When historical data is used to help predict future events, it is called predictive analytics. Generally, historical data is used to build a model that helps capture important trends over time. That model is then used with current data to help predict what is going to happen next. 

It may also help suggest actions businesses can take in order to get the best possible outcomes. Due to the recent increase in technology, the attention given to predictive analytics is gaining speed, especially in the areas of machine learning and big data.

Why Predictive Analytics Matters

•Big Data- A significant reason why predictive analytic applications matter is because of big data. For many businesses, their data might include sales results, transaction data, marketing information and customer complaints. All of this information together is used to create the predictive analytics model and help businesses make data-driven decisions for their future based on this information.

•Increasing Competition- For businesses to be successful, they need to continually find ways to beat out the competition. Predictive and big data analytics helps businesses solve long-standing problems with new approaches. Businesses may also use analytics to help create accurate forecasts to be used in their future planning.

New Technologies For Machine Learning And Big Data Analytics

In order to gain value from the use of big data and predictive analytics, companies gather the data and apply them to algorithms using tools like Spark ml. The data sources can consist of an equipment log file, transactional databases, videos, sensors, audio, images, equipment log files and other types of data a business may collect.

None of this data is worth much unless there are tools used to extract trends and insights from it collectively. Machine learning techniques can be used to help find patterns in a company's data and help build models to help predict possible and likely future outcomes.

How Does Predictive Analytics Work?

Predictive analytics is used in many industries to process large amounts of data to make predictions. Along with the data, the analysis and machine learning techniques are used to form a predictive model for forecasting events in the future. The process starts with a business goal outcome of using data to reduce waste, cut costs or save time. 

The process uses the massive amount of data sets to create models that generate actionable and clear outcomes to help businesses support achieving their goals. Examples of goals include less stocked inventory, fewer materials used and creating manufacturing products that meet specifications more efficiently.

Industries That Use Predictive Analytics To Manage Data

•Automotive- Automotive companies around the world are working on developing driving-assisted technologies and autonomous vehicles. These autonomous vehicles use predictive analytics to build driver-assisted algorithms.

•Financial Services- The financial industry uses predictive analytics to forecast prices and demands and develop credit risk models for offering loans to its customers. Quantitative tools and machine learning techniques are used to predict a customer's credit risk.

•Aerospace Industry- In the Aerospace industry, real-time analytics applications are used to monitor aircraft engine health. The analytics applications can be used to predict performance from various parts of the aircraft. 

Workflow Of Predictive Analytics

The workflow of predictive analytics follows for basic steps during the application.

1. Import Data- Data is gathered from various sources such as databases, web archives and spreadsheets.

2. Clean Data- The data is then cleaned of outliers as all data is combined together. The software application will identify missing data, data spikes and anomalous points to be removed from the data sources.

3. Create Model- The third step in the workflow of predictive analytics is developing an accurate model based on all of the data collected from various sources using curve fitting tools, machine learning and statistics.

4. Put Model To Use- Once the predictive model has been created, companies can use it to make predictions for the future. The model can be integrated into various load forecasting systems in a production environment.