How has Automated Predictive Analysis Developed Over the Years

Automated predictive analysis is making way for the greatest transformations in the industries!

Automated predictive analysis, or predictive analytics, uses historical data to predict future events. Throughout history, humans are obsessed with predicting the future. The fear of the unknown has led several scientific researchers and professors to develop technologies that can determine the future so that necessary steps can be taken to avoid drastic losses.

Predictive analytics has received a lot of attention in recent years due to its advances in supporting various technologies, particularly in the area of big data and artificial intelligence.

With the increased competition, businesses seek power over their competitors in bringing products and services to crowded markets. Data-driven predictive models can bring companies solutions to long-standing problems in terms of business operations. This technology provides a trove of information from which analytics tools and applications draw insights and predict the upcoming opportunities, suitable investments, and dangers in the market.

Businesses use tools like Hadoop and Spark to extract information from big data. These data sources might consist of transactional databases, equipment log files, images, videos, audios, sensors, and other types of data.

With all this data, tools are necessary to extract insights and trends. Predictive analytics finds patterns in data to build models that predict future outcomes. Other varieties of machine learning techniques are also available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.

For decades, automated predictive analysis has been used by meteorologists to predict weather and climate forecasts. With time, this concept has been used to study consumer behavior, forecast supply and demand in economic statistics, and related purposes.

Automated predictive technology: Today and beyond 

Data is the core of predictive analytics. Earlier, when there were no computers, businesses used other creative ways to understand what the customers want and predict market conditions. These ways did not involve technological tools or applications.

Currently, one of the most vital industrial applications of predictive models includes energy load forecasting to predict energy demand in the future. Energy producers, grid operators, and traders need accurate predictions of energy load to make decisions for managing tasks in electric grids. Grid operators use data to draw actionable insights.

Artificial intelligence and predictive technology, have revolutionized the way advertisers and marketers work. Targeted advertising uses data like previously purchased products, location, and age to serve the target audience. Today, consumer profiles are much more advanced, and enterprises can gather information from various sources.

Predictive analytics is also used to measure vehicle and pedestrian traffic to coordinate traffic lights, public transportation, and even pedestrian crosswalks to facilitate convenience and efficiency in community design. This also boosts the safety of the public and allocates emergency services more efficiently by predicting the number of officers needed on a task and reassigns posts accordingly.

Automated predictive technology, has played a crucial role in facilitating better medical resources. This technology helps improve the patients' health outcomes. Rather than completely relying on the patient's medical history, predictive systems can generate data from a broad spectrum of symptoms, data of other patients, and the treatments used to cure the disease.

AI and machine learning have provided us with various ways through which we can predict the future. With the growing technological evolution in automation and data analysis, our lives will be changed forever and for the better.