Big Data can be defined as the massive amount of structured and unstructured information that a business accumulates daily, spanning anywhere from business transactions to social networking insights. Without the proper management tools, this data is essentially useless.
Since available data is so expansive, the normal tools that have been used for data analysis up until now are no longer up to the task of handling it. Finding meaningful patterns and relationships among data is crucial to understanding the next moves. This goes for industries ranging anywhere from manufacturers to bankers.
So how can companies take an expansive set of figures and turn them into insights that will help capitalize on sales processes without having to sift through tons of useless information?
The answer: Turn Big Data into Small Data. Small Data is a subset of data made up of very specific attributes.
These attributes are gleaned by analyzing these larger chunks of data and translated into meaningful insights for a business. Guy Kroupp, CEO of Coralogix, a service that extrapolates vital information from Big Data through log analysis for businesses, explains "Collecting and visualizing big data used to be one of the biggest issue companies faced five years ago. Today, companies want to extract pinpoint insights which provide their business with the maximum value without having to dig in terabytes of data."
By isolating whole blocks of Big Data and turning them into much more manageable chunks of Small Data, businesses can extract the insights that are applicable to them while leaving out the excess data residue that is less meaningful.
What Is There To Find?
The question remains. What exactly should we look for in this data analysis, and how do we divide it into manageable tasks? Some recommend designating employees as "data owners," making individual managers responsible for various sections of data in order to spread responsibility for the important information across the company.
This way, no single individual will be overwhelmed by too much data to keep track of, and there is less danger of vital information somehow slipping through the cracks. Guidelines should be set up that instruct employees on which data is considered meaningful to log and analyze as opposed to the data that is less useful for the business's goals.
Different types of data analysis can include things like correlational analysis, visual analysis and horizon (scenario) analysis, depending on the aims of the business. Visual analytics involve creating an actual graph or visual aid that makes it easier to find patterns in the data, which turns the available information into a more tangible set of Small Data, thus making it easier for the human eye to analyze.
Correlation analysis is helpful in determining whether two variables are possibly related to one another, and what the direction of the relationship is (if there is an increase in one variable, can it accurately predict an increase or decrease in another variable), while horizon analysis functions as the helper of future decision making through examining the various potential outcomes of alternative future events.
How It Is Used
There is also a benefit to using data-analysis technology to make this process more manageable and less daunting. An example of a system that helps businesses utilize big data for their sales is Intelligence Node, which builds SaaS-based tools that use algorithms to break down Big Data into insights applicable to retail intelligence.
It is an imperative step for retailers in improving customer satisfaction and maximizing sales.
The potential this data affords you as a business is so vast and didactic, it is crucial to isolate the important variables and pay attention to the insights that will be most helpful for you.
Whether it's through analysis by the naked eye or the virtue of a technological tool, managing your Big Data will get you the figures you need to improve on performance and make future decisions that will take your company where you envision it going.