“The more we look, the more to see: altering this reality” — Zumwalt
In order to interpret, evaluate and ultimately understand, one has to observe.
However, there is so much out there to observe. It’s overwhelming. So we run into an apparent paradox: one must know, to some degree, what to observe, yet one cannot know without first observing, interpreting, recognizing, filtering, connecting, comparing, evaluating and have some level of realization.
And so one cannot ever start from scratch. There will always be some initial observation from earlier or from others that one relies on in order to determine what next to observe.
Observing then becomes a process of narrowing down and expanding what one observes. To be efficient this is not a random process, but one driven by the formulation of an objective.
For example, “I need to eat” is an objective. With that objective one then observes — or collects data — in areas that will be more likely to provide the necessary input to increase one’s success at an action designed to obtain food for eating.
If we are a retail chain, like Kohl’s, Macy’s or Sears, we must determine our objective. Is it to increase revenue, lower costs, or increase profit margin.? Or is it a less basic objective like increase in-store traffic or increase brand awareness which may be considered an objective in order to achieve the more specific objective of increased revenue through building customer loyalty?
Having a hypothesis can help narrow down what to observe, but a hypothesis should not be formulated too early in the process or opportunities to achieve an objective can be missed. Instead it is best to look for patterns in the data, and then determine what is a likely hypothesis based on that data as opposed to starting with an hypothesis and thus missing the opportunity of identifying more likely hypotheses.
For example, if my objective is to cross a river and I start with the hypothesis that one can cross the river on a structure that floats on water, I may miss out on noticing that 200 yards downstream someone has built a bridge, or the river becomes shallow enough to cross on foot, or that the river becomes an underground stream.
So important to keep the objective in mind, identify what data can be collected (what can be observed), and not prematurely limit the data that one will analyze.
With companies like JC Penney and Macy’s currently fighting against declining same- store sales, it seems like they missed out on observing several years ago how Amazon was increasing book sales and ultimately positioning itself to sell other items online — items that were also sold in shopping mall department stores. What were they observing? What was their hypothesis?
Five year stock chart for JC Penney
Five year stock chart for Macy’s
At this point, have these companies learned the hard way what data they need to look at? (If so, now may be a good time to buy their stocks. If not, expect further losses for JCPenney and further same store sale declines for Macy’s.)
There is a lot out there to observe. The first step is to clearly understand one’s objective and then focus on observing those things that will help achieve that objective. For music, if one wants to dance, observe (listen to) the beat — this means focus on the drums and the bass, If one wants wants to play along, focus on what the chord progressions are for the verse and chorus. If one wants to ignore the music, focus on something besides the music.
Don’t worry about formulating an hypothesis until we has examined enough relevant data for that intended objective to see what are recurring patterns. Fortunately in the data analysis world there is software (Artificial Intelligence, Machine Learning) that can identify patterns in the data. Without software, one can take notes, reflect and follow the steps in my diagram below: observe a significant amount of data, recognize what are the patterns that appear relevant to your objective, filter out the irrelevant data to focus on that data that has the patterns of interest, evaluate that data, understand its nature and then form a plan of action to gather or observe more of that data as necessary.
Eventually one can form a hypothesis and scrutinize that hypothesis each time one goes through the “comparison”, “evaluate” and “realize” steps to know if that hypothesis is correct or not. If that hypothesis is not appropriate, then one needs to formulate a better hypothesis that is aligned with previously collected data and will predict future observations. There is no such thing, though, as a correct hypothesis — there is only a workable hypothesis — one that provides the necessary guidance to act successfully. Where people get in trouble is when they stubbornly or subconsciously hold on to a premise or hypothesis that is not successfully predicting outcomes and then continue to act on that premise or hypothesis as if it was reliable.
No belief is important enough to hold on to when it doesn’t align with verified observations. Such beliefs end up doing everyone more harm than good.