We live in a completely dynamic world. One that is constantly moving and changing. So why would static data alone be a good enough source to base all critical business decisions on?
The Greeks didn’t only give us the Olympic Games and delicious desserts like Baklava, they also gave us wisdom in the form of some of the world’s greatest philosophers, one of whom was Heraclitus. He is credited with saying: “Change is the only constant in life.”
Today, that statement has arguably never been truer. As business executives, there is a massive array of decisions that need to be made each and every day.
These decisions range from the small to the truly massive in terms of not only their own magnitude, but also the magnitude of what impact they have on business growth and its future.
Creating a movement
No matter which industry you find yourself in, movement is becoming more and more important in every decision made.
In retail, it might be about understanding the movement of clients in terms of where they are coming from to get to the store, or where they go to after they have left it. It could be understanding the movement of potential clients to work out where to open the next branch to maximise footfall, or even whether the store is primed for a home delivery service.
In insurance it could be how and where insured vehicles are being used, or where they are being stored overnight. Understanding areas of high risk and how insured assets move though those predetermined areas is of immense value.
In research environments, it is one thing understanding the perception a population has of a brand, but the granularity of what that looks like from a geographic standpoint is something entirely different.
The nuance is in understanding what data can be used as static and what data needs to be dynamic.
Understanding where the pockets of positivity and negativity lie creates a map from which to plan communication campaigns with pinpoint accuracy.
Dynamic versus static data is perhaps better described as the difference between a vector and a scalar. A scalar data point is something that is defined by a magnitude or numerical value alone. A vector quantity is defined by both magnitude and direction.
Dynamic data gives a vector – in other words, it gives magnitude and direction. It gives movement. Static data is a scalar which is only giving magnitude.
No matter the industry or the use case, the movement of people and things is becoming more and more vital to enabling sound business decision-making.
There is a certain time sensitivity on data-driven decisions. This is really the crux of what differentiates static and dynamic data. Static or scalar data is a point in time with no native ability to infer a trend unless multiple static points are stacked up against each other. Dynamic data automatically lends itself towards a trend (magnitude and direction).
The nuance is in understanding what data can be used as static and what data needs to be dynamic. There are times when static data is okay. These are data layers where there is not a rapid change to their profile. Here we can look at town and city names, property price data, demographic data and the like.
These are static and at best, slow-moving data layers. They don’t need to be viewed in a live environment as they don’t change often. It is safe for these layers to be static and only refreshed at a far slower rate.
Other data layers like POS transactions, footfall traffic to a store and road traffic data are all very dynamic as they have high velocity, high variety and high volume.
Because of these three characteristics, these kinds of data demand a high refresh rate in order to make relevant and accurate decisions.
Data versus analysis
While the data is the first critical element and understanding its dynamic versus static nature is fundamental to the process, the analysis itself becomes the next crucial component.
While static data analysis is certainly still required and needed in most organisations, for a whole host of reasons, dynamic analysis is not something that everyone can fully grasp and deliver.
Let’s look at a simple data example in which a company’s sales numbers went up 5% when compared to the figures from the previous month.
Now let’s take that same example and convert it to a more dynamic analysis. Now we can say the sales went up 5% compared to the previous month, because the product array was expanded with local production.
Dynamic analysis is not only labelling the data, but connecting the dots, evaluating the factors responsible for the change, and wrapping everything in a layer of logic.
It is off of dynamic data analysis that machine learning and artificial intelligence are derived. These intelligent algorithms thrive in dynamic environments where trends are picked up and predicted. This is near impossible to do with static data, especially when not refreshed rapidly.
Flexibility and awareness of the situation, as well as the data, are the keys to good data analysis, which in today’s world has ever-increasing value and popularity.
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