Predictive analytics and data mining are often used interchangeably, but they address very different parts of the same process We’ll explain how each technique works, why you need them, and how they work together.
Predictive analytics prepare you for what’s on the horizon. While total accuracy is impossible, the best predictive analytics and modeling platforms provide an informed, insightful picture. They do this by interpreting past and current data from as many sources as possible to identify patterns and build models that can project these trends into the future. By combining descriptive and diagnostic analytics, predictive analytics algorithms gain a good sense of what happened and why, in order to pinpoint similar conditions in the future.
Every business wants to reduce risk and plan efficiently for the future, so it’s hardly surprising that predictive analytics has been embraced by companies across a wide range of sectors. Broadly speaking, predictive analytics can be used to identify new opportunities, boost revenue, and spot potential problems before they do any damage.
Predictive analytics help to keep maintenance costs under control. Using past and current performance data, predictive analytics will indicate when equipment is headed for a breakdown and anticipate a part replacement. Timely intervention means you aren’t at risk of costly unplanned downtime.
Data mining refers to a systematic approach to finding patterns and connections in Big Data sets. Combining elements of artificial intelligence (AI), machine learning and statistics, it is a process that applies algorithms and statistical methods to huge datasets and files to search for anomalies, rules and trends at great speed. Data mining organizes huge depositories of data making it easy to pull useful insights.
Data mining helps to arrange your data into patterns that help you make sense of it with by creating models that you can apply to future incoming information. The foundations of data mining are routed in organization and can help to make informed decisions.
The difference between data mining and predictive analysis is that data mining is all about classification, grouping and pattern-forming, creating models that are useful for anticipating the next stage in the trend in a very simple way. Predictive modeling, on the other hand, requires the tools to interpret that information in a specific business context, often drawing on other, less rigid, information streams alongside structured or numerical data.
Predictive analytics are only as powerful as the data that’s fed into it. If this is incomplete, misclassified, or improperly organized, it can’t inform the model. This means careful and effective data mining is an extremely important first step for predictive analytics.
In short, predictive analytics and data mining are both ways of making sense of data in ways that shape your future behavior. However, data mining helps you understand what you already have, whereas predictive analytics tries to apply this understanding to situations that have not yet occurred.
If you are struggling to make sense of your data, check out how LNS Research guides you through the shift from metrics to analytics.