Next we look at data, information, and knowledge, and how they are used to arrive at conclusions and make decisions. There have been a number of versions of this discussion used by the authors and others for a variety of purposes. We will confine ourselves here to four distinct issues, consisting of
1. The cognitive or information hierarchy
䡲 The information domain and the cognitive domain
2. The decision or, as used by the military, the command hierarchy 3. The Observe, Orient, Decide, Act (OODA) Loop
4. The decision timeline (or battle timeline for military applications)
Cognitive Hierarchy
The cognitive hierarchy deals with the steps that humans take, i.e., the processes that we go through to collect, refine, categorize, and understand facts about our environment. It is an attempt to explain how we go from a collection of small, seemingly insignificant facts to some form of understanding, awareness, decisiveness, and wisdom. Typically, there are three distinct sec- tions to the cognitive hierarchy: the information domain, the cognitive domain, and reality.
Information Domain
The beginnings of the cognitive hierarchy are based in the information domain which usually starts with a large variety of data points or facts that we have managed to accumulate via whatever means are at our disposal. However, data by itself is unintelligible until it is joined to a person’s image of reality or frame of the decision situation (frame of discernment). It is at this point that data becomes information and knowledge [Sage, 1990]. What, then, is knowledge?
Unfortunately, these terms as used here, especially “data” and “information,”
are applied differently than one might expect from common, everyday con- versation. Webster defines data as “information, esp. information organized
32 Building A Global Information Assurance Program
for analysis,” and information as “facts learned.” With respect to the information domain, these definitions are essentially reversed. For purposes of this dis- cussion, the terms data, information, and knowledge hold specific meaning and shall be defined as follows:
䡲 Data: An unordered, unorganized collection of facts (Exhibit 9) 䡲 Information: Data arranged in some logical, orderly, organized fashion 䡲 Knowledge: Insights created by the application of the rules of logic and
“real-world” facts to the available information.
In its raw form, data is simply a random collection of facts (Exhibit 9). In this natural, essentially chaotic state, it is of marginal, if any, use. In order to be meaningful, we must somehow arrange the data in a fashion that provides practical information.
As we can see, data means very little without some method of organizing, viewing, and interpreting it. However, it is not always obvious whether these facts are in any way related and, if so, how. The first task is to establish some logical connection between and among the data that we have collected, and then organize that data into a meaningful format. The result is meaningful, useful, coherent information (Exhibit 10).
Comparing Exhibits 9 and 10, we can see that somehow we have come to the conclusion that the data we have collected pertains to students, courses that Exhibit 9 Data
they have taken or are taking, and their grades in those courses. But the big question is, how did we get here? What did we have to do or assume in order to make this connection? How do we know that it is correct? We obtained information from the collected data by organizing it into some recognizable form. How we came to this recognition, however, is debatable. Most likely, our real-world experience and environment lead us in a particular direction. Perhaps the person responsible for organizing this particular data set inhabits a university or some other educational environment; that being the case, it takes little effort to realize that the data of Exhibit 9 fits the information mold of Exhibit 10.
Unfortunately, the connection is not always as easy to make. Nonetheless, more often than not the collector of the data is working within a specific domain, knows what he is collecting, and therefore knows how to fit the pieces together.
So we now have useful information, but not much. Knowledge must then be extracted from the available information. We know that Mary, for example, has a 67 in Eng 211, but what does that mean? What is Eng 211, and is a 67 good or bad? Without more information to put this into perspective, we really do not have any knowledge of what that means from any particular point of view such as Mary or the school. But when we combine the information that we have collected, with additional information from our environment, we can come to the conclusion that Mary is not doing well in this class (Exhibit 11).
Combined with other information concerning the school’s grading policies, her own study habits, and a host of other variables, one might also conclude that Mary is not working hard enough and is in danger of failing. However, in order to come to these conclusions, we had to apply logic and additional Exhibit 10 Information
34 Building A Global Information Assurance Program
information about how the real world operates, and assume information not currently available from the facts given. How, then, do we know when the combined information from several sources is sufficient?
Consider the following example. A number of years ago, a freshman at the University of Illinois brought his grades home during the winter break.
His parents were ecstatic; their son had achieved a grade point average of 3.85 during his first semester in a very demanding engineering curriculum.
Because he had always been an honor roll student with a solid A/B average in high school, averaging between 3.75–3.9, it was obvious that he was doing equally well in his new educational environment. Unfortunately, the parents were missing a vital piece of additional information. The University of Illinois was using a five-point grading scale rather than the typical 4-point scale used by most other colleges, universities, and high schools. The student had a solid C average, but not the high B average that his parents assumed. Although we usually can and often do make these assumptions, things are not always what they seem. Consider another example: today, in the Fairfax County, Virginia, public school system, students must rank in the 94th percentile or above to achieve a grade of A, as opposed to the typical grading scale where anything greater than or equal to 90 is an A. In Fairfax County, 93 is a B.
So, we have navigated the first part of the cognitive hierarchy, namely, the information domain (Exhibit 12), and have managed to collect, analyze, categorize, organize, and interpret raw facts to arrive at some level of knowl- edge with respect to the domain of discourse. Note that most of what we have accomplished so far has been rather mechanical. We have collected facts from one or more sources (Exhibit 9); we have organized the facts into something recognizable (Exhibit 10); and, we have combined the resulting Exhibit 11 Knowledge
information from multiple sources to arrive at some level of knowledge (Exhibit 11). Webster defines the term knowledge as “understanding acquired through experience.” Here again, our usage diverges; we use the term to indicate a realization of the information.
At this point, we have exhausted all of the “mechanical” steps that we can take to refine the facts that we have available. Through the cognitive hierarchy, we use experience to build understanding from knowledge. Now we arrive at the daunting realization that we must take the next step. Now we must actually start to become “SMART”: more specific, measurable, accurate, reli- able, and timely.
Cognitive Domain
The cognitive domain builds on the data, information, and knowledge we have previously gathered. If we think about the knowledge we have acquired, we can begin to interpret what we see and achieve some level of understand- ing. The depth, breadth, and quality of our understanding generally depends on the quantity and quality of the underlying knowledge on which it is based, and our cognitive abilities with respect to that knowledge. In Exhibit 13, we have begun to interpret what we know or think we know using deductive, inductive, and abductive reasoning techniques, which we touch on briefly at the end of this discussion.
Exhibit 12 The Information Domain
36 Building A Global Information Assurance Program
This is where we begin to make assumptions, draw conclusions and generally try to extend our understanding of the situation or environment based purely on thought processes applied to the underlying knowledge. By working through these thought processes and analyzing what we find, we can build a better awareness of our surroundings (Exhibit 14).
Exhibit 13 Understanding
Exhibit 14 Awareness
Being aware of the situation, however, does not necessarily mean that we are willing or able to act on our beliefs; that decisiveness comes from a level of confidence in all that has gone before. How good is our data, information, knowledge as measured by some set of attributes as discussed earlier? How confident are we in our assessment of that knowledge, and the understanding and awareness that results? Decisiveness (Exhibit 15) is also heavily dependent on the individual characteristics of the decision maker, but although it may be vastly different from individual to individual, some comfort level must be achieved before we can claim a decisiveness to our state of mind and a willingness to take action.
From here, the next step is far from clear. Indeed, many authors would debate some aspects of our discussion thus far. However, at some point we reach a lofty, somewhat magical state of wisdom (Exhibit 16). What that is and how we get there has been a mystery since the beginning of mankind.
Here, the common dictionary definition of wisdom is as accurate as any:
Exhibit 15 Decisiveness
Exhibit 16 Wisdom
38 Building A Global Information Assurance Program
insightful understanding of what is true, right, or enduring; native good judgment.
We should also mention that within the cognitive domain, our usage of terms follows common definitions much more closely than in the information domain discussion. For clarity and completeness, we present those dictionary definitions here:
Understanding: The ability to comprehend, perception; the capability to think, learn, judge; individual judgment or interpretation; accord of thought or feeling
Awareness: Being mindful or conscious of
Decisiveness: Marked by firm determination; resolute; unquestionable But despite reaching this illusional state and rounding out the cognitive hierarchy (Exhibit 17), we are not done yet. So far, our discussion has been confined to the facts that we can discern about our domain or environment, our interpretation of those facts, and the conclusion we can draw from them.
But there is still at least one piece missing. Why have we gone to all of this trouble collecting and mechanically orchestrating facts, then exercising our brains trying to decide what it all means? The simple answer is that we do all of this in an effort to better understand reality. Out there somewhere is the real world. Clearly, what we are trying to do is to build an understandable, unambiguous picture that accurately represents that reality (Exhibit 18).
Where reality fits with the rest of the hierarchy is debatable. The authors prefer to show reality somewhere between awareness and wisdom, as in Exhibit 19.
The question still remains, How do we use what we have learned? To answer this question, the U.S. military has adopted what it refers to as the command hierarchy or, more generically, the decision hierarchy.
Exhibit 17 The Cognitive Domain
Exhibit 18 Reality
The Command/Decision Hierarchy
The decision hierarchy is an attempt to map human decision processes to the information and cognition processes just described, and fits closely with the OODA Loop battlefield concept developed by Colonel John Boyd, USAF, in the mid-1980s. Referring to Exhibit 20, in any situation, military or civilian, we generally start by trying to collect as much information about our situation Exhibit 19 The Cognitive/Information Hierarchy
Exhibit 20 The Command/Decision Hierarchy
1 Data
2 Information
3 Knowledge
4 Understanding
5 Awareness
6 Decisiveness
7 Reality
Experience Interpretation
Organization
Confidence
Analysis
Environment Intuition
Information Domain Cognitive
Domain Reality
1
Observe/Collect/Store (Data) 2
Report (Information) 3 Assess (Knowledge)
4
Decision (Understanding) 5
Act (Awareness) 6 Assess (Wisdom)
Experience Interpretation
Organization
Analysis
Fusion Intuition
Information Domain Cognitive
Domain Reality
Cognitive Domain
40 Building A Global Information Assurance Program
as possible. We observe our environment and collect facts. We then collate the resultant information into some form of report. This may be an entirely internal, informal, and perhaps even subconscious process or, in any large organization, it is more likely to be formalized and written. Reports thus generated are generally read and assessed to formulate a decision on which some action is then taken. Typically, the results of this action are then monitored to determine impact or change in the facts of the environment or situation and the process starts all over again. This is the process that Boyd dubbed the OODA Loop (Exhibit 21).
Machines don’t fight wars. Terrain doesn’t fight wars. Humans fight wars. You must get into the mind of humans. That’s where the battles are won.
Colonel John Boyd Boyd’s theory was that a fighter aircraft with better maneuverability and superior speed characteristics should generally win the majority of “dog fight”
engagements. However, this was not happening in actual air-to-air engage- ments during the Korean War. U.S. fighter pilots, despite flying aircraft with wider turn radii, were consistently beating adversary pilots and their aircraft.
Based on an in-depth study, Boyd came to the conclusion that it was not necessarily the characteristics of the aircraft that was the deciding factor in winning a “dog fight,” at least not the only factor. It was the ability of the pilot to acquire the adversary first, and the speed with which the pilot’s decision-making inputs reached the aircraft’s control surfaces. Boyd’s hypoth- esis was that a U.S. fighter pilot would win the “dog fight” because he could complete “loops” of decision-making quicker than his adversary. Boyd sur- mised that quicker was better than faster. Boyd’s loop occurred in four distinct steps [Boyd, 1986]:
Exhibit 21 The OODA Loop
Otrien Observe
Act
Decide
䡲 Orient: Because U.S. pilots acquired the adversary first, they could then react by orienting themselves toward the adversary faster.
䡲 Decide: After reacting with their initial orientation, the U.S. pilot’s level of training then allowed him as a decision maker to act quicker, proceeding to the next combat maneuver.
䡲 Act: With the next combat maneuver decided on, U.S. pilots could then rapidly “input” aircraft control instructions, with the resultant quicker initiation of a desired maneuver.
A more in-depth look at the OODA Loop concept can be found in Chapter 9.