a transaction. At any instant, data in motion must rest in order to be converted into new data or information, and data at rest must move in order to capture history, become master data, or be archived.

Data integration is a means of cutting the fat from the lean of information. Too often typical enterprise architecture stack diagrams or matrices portray “data” as sitting between business intelligence and applications as in the Federal Enterprise Architecture Framework (FEAF). The FEAF model reduces data architecture to a storage and management function of applications. The “data” element is supported by applications and technology. In contrast to this, Zachman’s framework gives “Data” a cross cutting importance through all layers. Some Zachman diagrams name this first column “What” and other John Zachman diagrams label it “Data.” However, no application is worth more than the result of the data captured. The foundation of systems should be seen in terms of their function, not in terms of a popular sensibility looking for a technology or infrastructure foundation.

It is important how data” is depicted” in any ‘stack’ diagram. No matter how the rest of the application and infrastructural are stood up or configured, the referential integrity and semantic continuity are essential. Representation of where “data” sits or in what “swim lane” it appears, conveys meaning.

Typical IT Stack Diagram

BI, Report, GIS

Data

Applications

Infrastructure

The role of data is minimized in this representation and depicted as supported by the infrastructure, and not as a pervasive, cross-cutting, requirement. Furthermore, the fundamental ground of data is the “semantic layer.” There is no semantic “layer” in a swim lane by itself. Even such a robust software development book as “Design Driven Development” emphasizes the need to ensure understanding of
semantic content of data.

When “glossary” or “vocabulary” words are used to attempt to identify data semantics that does not mean that either is a complete or comprehensive or enterprise approach. A glossary can refer to only the words in a single system, API, or group of applications, or any non-enterprise development. A glossary can have no or little relationship to foundational meanings. Even “semantics” can be assumed to be equivalent to glossary. These views of meaning may reflect a strictly as-is and bottom-up approach to capture concepts that comprise physical data models. However, a to-be and top-down approach starts with a canonical model.

A canonical environment and its semantic derivations are foundations of continuity from data collection to analytics.  Building an ontology (or trying to automate discovery of one) or using Natural Language Processing to see into data and to check on its validity, and organizing data are foundations. Data cannot be analyzed which is not collected in the first place, and data that isn’t collected consistently is probably worthless. The data collected is a result of an event no matter how transient or persistent. The trajectory of data collection is analysis.

Nevertheless, there are three major contradictions in the organization of “analytics.”

  1. Creating and maintaining a “controlled vocabulary” and semantic continuity is possible, but doing so may not keep up with changes needed by users to gain analytical insight.
  2. Making faster and flexible self-service BI applications may be desirable, but doing so may be at done the cost of data quality.
  3. Relying solely on a client’s statement of a data problem may be “business” oriented, but may miss insights into the actual substance of the problem at hand. This is not an IT problem – it is not a problem of too much or too little data – it is a problem of knowing the subject at hand (medicine, health care, customer demographics, geography, housing finance, agribusiness, civil engineering, urban design, linguistics, logic, and all the rest).

The purpose of information technology is not software development for its own sake. Definitions of information technology may just be a list of the means of creating systems and data with emphasis on the technology, and not the information.

Get Smart – From Wearable Technology to Talking

In 1965, Maxwell Smart used wearable information technology to share administrative information. Smart bumbled into action, while the anonymous woman, “Agent 99,” was the rational voice supporting him. Occasionally, he had to use his rotary telephone in his shoe only to confirm her advice from “Control”. The “shoe phone” became a pop culture symbol for things to come. The Smart Phone (maybe named after Maxwell Smart?) has now replaced the Shoe Phone. Then when talking to his supervisor a “cone of silence” was the secure communication space where tactics were discussed. The “cone of silence” was a transparent bubble where what was said could not be heard outside. This is an interesting metaphor for claims about transparency, when there is really secrecy instead.

Get smart means knowing your strengths, which may not come stamped by a post-graduate degree. I have been extremely lucky, because I am quick to synthesize ideas rather than follow procedures, especially procedural computer programming. I hate procedures, but in 1989 my girlfriend at the time taught me how critical administrative procedures are. She taught me why a “two-hole punch” with holes 2.25 inches from center to center in a cardboard file was necessary to organize hard copy (then when “soft copy” did not exist) grant applications. Punching holes in folders was the epitome of administrative manual labor to make this a uniform container for hundreds of documents. Today, there are still valuable non-technical skills needed. As always, proof reading, without software assistance, is a critical manual skill.

Get smart means “emotional intelligence.” This does not come only from reading the book by that name. It means knowing weakness in modulating your emotions. The “civility” discussions are related to that. Historically under Roman law, I was shocked that in displaying administrative discourse a man (then only specially selected men) could be ostracized, flogged, or executed for showing emotion, especially anger. There was no excuse accepted for demonstrative behavior. Those necessary qualities of behavior are still applicable, but without capital punishment. I would have been a goner long ago if it hadn’t been for the indulgence of supervisors. Other GovLoop discussions on conversations are good guides. However, there must be a place for emotion and intuition at work. For example, see here.

Get smart means knowing the interpersonal and political contexts of what is said or written. Reading between the lines is usually required. Plain writing does not necessarily clear that up. Long ago, in, “On Authority” Richard Sennett stated that writing in “active voice” (now perhaps a part of “plain writing”) rather than in anonymous passive voice would be a step forward in responsible communication. However, for example, in the sentence, “Today the House Committee on …..decided that ………legislation creates jobs” that active voice tells you nothing. Making mistakes and political decisions to handle linguistics mistakes are significant too. At HUD, I was very personally caught up in missing “,”, which I did not create, in a homeless program regulation. For want of a comma, many necessary shelter operating eligible funds were excluded. The missing comma kept me busy answering regulatory questions to the contrary. When I recommended a “technical correction” in the Federal Register be published to correct this at that time no one did anything about it. That was very stressful and disappointing for me, even though as a result it kept me in contract directly with homeless shelter providers.

Get smart means paying attention to co-workers, friends, family, spouse, or a partner. Nothing brings more stress to interpersonal relationships than carrying all the above home after work. Lingering stress makes for bad relationships. It is not easy, for men anyway, to focus on maintaining relationships when personal values and professionalism are at stake. Asking and answering the question “what do you do,” are not foundations for relationships. This GovLoop essay on conflict resolution has good advice important in professional and interpersonal contexts.

My point is only that being adept at some fundamental skills and attitudes is vital. They are not as fun as purely technical ones, however, they open more doors to doing technical stuff. After an eon I haven’t mastered any of these. Nevertheless, computer science and data science have caught up to me, i.e. recognition that their foundations are logic and linguistics, which establish coherent meanings of data. I am not a DBA or programmer because those are my weaknesses. I more or less know where a semi-colon goes in a sentence but not in script. I have decades of technical experience with GIS, SPSS, and SAS (a little with R), but don’t get Python or JavaScript. Natural language processing and Graph models play to my strengths.

Get smart means getting smart. Being aware of all this takes work. Being smart about the law, political motivations, and social context is part of our job. The old fashioned means of citizen engagement was and is talking to citizens and one another. There is no doubt that some of us are more comfortable and adept at that than others. Max stumbled when he talked, and got better advice from “Agent 99” than from any info coming in over his wearable device. Rather than being a last resort, talking should be first. I know I’d rather send email because I communicate better in writing, I think. Keep on talkin’.

Robots, IoT, Drones (RID) and Zombies

Robots, IoT, Drones (RID) and Zombies

Dennis Crow
June 2, 2015
https://www.govloop.com/community/blog/robots-iot-drones-rid-zombies/

The future is hard to predict when the present is torn between the living, the living dead, and the never have lived at all. We don’t know the demographics of robots, sensors of the Internet of Things (IoT), learning machines, and drones. Of course, the count of zombies is unknown because they could very well be increasing at any time. Robots, Internet of Things, and Drones (RID) is advertised as new technological miracles.

If we think that humanity lives in valley of obsolescence, we are asked to favor machines or zombies. My acronym of RIDz points to a “rise of the machines” and the decline of the “human” species. It is clear that the “last hope of humanity” resides in women facing great danger even at the bottom of the technological and human barrel. (See: “Mad Max, Fury Road”) Zombies fill the gap between the still living and the never living. If you watch CW’s program, “iZombie,” the young female seems very much alive and cozies up to the dead in the coroner’s center of a hospital. Brains, situated in an antiseptic environment, make convenient snacks and carry out. Because she works in a hospital, one wonders if she will hook up with surgical robots and medical sensors – but not with the doltish medical records system –instead of the other zombies who are “coming out.” Literally, closer to home, the State of Kansas made zombie apocalypse an acceptable training exercise for the State militia. There are probably private militias doing that as well.

RID’s role in administration is rapidly increasing for medicine, “smart cities,” retail, and farming. This trend exponentially increases without much oversight in spite of FAA’s futile attempts. In IT publications, RID analyses and advertising themselves are constant streams of data. Telemedicine has been used for many years. Now doctors can remotely do surgery, but not through an autonomous robot. Retail sales can be tracked in near real time when and where customers have bought and are sent immediate discount notices based a few meters of location. Agricultural agencies or firms can collect boundary, soil, and crop yield data via RID.

Again zombies exempted, “cybernetics” has been the name of RID since the 1950s. You probably know that a “Universal Turning Machine” (1936) combined an analogue computer and a robot that could perpetually compute and manage itself. (Stuart A. Umpleby, A Brief History Of Cybernetics In The United States, Research Program in Social and Organizational Learning, George Washington University, 2008; Slava Gerovitch, From Newspeak to Cyberspeak: a history of Soviet Cybernetics, 2002) In 1950, the father of cybernetics defined information: ”information is a measurable quantity, and that it can only be studied on a statistical basis.” In the 1960s, Gregory Bateson emphasized that “…the subject matter of cybernetics is not events or objects but the information ‘carried’ by events and objects. We consider the objects or events only as proposing facts, propositions, messages, precepts, and the like.” “Machine learning” is a feedback loop, which is metaphorically applied to the long-ago electrical mechanism of a thermostat.

The industrial dreams of the past still shape the technological labor for the future. At the 1939 World’s Fair, General Motors set up a gallery where people could see interstate highways and self-driving cars; Westinghouse created the “Electro” fake robot, which was a precursor to the iconic B9 robot on “Lost in Space in 1965. These were the fantasies now coming true of a technically skilled household or administrative assistant. The “iRobot” company (www.irobot.com) “designs and builds robots that make a difference in people’s lives.” Either for cleaning a human’s house or for killing them through military applications, iRobot makes “unmanned ground vehicles [to] reduce risk to personnel, operate downrange, report data and deliver predictive intelligence/ISR.” ISR is defined as “an activity that synchronizes and integrates the planning and operation of sensors, assets, and processing, exploitation, and dissemination systems in direct support of current and future operations.” (www.thefreedictionary.com). In “iZombie,” the character ‘Liv Moore’ — aka “live more” (Rose McIver) –inherits the memories of brains she eats to preserve her humanity, and she helps solve crimes, while trying to protect the city from new conniving smart zombies. Today’s evolved earthly zombies differ from the emotionless body snatchers dropped from space, in the original movie “Invasion of the Body Snatchers,” (1956), who really served no purpose. Now brains are for eating, and not for extending science and culture. Robots are a menace to humanity, and not a “labor saving” device.

What administrative purpose does RID serve? Robots, drones, and the internet of things populate the taxonomy of machines. Moreover, they are expected to learn, whether living or not, which has been their purpose since their advent of cybernetics. Since Charles Babbage maybe, “thinking machines” have been personified and designed for personal and administrative functions. In an insightful article, “If Algorithms Know All, How Much Should Humans Help?” Steve Lohr goes after – what used to be called “decisionism” in the age of cybernetics – what he now calls “Data-ism.” (nyti.ms/1MXHcMW) In both cases the fallacy is that “decisions based on data and analysis” and run by “algorithms” yield better decisions. Recently when I was debating this in a coffee shop with other guys, and when the only woman customer rightfully left in disgust, I argued that regardless of the computing power, people had to decide what decisions to decide, decide how to write the algorithms to be calculated, and decide if the results are valid. RID alone cannot do that. As they re-evolve, zombies are more likely to be able to do so in the future; after all, even eating human brains should give them a leg up on robots. Bad and good RIDdance should teach us how we desire more powerlessness and are ready to abdicate human responsibility. I prefer to stick with the ponderous human beings. Because I read about and strategize about RID for agriculture most of every day, if I had to make a choice, I come down on the side of Liv Moore.