Data Analytics: Need of the Hour

 

Data Analytics: Need of the Hour

We all know that data is the new commodity which is highly valued these days. And when we speak of data, we not only speak of personal or financial data, which is considered as highly confidential, but also mere information of the sites we happen to visit on a daily basis. This can be of great potential to people who can use it for their respective business gains.
Generation of data during the past decade has increased exponentially due to the invention of social media and the amount of individuals for whom it is freely and easily accessible.

Let’s take a quick look on some fun facts related to data generation that takes place on a daily basis:
2.5 quintillion bytes of data are produced by humans every day.
500 million tweets are sent.
294 billion emails are sent.
4 petabytes of data are created on Facebook.
4 terabytes of data are created from each connected car.
65 billion messages are sent on WhatsApp.
5 billion searches are made

Looking at the above figures makes us wonder how we can use this vast sea of data for achieving our respective needs and goals.
In comes Data Analytics. To define it in simple words, it can be described as qualitative and quantitative techniques and processes used to enhance productivity and business gain.
Now, if we see data in its raw form, it is surely of no use for making any decision or predicting any trend. There are certain number of operations which need to be done on this raw data to refine it for one’s specific need.
Following are a few steps of Data analytics:
Collection of Data according to the purpose
Cleaning and manipulation of the raw data
Model Planning
Model building
Extracting results
Operationalize based on the results

The refined data that we speak of can be translated into different forms such as graphs, histograms, etc. Modelling and visualizing plays an important part in data analytics process as this is the stage at which the decision making individuals of an organization get an insight of the situation at hand.

Let’s take an example of a marketing firm, which has collected a data of a certain number of customers’ choices of products they view in their apps or browser. Applying data analytics to this raw information in a proper way can be very fruitful for the company. They can target the proper audience with appropriate set of product ads and offers of which seems to be in their trend of interest. And due to the tools which are now available to analyze the data, these decisions are taken at quicker rate.

Data analytics can help grow businesses, both old and new to the fields, as it helps to get a clear picture of the industry as well as to discover new opportunities. In the changing economy, making profit means to be in trend with the dynamicity of the market’s/customer’s need. It not only helps the business to grow and provide better services to its clients, but also generates a lot of job opportunities for working professionals. In the past 5 years, data analytics has injected certain new profiles in the job categories. Roles such as data scientist, data engineer, data analyst, etc. are becoming common positions in every small & large organization.
Due to the growing effectiveness of data analytics, IT and analysts are starting to increase their co-ordination. It has also put a tad of a pressure on IT to provide an infrastructure so that the people responsible to make decisions for the organization get their answers quickly and correctly. We have till now addressed the data which is generated by humans on a daily basis, now to speak about the data generated by machines. Data generated by machine, be it as common as your car GPS location, or the reading of your smart blood pressure monitor, can be very resourceful if put to proper application.

Data analytics has also intrigued students; we see new courses which are being developed and adapted in the curriculum as there is a growing demand for such profiles. It has also created many research opportunities which can be explored. Data analytics itself can be applied on student database to get better performance and set appropriate goals based on the student’s proficiency level and interests.

Data Analytics has great application in the medical field as well.  One such use is for image processing of a tumor to determine whether it is malignant or benign. Another good example is to predict the spread of pandemics and take necessary precautions/measures for the same.

To conclude, there are a lot of advantages of data analytics and soon it will be essential for an organization to survive. It not only finds new opportunities but also detects the hidden potential within the existing ones.