The term itself is not new and there is a very good reason for that. Companies across the globe, both large conglomerates, and small startups are utilizing its potential to gain valuable insight into existing operations for future development and to improve their customer service.
Take today’s data for instance. According to a study conducted by scientists at the UC San Diego, by 2024, most businesses across the globe would have processed the digital equivalent of a gigantic number of books that if placed on top of each other, could go from Earth to Neptune and back. At the rate global enterprises are focusing on big data, that feat would be repeated 20 times each year!
What is Big Data Analytics?
However, just why are so many enterprises dependent on this phenomenon? This is where analytics comes into the picture. The process refers to the examination of big data to reveal revealing patterns, significant correlations and other useful info that business owners can use to increase decision making and unearth new opportunities. Data scientists today use such data to get access to and simplify huge volumes of info that traditional analytics falls short of.
To understand its importance, say your company has already collected large amounts of data along with multitudes of data combinations, formats, and stores. Analyzing billions of rows of data to figure out what is important is not possible manually. Big Data analytics can allow you to go through said information in context via:
These processes have allowed countless business owners to streamline their decision-making processes and pinpoint the best ones for enterprise development. Additionally, there are four ways entrepreneurs are harnessing the power of Big Data analytics to improve their businesses:
Big Data Business Intelligence
Business Intelligence or BI refers to typical business reports, ad hoc reports, alerts, OLAP and even notifications that are based on this process. The main aim of this process is to analyze the static past that can be used to determine future actions. When reporting involves extraction of data from huge data sets we call it performing Big Data business intelligence or BI. However, the decisions that result from these two methods are largely reactionary.
This method is largely proactive and requires a hands-on approach which involves optimization, predictive analytics, modeling, text mining and statistical analysis on a large scale. These processes allow big data analysts to pinpoint weaknesses, strengths and also figure out new and better decision making practices for the future. However, this is where it gets interesting; using big data analytics, business owners can hone in on and extract relevant information for easy analysis.
In other words we can say that big data analytics is more than just a one-time endeavor. If they are proactive with it, business owners can do wonders for their enterprises and remain ahead of their competitors with tactics that the latter would not be privy to.