Reasons to be excited about Data Analytics

Harvard Business Review stated in an article that big companies are adopting analytics in 2018. However, if seen closely, most of these companies are yet to initiate a data-driven culture. Frankly, the current scenario is something like this: Companies believed that they are data-driven, but it only in the recent times that specific parameters are being laid down to explain what it means to be data-driven. It is time to clear the air- data collection is not same as data analytics; the latter being a disciplined data science.

This means having an isolate reporting structure from Facebook or Google, or any other marketing platform is not the same as answering queries backed by data about your customers and their buying journey.

We are living in a time when customer-experience is the primary focus of any business, whether B2C or B2B. If you are not able to process the huge amount of customer data to predict their purchase behaviour, your customers will show you the exit door in no-time.

There is a reason why the Indian Government has turned to data analytics to track leakages in the GST or major firms turning to data analytics to gain a more competitive edge in their run for acquisitions. Data analytics is the key that can help businesses dig out the elements that often go unnoticed while handling customer complaints or more simply, customer targeting.

The wonders of data analytics is not a myth but a necessity that businesses have now started taking notice of, more seriously. Each time it is about improving business practices, organizations go over analytics because analytics is the key to finding meaningful patterns in big data. These patterns unlock information that is crucial for business decisions. This is just the overview of why data-scientists are vouching for data analytics and encouraging beginners to learn it. Reasons are in galleons, but here are few reasons that make sense of all the excitement and hype that surrounds data analytics.

Reason #1: Data visualization for complex concepts

Raw data is huge and comes with huge volume and velocity. Since visuals are the more communicative and interactive, it makes more sense to mold data into graphical representations rather than rows and columns of numbers. If you listen to David McCandless’s Information is Beautiful, you will understand why visualization is important when it comes to an understanding and identifying data patterns. And this is possible only through data analytics because without analytics there is no insightful information and no properly aligned business decision.

Reason #2 Traditional business analytics is making way for agile analytics

Traditional business intelligence projects are all about time. They take years which makes it difficult to fit in into the original requirements. On the contrary, agile analytics is turning the tables by reducing the learning time manifold times. Thanks to all the new analytics tools, business insights are more actionable bringing down the delivery time by weeks or months. By putting together raw data with a set of hypothesis and people competent with domain knowledge, analytics experts are speeding up the decision process to determine whether a long-term implementation will benefit a business or not.

Reason #3: Data Analytics has become more prevalent over time

The most important reason that makes all the buzz about data analytics worth it: Data analytics is becoming more and more prevalent. Whether it is about automating assessments for forecasting or creating customized recommendations (used by banks), advanced analytics is spreading into multiple industries like fire. Data analytics lets businesses go back into the past and understand customer behaviour to predict the future. In short, businesses are steadily moving into predictive analytics from descriptive analytics.

The first step to begin with data analytics should always be detailed analysis; analysis to ensure that you have the right tools at your disposal and the right processes to start with. You must be sure about your historical data before you start using it to predict the immediate future.

Data analytics is not just any hyped terminology or a passing phase. It is here to stay and for good. There are multiple ways of finding value from your data, only if you know where to begin.

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