Not long ago a good friend of mine looked at my website and said, “Ron, you need a byline.” That is to say, a catchy phrase that describes what I do. Well, I’m hardly an expert on catchy phrases. Most folks find my writing painfully convoluted – bordering on arcane (probably on the other side of the border). As I thought about that, I tried to think of a simple phrase that described what I did. I think it boils down to:
Turning Data into Knowledge
Naturally I feel compelled to explain this a bit. I spent a career in the laboratory business. I’ve done a lot of environmental analysis, but I’ve also done work in oil and gas, industrial development and even some forensic work. It has seemed to me for a long time that we tend to rummage around with lots of random data points that frequently don’t make any sense. That hasn’t kept folks from trying to make sense of their data, but often they see what they want to see. It’s kind of like Dr. John Nash in the movie, “A Beautiful Mind”:
In the clip we see Dr. Nash (Russell Crowe) illustrating the mind’s ability to see order in chaos. In fact, it is not at all unusual for our minds to infer order from chaos or causality from random events. That is how our minds work and, though we are sometimes deceived, quite frequently, it is this ability that actually brings us to knowledge.
It is not enough to catalog random data. If we are to have any useful knowledge, we must get at underlying causes in order to obtain any reasonable ability to predict future events. This is the fundamental basis of useful knowledge. If, for example, we could not depend on gasoline of certain specifications to work in predictable ways to power an automobile, knowing the composition and properties of the gasoline would be completely useless. If causality were completely unhinged, all that could be known is that which is defined. We might, for example, be able to define gasoline by its specifications, but that would be a completely useless bit of trivia of no greater consequence than defining a triangle as a polygon with three sides.
Philosophers have dealt with this ability to know in a very general way. This study is known as epistemology – the theory of knowledge. There are many subtleties from a philosophical perspective, but there are some issues of real practical importance that arise from these rather esoteric discussions. The writings of Emmanuel Kant are especially interesting in that we learn that random data points are not real knowledge unless the relationships between these data points can also be understood. That is to say, we understand how order can be seen in what first appears to be random. And here, of course, is where things can get fuzzy. Our mind tends to categorize and relate data points, naturally looking for recurring patterns and assigning order and causality. In this we are both blessed and cursed. We intuit order and causality, but is it really there?
We may observe that A and B always seem to appear together. Our experience may be that A tends to appear before B and our minds begin to associate causality of B to A. This may be true, but there are a lot of things that could be wrong. Our perceptions could be wrong. A may be perceived by us before B, but B might have actually existed before A without us knowing it. In that case B might actually be causing A. And, of course, both A and B may be caused by something totally beyond our perception and neither actually causes the other.
This whole process of knowing gets further complicated by inexact measurements, interactions between factors and the cost of knowing with greater confidence. Inexact measurements can not only obscure relationships between factors, it can bias results such that apparent relationships are completely without objective reality. Experiments must begin with proper analytical methods so that artifacts of measurements are eliminated from consideration. Experiments must be designed so that important factors are included and we don’t waste time and money on irrelevant minutia. Even the potential for two or more factors to work in concert to affect results must be considered. And all the while the cost of knowing more must be balanced against the cost of knowing too little.
This is what I do. I do look at the analytical challenges and devise better, more relevant and cost effective ways to get the right data. I am especially keen on using GC/MS for organic analysis since it provides two independent methods for identifying compounds and avoiding much analytical confounding. I am also very keen on using standard reference methods that have been proven time and time again. Too often we leap to a new, slick, high tech method only to find that there was some interference or subtlety that we overlooked.
I also look at the possible relationships between factors and devise trials to test potential interactions in objective ways. In this I use my years of experience in engineering settings to think through possibilities. And I do look at the most cost effective ways to get the knowledge needed with the confidence required without chasing too many irrelevant issues. I model these ideas in statistically based Design of Experiment software searching for optimal experimental designs. This is both art and science. It is an application of good Analytical Chemistry and shrewd Accounting and Finance to take statistically valid data and transform it into useful business knowledge.
The process always begins with the end in mind. That end must be defined in quantitative terms that include both precision and accuracy terms, but also a sense of the monetary value of the knowledge. It does no good to come up with a number and have no idea of its reliability. Furthermore, it is unwise to begin any task without some idea what the cost will be. Armed with basic ideas of the purposes of an investigation, the methods and experiments designed are much more likely to meet objectives. Historical data can be used to intuit causal relationships and then these can be systematically tested to verify the reasonableness of our thinking. It is only after planned experimentation that we have any hope of attaining something close to knowledge. This is how data is systematically turned into knowledge.
One final comment should be made. Outside of logical definitions (see “Analytic-Synthetic Distinction”), there is no such thing as knowing anything with perfect certainty. In the simplest of circumstances we may come close to certainty – at least for all practical purposes. Nevertheless, with complexity comes greater uncertainty. We may, for example, be quite convinced that our experimental yield for a particular reaction will be between 85% and 90% at the lab bench. Nevertheless, how much money we will make in plant running that process will be far from certain. The key is understanding all the many factors and being able to make reasonable estimates with enough reliability to make shrewd decisions.
This is what I mean when I say, “Turning Data into Knowledge.” It is more than just careful analysis. It includes a careful evaluation of the relationships between data from measurements and desired outcomes.
Ron Stites holds a BS in Chemistry and an MBA in Finance and Accounting. Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking. For more information see: www.tek-dev.net.
Comments