How predictive analytics will revolutionize software development

Predictive analytics refers to the use of data, statistical algorithms, and machine learning techniques to predict future outcomes. It is quickly becoming a key part of the global business landscape, and for good reason. Thanks to the rapidly increasing volume of structured and unstructured data available to companies around the world, businesses can use predictive analytics to gain valuable insights they are unlikely to obtain elsewhere. Best of all, companies can use these insights to make accurate, data-driven predictions so they can stay ahead of their competition.

Expect the push for predictive analytics to continue in the foreseeable future. The combination of fast, high-powered, and inexpensive computers, easy-to-use software, and the need for competitive differentiation is accelerating the demand for predictive analytics. Meanwhile, the sooner a business integrates predictive modeling and other predictive analytics technologies into its everyday operations, the sooner it can discover innovative ways to differentiate itself from rivals.

Common predictive analytics applications

There is no shortage of predictive analytics applications available to businesses. Some of the most common predictive analytics applications include:

  • Fraud detection: Various predictive analytics methods empower law enforcement agencies to identify and prevent criminal activities. Additionally, behavioral analytics tools enable companies to detect cyber fraud, zero-day vulnerabilities, and advanced persistent threats.
  • Marketing: With predictive analytics, marketers can analyze customer responses or purchases and identify cross-sell opportunities. Plus, predictive modeling empowers marketers to determine the best ways to attract and retain a company’s most profitable customers.
  • Operations: For organizations that want to take their productivity and efficiency to new heights, predictive analytics is ideal. Predictive analytics allows businesses to create predictive models to forecast their inventories and manage their resources effectively.
  • Risk management: Predictive analytics may prove to be exceedingly important for anyone who wants to buy a home, car, or boat. For instance, a lender typically reviews an individual’s credit score, i.e. a figure that is generated via a predictive model that leverages data related to a person’s creditworthiness, prior to approving or declining a borrower’s request for a mortgage, car loan, or boat loan.

How does predictive analytics apply to software development?

Predictive analytics is an important part of software development, too. Software developers produce lots of data related to testing, running an actual build, and other everyday tasks; this data usually dictates the short- and long-term success of a project.

Generally, software developers collect data and create prescriptive analytics to detect patterns “hidden” in large structured and unstructured data sets. To develop effective algorithms, software developers use Big Data, which consists of data sets that follow the “three V’s” of velocity, variety, and volume. These data sets have large amounts of data (volume) that range from test results to log files (variety) and can be processed at a high speed (velocity).

Next, a data scientist performs extensive research to select the ideal algorithms. Each algorithm must be tailored to both the data being used and the issue at hand.

Finally, after software developers have data and algorithms, they can identify patterns and forecast future problems that a business or an end user might face. This helps a company recognize patterns and trends, plan for the future, and, perhaps most important, explore ways to gain a competitive advantage.

What does the future hold for predictive analytics?

From cross-platform mobile development to customer relationship management software, predictive analytics can be applied in a number of ways across all industries. Going forward, businesses should expect predictive analytics to continue to evolve, particularly with the advancement of machine learning technologies.

Machine learning is a form of artificial intelligence (AI) that automates analytical model building. It is based on the idea that computers can learn from data, identify patterns in data sets, and make decisions with little to no human intervention. Thus, with machine learning, software developers and others can quickly and automatically produce models that can analyze bigger, more complex data, and deliver faster, more accurate results than ever before. As a result, the combination of predictive analytics and machine learning could help businesses discover new revenue generation opportunities, find ways to reduce risk, and speed up the process of achieving their short and long-term goals.

At Seamgen, our team of software developers and data scientists are experts with multivariate time series data and understand the ins and outs of predictive analytics, machine learning, AI, and other state-of-the-art technologies. Using our existing algorithms and API’s we can easily craft a custom AI solution to meet your business needs.. To learn more about Seamgen and how we use predictive analytics for software and application development, please contact us today.

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