The Power of Predictive Analytics in Data Analytics

Business intelligence tools are fast becoming an essential part of businesses today. Company executives are exploring the utility of these tools to optimize business processes that can result in better business outcomes. One of these options is to use predictive analytics, a form of data mining technique that uses historical data to make forecasts of future trends and events.  

This article explains predictive analytics in data analytics solutions, its importance, and how it can bolster your business performance.  

What is Predictive Analytics?

Although predictive analytics has existed for some time, it is now coming into its own due to the rapid rise of big data and modern technologies like artificial intelligence (AI).  

Predictive analytics is the process of applying statistical algorithms and machine learning techniques to data to uncover the probability of future events and trends based on historical data. The aim is to find out what will happen in the future based on what has already happened.  

The reasons why predictive analytics is now becoming really a popular and widely-used technology are because:  

  • There is widespread interest in the business community in using data to generate valuable insights.  
  • Big data is now the go-to commodity for companies – they cannot harness its power without using AI and machine learning, and predictive analytics.  
  • Computers are now a lot more economical than they were a few decades ago.  
  • The use of software has spread far and wide, and today’s software is also easier to use.  
  • Economic competition has increased dramatically, forcing companies to seek innovative ways to gain and retain a competitive edge.  

Thus, business intelligence jobs and their associated roles, like data analysis, etc., are no longer restricted to mathematicians and statisticians – business intelligence tools like Power BI and Tableau enable non-IT people to generate critical business insights.  

These insights include understanding customer behavior—what your customers like and dislike, what they want from you, and what their behavior will look like going forward. Moreover, predictive analytics also helps you unearth new opportunities and vistas for innovation and market entry. It also helps identify potential threats and problems before they arise.  

Let’s see how predictive analytics works.  

How does Predictive Analytics Work?

All business professionals in the 21st century, particularly those who do not have a background in business analytics or data science, need to learn the basics of data analytics technology. This need stems from the critical importance of big data in today’s technology-driven global economy.  

You need three things to implement viable and robust predictive analytics strategies.  

  • Data – You must have access to a large amount of reliable data that can be used to generate forecasts to unravel opportunities and threats.  
  • Statistics – Predictive analytics is primarily a statistical job that involves the use of regression analysis, etc.  
  • Assumption(s) – All predictive models are based on some assumption. Thus, you need to know what that assumption is and persistently check if it still holds true. Usually, businesses assume that the future will look pretty much like the past.  

These three components are the building blocks of predictive models. But what are predictive models?  

Developing Predictive Models

Predictive models use available results to train or develop a model that can be employed to predict values for new or different data. Modeling gives predictions based on input variables representing the chances of a particular target variable occurring. These may include revenue, costs, etc.  

Predictive models are not like descriptive or diagnostic models. Descriptive models enable you to understand what has happened. On the other hand, diagnostic models show why something has happened. Analytic models and techniques have long been the subject of academicians. Countless books are available today that talk about these techniques in detail.  

But we only need to understand the basics. There are two types of predictive models.  

  • Classification models: These models aim to derive conclusions based on input values provided for training. They classify data based on categories/classes
  • Regression models: These models predict a number. For example, a regression model predicts how long a machine component will work or how much revenue will be generated over a given period.   

Popular Predictive Modeling Techniques

There are three popular predictive modeling techniques in use today. Let’s look at these briefly. They are as follows:  

  1. Regression (logic and linear): It is a very popular statistical method. Regression analysis evaluates the relationship between variables. It enables you to identify critical patterns in a data set or multiple data sets. As a modeling technique, regression analysis is used to identify what impact specific factors (price, cost, etc.) will have on an item (e.g., asset price).  
  2. Decision trees: These are classification models that divide data into subsets contingent on classes of input variables. They enable you to identify and understand someone’s decision paths. A decision tree is a classification model that looks like a tree. Each branch of the tree represents an option or choice from several different alternatives. And each leaf represents a decision or classification.  
  3. Neural networks: These are nlp modeling techniques that deal with very complicated and complex relationships. Neural networks owe their popularity to their robust and flexible nature. They can manage the modeling of non-linear data and are used to verify the results of both regression analysis and decision trees. Moreover, neural networks focus on pattern recognition, which was developed by AI engineers to mimic the workings of the human brain.  

Here’s a list of some of the other modeling techniques that you might come across:  

  • Memory-based reasoning  
  • Partial least squares  
  • Bayesian analysis  
  • Gradient boosting  
  • Ensemble models  
  • Principal component analysis  
  • Support vector machine  
  • Incremental response (also known as uplift or net lift models)  
  • Time series data mining  
  • K-nearest neighbor (KNN)  

Benefits of Predictive Analytics

  1. Fraud Detection: You can use predictive analytics and combine them with other analytical techniques to discover criminal behavior patterns. You can act proactively to prevent fraud and mitigate risks. As the importance of cybersecurity grows, the use of data analytics tools and techniques also increases exponentially.  
  2. Enhancing Operational Efficiency: Many companies use predictive models to manage inventory levels via forecasting. Large retailers and wholesalers use these models to ensure they have optimum stock levels at all times. Even hotels and airlines use predictive models to increase their revenues. In short, predictive analytics enables companies to enhance operational performance.  
  3. Risk Reduction: Banks use predictive analytics to assess credit risk. They try to find out the default chances of potential clients. Insurance companies also use these models to predict the risk scores of individuals and organizations.  
  4. Improved Marketing Campaigns: Predictive models are often employed to identify customer behavior, response, and purchase patterns. Thus, these models help you tailor your marketing campaigns according to the needs of your target audience, enabling higher business growth and brand recognition.  

Here’s a list of industries that employ predictive analytics:  

  • Banking and Financial Institutions  
  • Oil, Gas, and Energy Companies  
  • Retail and wholesale  
  • Governments and Public Sector Organizations  
  • International Organizations  
  • Non-governmental Organizations (NGOs)  
  • Healthcare Sector  
  • Manufacturing Industries   


As AI continues to transform how we perceive the world around us and conduct business, techniques like predictive analytics will only grow in importance. Companies are also becoming aware of the power of predictive analytics. Thus, the industry is growing and will continue to expand by leaps and bounds in the coming years.  

If you want to use predictive analytics for your business, contact us at [email protected]. We will build a top-notch solution for you! 

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