Predictive Analytics

4 min readJun 22, 2022


The concept of humans using past data to predict future events has been around for decades, however, only recently has predictive analytics found itself in the spotlight. So, what exactly is it and why is everyone talking about it?

Predictive analytics is the use of data, statistical algorithms, and machine learning to identify future trends and outcomes based on the analysis of past data. The goal of predictive analytics is to give the best approximation of what will happen in the future using advanced techniques and algorithms. It is easy to see how extremely useful such a technology could be in a business context. In recent years, as computers and algorithms become ever more advanced, these predictions are almost a mathematical certainty. But if the concept itself is not exactly new, why is predictive analytics so popular all of a sudden? Let’s look at some of the reasons why.

Why is predictive analytics relevant now?
● More Data — The Internet is the storehouse of a large amount of information. There is data constantly being added or updated. This data can be analyzed to identify popular trends and obtain information as to the viewer demographics.
● Faster, cheaper computers — We have come a long way since the first computers. Technology has progressed at such a quick rate that the smartphone in your hand is many times more powerful than the computers that made the first landing on the Moon.
Thus, predictive analytics is now easier and more efficient than ever before.
● Economic reasons — The economic conditions have been getting more and more competitive in the last few decades This has led to a demand for competitive differentiation. Companies are looking for an edge in bringing products to crowded markets, and predictive analytics supplies that edge.
● Interactive and easier-to-use software — With the advancement of user-friendly software,
predictive analytics is no longer just the domain of statisticians and mathematicians.
Thus as predictive analytics is finally getting its time to shine, it becomes ever more important to know exactly how it can help us.

Why is predictive analytics important?
● Detecting fraud — As cybercrime becomes more of a threat, the use of predictive analytics to identify patterns and detect any deviation or abnormality is very important in identifying criminal behavior. High-performance behavioral analytics examines all actions on a network in real-time to identify potential frauds and zero-day vulnerabilities which help to keep the internet safe and secure.
● Optimizing marketing campaigns — Based on statistical data collected, predictive analytics can be used to determine the level of purchases or type of customer response
to a given product. Predictive analytics help businesses to attract, retain and increase the number of desirable customers to maximize profit.
● Improving operations — Many companies use predictive analytics to conduct their operations more smoothly and efficiently. Companies use predictive models to predict inventory levels and manage resources.
● Reducing risk — Predictive models are used to obtain credit scores, numbers that give an idea as to the likelihood of a buyer paying back a loan. Other applications of predictive models are in insurance claims and collections.
Now that we know why predictive analytics is significant, let’s look at the people who use this tech on a daily basis.

Who’s using predictive analytics?
As we have seen, predictive analytics reduces risk and improves operations, hence it is used in fields where a large amount of data is at stake such as the banking and financial sector, retail, and public sector.
In the case of the Banking sector, predictive analytics detects and reduces fraud, measures credit risk, maximizes cross-sell/up-sell opportunities, and retains valuable customers. A fun fact is that the Commonwealth Bank uses analytics to predict the likelihood of fraudulent activity for any given transaction before it is authorized — within 40 milliseconds of the transaction initiation, which is a great leap in the field of predictive analytics. Just like that in the Retail sector, predictive analytics plays the role of the merchandise planner where it optimizes the offer that is best for the consumer and finds the outcome of the promotions is done, and so on. In the Energy sector, predictive analytics is used to predict equipment failures to mitigate safety or reliability issues. Predictive analytics has become so widely accepted that in the Energy sector the Salt River Project, the second-largest public power utility in the US and one of Arizona’s largest water suppliers, does analyses of machine sensor data to predict when the powergenerating turbines need maintenance.
We all know that Governments and public sectors have been key players in computer advancements where they now use predictive analytics like many other industries — to improve service and performance, detect and prevent fraud, better understand consumer behavior and improve cybersecurity. Big tech giants like Lenovo also use predictive analytics to better understand warranty claims.

But before using this tech, it is important to know exactly how it works. Let’s look at how the workflow happens in predictive analysis.

Workflow of predictive analysis
At first, the data is imported from different sources in the form of CSV files. Then the data is filtered and data sources are combined, after that with the help of different variables, and by using the neural network, we train a predictive model by using statistics or machine learning. Once the training is complete, the model is tested against new data. When the model starts
working accurately, it is ready to be available for further applications.

The takeaway?
In short, predictive analytics is a very exciting, up-and-coming technology with a great many applications. It is a field that more and more people are taking up nowadays and it can provide a lot of job opportunities very soon. All else aside, just the fact that we can now, within reason, see into the future is in itself truly remarkable.