Who Needs Data & What Is It For?
The skills required to function in the workplace are changing. If we look at the prevalence of technology in the workplace even 50 years ago, we would have found that while there were some software tools that people used on a daily basis, it was not necessary to be digitally literate in order do your job.
The scenario today is very different. Digital technologies permeate the workplace nestled in every nook and waiting around every corner. Across various job roles, you would find that digital skills play an important role. From marketing to supply chain management, digital tools allow for more productive outcomes and faster turnaround time for tasks.
A direct consequence of the digital revolution has been the ability to collect data about processes- big and small and, most importantly, being able to store them for future access. Data that is is actionable and in a format that a decision maker can easily understand are also extremely important.
Data can be in many formats: text, numbers, or a set of images. A list of customers for a product is data, the value of stock markets over the course of a year is data, or even traffic maps of your favorite cities are data.
What Do We Do With All This Data?
The question now is what do we do with all the data we collect? This is where we borrow ideas from the field of statistics and machine learning. Both of these fields (which are somewhat related) allow us to derive insights from data that we previously could not. The use of both of these tools ushered in the age of data analytics. This was followed by even more advanced tools such as Artificial Neural Networks (ANN’s) which allowed us to deal with large complex datasets.
A combination of analytics and ANNs allows us to make predictions using the data that we collect. Which inherently is amazing, but there is one kicker here: we need to know what we are putting in.
Data is Only As Good As How Well You Collect & Understand It
You see any prediction method or algorithm is only as good as the data you throw at it. If the data is bad then we cannot derive any value from prediction methods. This is why we cannot blindly collect data. We must be careful to collect the correct kind of data for the correct task.
If you have a system that works with text data, you cannot feed it audio recordings of the same text. You can give an algorithm that works with RGB images a black and white image and expect it to perform the same kind of prediction. These may seem like trivial cases but it’s important to understand that every tool has a limitation, has its own accuracy and does not work in all situations. This is precisely why it is imperative that organizations and individuals invest in making themselves aware of the benefits and limitations of the various data skills. Our ML fact today gives some topics that you can look into to start understanding what are some data skills that you need to know.
One thing you may notice and it also lists two other skills that seem to have nothing in common with data analytics or data sciences: communication, strategy, and planning. We mentioned these two skills because they are extremely important while working with data.
Communication skills are important because you need to be able to communicate essential points about the data you are collecting or the predictions you are making. Planning and strategy are important since you can always keep collecting data, but you need to be aware of what is the outcome that needs to be achieved. Without appropriate data planning, it is close to impossible to know how to leverage your data for success.
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