Big data fuels predictive analytics because without adequate data it’s difficult for organizations to make accurate predictions about future events. Generally speaking, there is a correlation between a large volume of data and a high degree of accuracy and a smaller volume of data and a lower degree of accuracy. Of course this is assuming that an organization is utilizing best practices and effective data management techniques. When this is the case, the larger the volume of data, the better – and as an organization accumulates more data, this allows it to make more accurate predictions and create actionable intelligence for the future.
When is Data Considered Big?
The term “big data” is often used in a broad sense and somewhat subjective. According to a popular big data study in 2011 by McKinsey & Company, it’s defined as “data sets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Some would say that once data gets in the gigabyte range that it’s considered big, while others would say that it’s petabytes. Regardless of what the precise definition may be, the more data an organization has, the better its decision-making typically becomes.
Examples of Utilizing Predictive Analytics with Big Data
By having access to a large body of data such as previous customer purchases, buying patterns, etc., a business could use both stored and real-time information to its advantage when sending out promotions. For example, if a business knows that a group of customers have purchased a particular product, they could send out promotional materials featuring a similar product in which the customers would be highly likely to buy.
Another example pertains to website exploration. For instance, if a customer looked at site pages featuring a particular product or service, the company could provide a unique website experience that’s tailored to the customer’s specific interests.
While having only a small body of data would probably allow some degree of accuracy in terms of gauging what customers are looking for, having a much large body of data should give the company a significantly higher level of certainty. The point is that big data is usually advantageous over small data because it helps organizations get the most out of predictive analytics.
Accuracy is Contingent Upon Data Quality
On a side note, it’s important to mention that there are limitations to big data – and there’s a certain point where there’s so much data that it’s actually counterproductive and a hindrance. For big data to be effective, it’s critical that an organization utilizes some form of data management where it becomes properly organized and obsolete information is deleted once it’s no longer useful. Basically, big data must be “tamed” and structured in a way that it ensures that the information found via predictive analytics is legitimately helpful.
The bottom line is that predictive analytics used in conjunction with big data enables organizations to make sound decisions. And when best practices are utilized, this gives an organization a significant edge when predicting future events.