Virtually every company has some sort of training program with the goal of having their people work smarter, but it is not the program that counts but how effective it is. What factors determine this in today’s economic climate? The nature of information flow, a worker’s learning style preference, and the nature of creativity are three key factors in the equation.
While working faster, harder, and longer has its limits (limit theory and chaos theory guarantee that mathematically), there is no end to working smarter. This is no doubt why gargantuan efforts and resources have been and are being spent to train workers to work smarter. In fact, working smarter may just be the only option left for many companies struggling with limited, or worse yet, dwindling resources. Fortunately, the Information Age lends itself well to this modus operandi and much more so than the Industrial Age did. This is due to the fact that there are no physical limits on information itself like there are with physical products (e.g. a widget factory can only produce so many widgets). And this distinction becomes more acute when time is involved. In the past, if you could get a report delivered a minute earlier, it most likely wouldn’t make much of a difference, but fast forward to today and a split second can be too late (e.g. in quantitative trading on Wall Street).
But the problem is that the gargantuan efforts made to train workers to work smarter often end up as shelfware due to not being fully applied or even tried. Some companies take the approach that if they could just find the right training program, their workforce will be miraculously changed into a much smarter functioning unit. But are they looking in the right place? Could it be rather more a matter of workers being able to train themselves and being motivated to do so? It becomes an interesting scenario when we start modeling such an approach in the context of information flow.
For one, we have to look at how people learn. Not everyone learns the same way, and how much is learned is very much dependent on motivation which in turn depends on the degree of interest the learner has. We also have to look at the nature of creativity which is closely associated with learning. We need to recognize that to schedule a brainstorming session at a certain hour of the day is somewhat of an oxymoron because the brain doesn’t work that way (for creativity). And above all, we have to consider the very nature of information and how it flows through a company and beyond. All these factors have to be taken into consideration if we want a viable self-training model for workers to use.
Starting with the crucial factor of information flow, we can borrow from physics the concept of complex waves in acoustics. The flow of music (which itself is a form of information) comes from a complex combination of otherwise simple sound waves that together form a harmonious pattern (or not). If an instrument is out of tune, or a player strikes the wrong chord, the harmony will be off. The parallel to this can actually be found in the kind of information flow that you see in a corporate setting. First off, the totality of information is never simple, and the larger the corporation, the more complex the “waves” of information. A single note off-key (read, a company issue) can throw off the harmony. And the more notes that are off-key, the greater the effect (read, corporate bureaucracy). So we perceive from this that information flow is amazingly generic.
This is further shown in the striking parallel that exists between the legal and I.T. professions when it comes to information flow. For instance, we can match up the roles (in principle) played in an I.T. QA (Quality Assurance) cycle and a legal court case. The test engineer that finds a software bug is like the detective that uncovers the evidence. The test engineer (often the same person) that meets with the necessary parties to discuss the bug’s gravity, validity, etc. is like the prosecuting attorney presenting their case in court. The developer for the software having the bug is like the defense attorney, and by extension, the software application itself is like the defendant. The final decision-maker on the go-no go shipping of the software application is often the product’s business owner which parallels the judge in court who renders the final judgment call after the case is heard.
The parallel here is so close because the logic pathways of the information flow in both scenarios are basically the same. This has huge implications because mapping that information flow (for worker training purposes) may allow for a highly generic and therefore reusable model. Let’s now delve further into these two scenarios to look for more patterns of parallelism. There are two kinds of activity in the flow, namely, procedural and creative. We have QA procedures and court procedures, and we have the creative thinking from the test engineer and developer as well as the court attorneys. The processes of discovering, identifying, explaining, interpreting, documenting, confirming, etc. are also there in both scenarios. The training for both procedural and creative activity in those named processes will be impacted by the same factors, namely, the professional’s learning style preference and the degree of creativity involved.
This is because even for the procedural aspects, creativity will allow for connecting the dots in a way that would otherwise not be thought of, and how a person learns can influence how well they work with such procedures (e.g. memory recall). With human resources coaching (HRC) to build on a worker’s specific genetics and conditioning (http://www.managementexchange.com/hack/human-resources-coaching-hrc-vs-human-resources-management-hrm), this can address the worker’s learning style preference directly. And the role of “left” and “right” brain usage for procedural and creative processing respectively is tied in right down to the biochemical level.
Now let’s consider further the factor of learning. Suppose the worker is allowed to choose what, when, and where they want to learn as well as what method they want to use for learning. The tie-in, of course, needs to be applicable to their work, but with such freedom to choose, wouldn’t their motivation/interest be greater than with a one-size-fits-all training program? And by extension, wouldn’t the training undertaken be less prone to end up as shelfware?
And further on the factor of creativity, we need to realize that working smarter comes from thinking smarter. The need for what is a well-worn adage of thinking outside the box has given way to the need for thinking outside the building (and not just literally). While it can be argued that incremental innovation is indeed creativity, game-changing innovation is creativity on steroids and which for that matter is becoming more and more necessary for companies to have if they want to excel in the Information Age. And since the source of true creativity cannot come from machines, the use of human resources is a company’s only avenue here. Now the connection to worker training is this: Creativity comes from creative thinking -> creative thinking involves meaningful change -> meaningful change comes from retraining former mental pathways in new ways -> such internal retraining comes from within and can be enhanced by external retraining. So the effect of external retraining (read, worker training) is dependent upon how well it can enhance that internal retraining mechanism. In this way then, the workers are training themselves even if using externally sourced training programs.
So if we take these three factors (the nature of the information flow (F), the worker’s personal learning style (L), and the process of creativity (C)) and tie them together algebraically, would we have some semblance of a working model for effective training in the Information Age? Let us see. First, we address the question of whether our model would be something like F + L + C or F x L x C or FL^C or F^(L^C). In other words, how strong is the synergy of these factors (e.g. additive, multiplicative, or even exponential)? While we cannot exactly quantify what is obviously not just a nonlinear situation but one that subjectively involves humans, we can get an idea of the degree of such synergy by rephrasing the question using another real-world scenario that is worker-specific.
For instance, let’s say that the information flow (F) is very complex in a situational example involving massive amounts of information that flow to a large corporate customer. Now the specific portion of that information that flows from that customer’s account manager (our worker in question) happens to be very complex and now needs to be done in a much more meaningful way. Let’s now say that our worker has a personal learning style (L) of learning best when self-taught, following their own trail as it were. And for our third factor, the process of creativity (C), let’s say that it has to be truly game-changing because the information in question has to be meaningful enough to merit a large increase in the revenue stream coming from that corporate client due to their getting such a value-added product (of information).
Let’s now put some numbers to our algebraic expressions. Let F = 5 or 10 (on a scale of 1 to 10 in information complexity), L = 5 or 10 (on a scale of 1 to 10 in closeness of match to the preferred learning style), and C = 5 or 10 (on a scale of 1 to 10 in degree of creativity needed). For the lesser case (F = 5; L = 5; C = 5), we have the subsequent algebraic solutions (for F + L + C or F x L x C or FL^C or F^(L^C)) as 15, 125, 9,765,625, and 1.9 x 10^2184 respectively. For the greater case (F = 10; L = 10; C = 10), we have the solutions 30, 1000, 1 x 10^20, and 10^10000000000 respectively. Obviously, both solutions for the fourth algebraic expression (F^(L^C)) indicate that the expression is not at all realistic, and when comparing the two solutions for the third algebraic expression (FL^C), the gap is too great to be realistic. The solutions of the first algebraic expression (15 vs. 30 for F + L + C) appear more meaningful, in essence, saying that you double the synergy when you double the factors. But remember that our scenario is nonlinear and such simple doubling does not explain the high ROI (return on investment) that can come from the right kind of creativity, when Big Data is involved, and/or when worker training is highly successful. Therefore, the second algebraic expression appears closer to reality whereby the synergy is an eightfold difference between the cases (125 vs. 1000 for F x L x C).
So here we have a fairly simple model (F x L x C or FLC) for approximating this synergy when weighing these three factors of worker training. The goal is to help workers work smarter since that is a win-win situation for all the parties involved (the worker, their manager and fellow workers, the company, and the customer). So having some sort of metric to work with, albeit for subjective and nonlinear scenarios, may help guide companies to determine what best direction to take for their training needs. For instance, if trying to decide between three available training options (e.g. among external training programs) and the FLC formulas produce the values 2, 5, and 9 respectively for those options, this is a strong indication that the one valued at 9 is the best choice of the three. At that point, you’ve at least quantified to a certain extent what would be considered “soft values” which by their nature are fuzzy, nebulous, and difficult to measure. This quantification then is relative; in other words, the 9 in our example does not mean the quantity 9 of some stand-alone ROI unit of value but rather the quantity of 9 in relation to the 2 and 5 (in other words, significantly greater than the other two options). So the model is a comparative tool to help show potential for ROI. The true level of ROI, of course, will become manifest after the fact.
The algebraic model may help quantify things for decision-making purposes, especially when crucial judgment calls are needed. And since the training is more tailored to the desired results and would not just be some moot academic exercise, the effects will more likely be on the mark. Finally, because this training approach models the information flow in question, it would not be bound to the physical components that happen to be in that flow at the moment. Thus when things change as they always will, the methodology used would still be reusable.
At first glance it may appear that this approach involves designing a customized training program for every single worker in the company. However, keep in mind that this approach has the worker choosing their training; it is not chosen for them. The company simply provides the means for them to do so.
Modeling information flow may be a novel concept for many, and since it involves abstract objects it could even appear daunting. However, we do it every day with natural language, representing abstract concepts with words - it's the same principle.
There may be the tendency to want to drill down in great detail on every point involved in the training strategies. While details are appropriate at times, one should try to keep things as generic as possible based on principles, thus allowing for flexibility. Focus on the what rather than the how; remember that the workers themselves will be working out the how part.
Pick a few eager participants for trying out the process. Begin by asking what they think or feel is their preferred learning style based on their experience with former training endeavors. Here's where a lot of details are welcomed because such information can provide fodder for determining patterns (of learning behavior). Also, honesty is important in the feedback because you don't want to repeat past failures. And really, by their very answers, the participants would be choosing their own learning style, something that you will be able to observe firsthand. Try a few runs of actual training and then observe the results. Tweak it from there, then start slowly expanding the program. Also try out the algebraic model with different scenarios and see how the numbers turn out - the results may surprise you.