What is Predictive Analysis?

Predictive analysis or predictive analytics is a branch of advanced data analytics. It helps businesses forecast future events by examining historical data through predictive models.

Data trends and actionable insights gained through predictive analytics tools assist business leaders in making impactful business decisions.

Businesses have big data available today in the form of sales history, customers’ information, customers’ reviews, complaints, etc. All this data is useless if not properly analyzed and converted to something meaningful. 

The predictive analytics models make the process of data analysis for future predictions easier through the use of statistical techniques, data mining, and artificial intelligence techniques.

Artificial neural networks and deep neural networks are trained on big data using machine learning algorithms to find trends and patterns in the business data.

Different machine learning algorithms such as multiple regression, logistic regression, support vector machines, random forest, linear and nonlinear regression, etc. help to analyze data.

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Businesses are able to find opportunities and risks such as potential customers’ demographics, future best-selling products, etc.

Stages of a Predictive Analysis Process

The predictive analysis process consists of the following steps:

1. Project Definition

As a software project manager, you will define the business objectives and the scope of the data analytics process. You will decide the metrics to ensure better monitoring and reporting of data analysis methods. Data sets to be used in predictive analytics are also decided in this stage.

2. Data Collection

Data mining is performed to collect the relevant data from multiple sources defined in the previous stage. Structured data in the form of tables, etc. can be readily used for predictive analysis. However, the majority of the data businesses have today like customer notes, social media posts, etc. is unstructured. 

3. Data Analysis

This stage makes unstructured data suitable for data analysis. Data mining techniques are used to extract the required information from such unstructured data. This clean and formatted data is then used, as an input, to the machine learning and data analysis models.

4. Statistical Modeling

Statistical methods are used to understand the data extracted in the data analysis stage. Assumptions are made and validated against the hypothesis. Predictive modeling is done to develop systems that can predict future events based upon the learning done using machine learning or statistical algorithms. 

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5. Project Deployment

A predictive analysis model is then deployed for business processes to learn from real-time data. Model deployment is a complex process. Deployment personnel ensures that a model is integrated seamlessly with the existing business applications. 

6. Monitoring and Analysis

The performance of the predictive analysis model indicates if it is created and deployed according to the set requirements. Automating reporting system facilitates the process by providing information on the model’s performance on a daily and weekly basis. 

Conclusion

Predictive analytics is helping businesses in multiple ways, from customer relationship management, fraud detection, risk management to marketing strategies and underwriting processes. An elaborate predictive analytics process can help you save resources and increase revenues. 

Read our guide on how to implement predictive analytics in your business for more details.

If you are looking for data scientists and software engineers to implement predictive analytics software, DevTeam.Space can help you. We have software developers experienced in the latest technologies to develop market-competitive software solutions.

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