Data Science in simpler words for non-IT business leaders
For non-IT business leaders, complete awareness of all available tech solutions is a daunting task. Yet it is also true that organizations that are at the forefront of technology adoption have some of the best performing business KPIs. In the light of the above arguments, it becomes imperative that every non-IT business leader should at least be aware of key tech verticals and potential uses in their own work areas.
What is Data Science?
First let's understand what is meant by science. Britannica definition of science is as follows: knowledge about or study of the natural world based on facts learned through experiments and observation. Let's extend it to Data Science.
Data Science can be called as the knowledge gained by study of Data (Data can be of your organization or external data or a combination of both) based on facts learned through experiments and observation.
Let me make the life easier by giving a very simple example. A 75o Fahrenheit (or 23.9o C) room temperature will feel like 70oF (21.1oC) at 10% humidity level while it will feel like 80oF (26.7oC) at 100% humidity. So, if you are the operations manager of a five-star hotel, a chart like on the left will help you adjust HVAC (Air Conditioning) temperatures best suited to your guests.
Looking closely on the example above, it can be said that a relationship exists between three variable : Room Temperature, humidity and Apparent (perceived) temperature. So, a model between these 3 variables can help HVAC operators in discharging their duties better (which is what the chart on the left is all about).
Now you can apply your thinking hat and extend the above example to any field irrespective of the industry. If there are variables (input and output) that have a correlation with each other, it is a potential Data Science use case. Quite easy, isn't it?
What does it take to implement a Data Science project?
First let's understand what is meant by science. Britannica definition of science is as follows: knowledge about or study of the natural world based on facts learned through experiments and observation. Let's extend it to Data Science.
Data Science can be called as the knowledge gained by study of Data (Data can be of your organization or external data or a combination of both) based on facts learned through experiments and observation.
Let me make the life easier by giving a very simple example. A 75° Fahrenheit (or 23.9°C) room temperature will feel like 70oF (21.1°C) at 10% humidity level while it will feel like 80°F (26.7°C) at 100% humidity. So, if you are the operations manager of a five-star hotel, a chart like on the left will help you adjust HVAC (Air Conditioning) temperatures best suited to your guests.
Looking closely on the example above, it can be said that a relationship exists between three variable : Room Temperature, humidity and Apparent (perceived) temperature. So, a model between these 3 variables can help HVAC operators in discharging their duties better (which is what the chart on the left is all about).
Now you can apply your thinking hat and extend the above example to any field irrespective of the industry. If there are variables (input and output) that have a correlation with each other, it is a potential Data Science use case. Quite easy, isn't it?
What are the expected benefits from it?
Huge, simply huge if done in the right way. Data is the new Oil, but it is of no value if left unrefined. More so, if refined in the wrong way it may prove to be disastrous. In our engagements, we have typically seen baseline ROI up to 100 times in such projects.
Some of the few use cases and expected benefits are listed below.
