Predictive data analysis is nowadays the best crystal ball enterprises have, whenever they need to shed light into the future of their operations and to optimize their decision-making. Predictive data analysis focuses on the prediction of future outcomes of business processes in areas like marketing, sales, finance, accounting, manufacturing, logistics and other industrial sectors. One of the main characteristics of predictive analytics techniques is their predictive accuracy i.e., their ability to predict future events with very high success rates.
Predictive analysis is no new technological field: For over three decades, most enterprises have been using predictive analytics as part of their data mining and business intelligence processes. Specifically, they have been implementing and deploying predictive analytics programs over enterprise databases and data warehouses, for tasks like sales forecasting and production planning. Nevertheless, during the last decade, the interest in predictive analytics has become more intense than ever before. This is mainly due to the explosion of the data volumes that are generated by modern enterprises, which boosts the accuracy and versatility of predictive analytics algorithms based on machine learning techniques. Nowadays, enterprises are provided with more data and increased computing power to process them. This empowers them to implement and deploy advanced predictive analytics based on deep learning and other forms of Artificial Intelligence (AI). Leveraging advanced models for predictive analysis over large amounts of historic datasets, organizations can reliably forecast business trends and the outcomes of business processes in ways that were hardly possible a few years before. For instance, they can forecast sales, marketing, and production trends across different timescales i.e., from short real-time time scales of few seconds to predictions about what could happen within a five-year horizon.
Modern predictive analysis is not based on a single technology but rather on a combination of technologies that span statistics, machine learning, databases, business intelligence, data visualization and more. Some of the most popular predictive analytics software packages and predictive analytics tools are:
Predictive analytics are used in a variety of industries and business contexts. Here are some prominent examples:
The are hundreds of other use cases in almost every industrial sector. Furthermore, predictive analytics companies are constantly seeking new use cases and innovative ways to leverage the power of predictive analysis.
Overall, the transition to the data-driven economy is associated with rising use of predictive data analytics. In this context, modern enterprises must integrate predictive analysis in their data management and data analytics tools. Moreover, they must undertake the ever-important transformation of their business processes in ways that can take advantage of predictive analytics. Predictive analysis is here to stay, and companies must do whatever it takes to benefit the most out of it.
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