When you look around in today’s world, you can see technology evolving by the day. Within the last decades, calculation speeds have grown far beyond what a human mind can even comprehend. Storage devices dwarf the world’s biggest libraries and manufacturing robots assemble parts or whole appliances faster than the eye can follow. We have built tools to help us to do things we are not efficient at, allowing us to focus on the traits we really shine at. For some, this can be arts and design, while others hone their empathy and social skills. If you work in technology, often times you become a problem solver for the things that cannot be calculated (yet). These are ideas on how to architect software to achieve maximum flexibility, how to structure a company to boost creativity and team spirit, or what to do to optimize the availability of your cloud-based services. Most of the time we do not ask ourselves – “Why are we so much better at resolving these types of problems than a computer?” – we just do it. So why is “seeing the big picture” such an incredibly useful skill to have? And why is “Tomato, tomato” a good example for this?
The answers are not simple. However, when you do the research, one of the strongest traits that makes us stand out from our computerized helpers is our ability for pattern recognition. Humans excel at it. Recognizing facial expressions and social interactions, understanding, reading and speaking languages and piloting a vehicle in a city are all skills that use our capacity for pattern recognition in unique and different ways. Once you start looking at the current state of science you will also find that, even though there is a lot of research in these areas, computer-controlled driving, voice recognition and similar endeavors are still significantly behind human abilities. Yet, there is one important caveat: our proficiency is limited to the types of patterns we have learned since birth. For example, if you have never identified tree leaves in your childhood, you will have a hard time doing it as an adult without consulting a field guide. This is a much slower process than the instant recognition resulting from learned pattern recognition.
Consequently, the next step in solving problems is to leverage today’s abundance of computing power to transform data into the types of patterns that we are so good at isolating. We can process the data that we have into a perspective that contains shapes and arrangements we have an easier time recognizing. Consider the following three approaches of describing the revision of a metal plate in a manufacturing process:
Metal Plate Comparison (Fig. A)
“The dimensions of the metal plate were changed from a size of 2” x 1.25” to 2.5” x 1” while also adding a hole of a quarter inch diameter in the bottom right corner.”
Metal Plate (Fig. B)
Metal Plate Definitions (Fig. C)
Location: Bottom Right
We certainly understand what has happened to the metal plate in the verbal description (Fig. A), but it takes us only a fraction of that time to understand what was changed when looking at the pictures (Fig. B). In addition to seeing the quality of change, we are also able to quantify it and analyze if that change is appropriate. The tabular form (Fig. C) might be the most abstract, but most closely resembles how you would store this data to make it available for a machine.
In other cases, you might not even see the difference until you change your perspective, or you might perceive a difference where there actually is none. Different pronunciations can lead you to believe that a tomato is not the same as a tomato. Once you look at the written word however, you can easily determine that they are one and the same.
In the world of process, business and performance management, recognizing subtle and obvious developments early on can give you an edge over a competitor or prevent a decline in productivity. So, naturally, we are always on the lookout for changes in the status quo. In addition, if we have isolated an area for improvement, we are looking for ways to implement it by adjusting the current state and then comparing it with the past. If you have your processes or even your whole business modeled, you have a very good foundation to start from for both of these scenarios. Taking what we have learned about the usefulness of transforming data into easily recognizable patterns, we can now start building tools that allow us to improve our existing model.
These transformations can take a multitude of forms, and we can take advantage of them to determine areas which need further investigation:
Modeling your processes and business can give you access to all this information in a variety of different representations without actually having to change the underlying data. You are now able to search for patterns and make predictions, plans and decisions based on those insights. The ability to view your information from a variety of different angles will give you perspectives into your business you might not have considered before. This is where you start thinking out of the box. And once you are out of the box, you can finally start to see the big picture.