To draw parallels to set theory, data analytics is the universal set, and predictive analytics is a specialized set inside the data analytics. The broader objective set by the organization to obtain data, check data, clean data, and transform data to get meaningful insights is called data analytics. In comparison, predictive analytics is to model the data with the already cleaned set of data to predict future outcomes. There is multiple predictive analytics software that can help in performing this specialized operation. The end objective of predictive analytics is to forecast the future.
This article will guide you to explore the differences in detail; read on to know.
- Data analytics assumes a much more general form, while predictive analytics is more specific. Predictive analytics uses various techniques to forecast future outcomes. But, data analytics aids organizations with decision-making on a much broader objective.
- Data analytics encompasses collecting data, cleaning data, and analyzing it. The same data could be used for multiple types of analysis. But, predictive analytics models and monitors the data to forecast future outcomes.
- Typically, the arrangement in data analytics is to obtain, inspect, clean, and transform data to get conclusions. But, predictive analytics is to model the data and do rigorous testing. Then, it is continuously monitored and improved for accurate predictions.
- The analysis in data analytics could be anything – prescriptive analytics, predictive analytics, or descriptive analytics. But, predictive analytics is just predicting future outcomes with past data. It involves a hypothesis and testing it with various statistical techniques.
- The uses of data analytics are vast. It could be as general as understanding customer behavior, studying competitors, improving product offerings, etc. But, predictive analytics is more specific. Predictive analytics software could help in planning the production for the next quarter, optimizing pricing for maximizing sales, etc.
Data analytics is broadly divided into three types:
The existing data set is studied with this technology, and the results it caused are examined. It is more a summary of what happened. It may or may not help predict the future outcome and is the most accessible form of analytics to aid in providing hindsight analysis. Although it can add some value in understanding the basics, it by no means helps in providing any predictions or prescriptions.
This is slightly more complicated, which involves building models and employing statistical techniques to predict future outcomes. The software helps in building the model, training the model, monitoring the model, and predicting accurate outcomes. This is of some value to the organization as it analyses past data and forecasts the future.
This is of great value to the organization, as it predicts and helps improve outcomes. It uses both descriptive and predictive analytics and plugs in choices. These choices can improve the results. This is complex as it involves framing options with a lot of brainstorming. The ideas could be tested with this model, and if one such idea has significant positives, the organization can pilot it and employ it to improve its objectives.
To sum it up, data analytics is getting cues from data to answer questions that the organization has at a broader level. At the same time, predictive analytics is to get answers to questions for predicting future outcomes.
Also Read: How Can Analytics Help Businesses?