Analytics is not considered as a separate department anymore in organizations but rather a function of every department. It is a critical component to understand how effective each department is operating and identifies ways through statistical methods to improve profits
In the pre-digital era, when every record and information was in the form of ledgers and notebooks, collation of data was nearly impossible. Preparation of profit loss statements took weeks and was rarely accurate. The Digitization of records made it possible to archive, summarize and tabulate the data. Analytical graphical representations became the in-thing for MNC’s and its impact had multiple effects
Several companies started offering analytical offerings to other clients which was primarily non-existent ten to fifteen years back. The methods deployed were age old and probably very effective if properly applied. Be it a normal exploratory analysis of your data or identifying patterns and building a model over it, analytics is a tough subject that cant be easily approached.
Analytics is defined by Wikipedia as the discovery and communication of meaningful patterns in data. If the organizations invests in proper softwares to keep track of their data , they can deploy analytics to make a meaningful impact.
Impact of analytics includes:
Regression, Modelling, Decision Trees, Clustering, Prediction, Exploratory Data Analysis, Segmentation , Data Mining are some of the common methods deployed in an analytics solution.
Research has clearly shown that analytics has impacted the profits of the company by more than a billion dollars a year. Even a small change in the implementation can twist the tides in their favour and make it a profitable scenario. Companies using analytics solutions were probably the ones who were able to survive the tough times during the recessionary periods
Being a data scientist myself, sometimes it’s an uphill task to learn and implement the analytics concepts. They vary from data to data, and not a straightforward process. One requires an extraordinary eye for detail , good programming knowledge in softwares like R and able to extra meaningful information from the vast amounts of data a company generates
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