Predictive Analytics vs Data Mining: What’s the Difference?

With the advent of AI, technologies like data mining and predictive analytics have gained much popularity.

‘Predictive analytics Vs data mining’ is a much-discussed topic in the industry but to understand that one must first comprehend the scope of data in today’s world of business. Data has become the lifeline of any business that wants to survive and succeed in modern times. The only problem is that there is too much data. It is estimated that about 402.74 million TB of data is produced each day around the globe. Visual Capitalist gives a figure close to 12,000 data centers around the globe with about 45% (5381) of them being in North America, followed by 521 in Germany, 514 in UK and 449 in China.

According to a report by Fortune Business Insights, the global data analytics market size was valued at $64.99 billion in 2024, and is projected to grow from $82.23 billion in 2025 to $402.70 billion by 2032, at a CAGR of 25.5% during the forecast period. So, that is the volume of data we are talking about, and it is humanly impossible to analyze all that data and decide what’s relevant for your business. This is where technologies like data mining and predictive analytics come into play. Though the two technologies may look similar to untrained eyes, they serve distinct purposes and have different applications. Also, it can be safely said that the two are not mutually exclusive and one serves the other and vice-versa.

This post aims to discuss what each of these technologies are and how businesses can leverage their data more effectively and efficiently by making use of both.

Predictive Analytics Vs Data Mining: Know the Difference

Data Mining

As the word ‘mining’ suggests, the data mining process is the extraction of usable data from a bigger set of raw data to detect patterns and establish relationships. Data mining is done on historical data, which can range from a simple array of a few numeric observations to a complex matrix of millions of observations with thousands of variables. The source of data can be web, database, or data warehouses.

Data mining is the process of turning large data sets into general information that can easily be comprehended by people. During this process, many clusters of information and patterns would emerge which would then be sorted, analyzed and classified.

The following are the four steps involved in data mining:

• Data cleaning and preprocessing: At this stage, the raw data is vetted for erroneous, incomplete, or inconsistent data, as well as formatted into a usable format for analysis. Preprocessing includes normalizing data, reducing dimensionality, and identifying important features. Once these are done, the data becomes ready for exploration and visualization.

• Data evaluation and modeling: This stage involves training machine learning models with the data and then evaluating their performance. At this stage an appropriate algorithm is selected, its hyperparameters are tuned to optimize its performance, and the results are evaluated using measures such as accuracy or precision. After a model is trained and evaluated, it can be deployed for real-world applications. Any anomalies or outliers in the data are also detected at this stage. This is especially useful for fraud detection and cybersecurity applications. Any anomalies or outliers detected can be used by analysts to investigate further and gain more insight into the problem.

• Data exploration and visualization: This process includes exploring, analyzing and visualizing data to gain insights and identify patterns. Data professionals like data analysts, data scientists, and data analysts summarize the data using various descriptive statistics to cluster or classify to group similar data points together. This helps them gain insight into the underlying structure of the data and understand the relationship between features. They then use data visualization tools like bar graphs, histograms, heatmaps and others to see how different data sets correlate. After this process of exploration and visualization is complete, data professionals can decide which machine learning algorithms would be ideal for their project.

• Deployment and maintenance: Deployment is the final stage of data mining, where the trained models are configured and released in real-world production environment. If any changes are made to the model or dataset, it may require re-training the model and redeploying it to production. Nevertheless, the final stage does not mean, install and forget. Regular maintenance and updating of any changes to the data or environment are also necessary to ensure optimum performance. This would ensure that the data mining models remain accurate and can give reliable results to their respective businesses.

Predictive Analytics

You can say that data mining serves as a first step for predictive analysis because it uses data gathered during data mining to identify trends and tendencies to give predictions of future to businesses. To put it in simple terms, predictive analytics provides future trend forecasting based on data provided by data mining based on historical data, which is quite different from what data mining does.

The primary purpose of predictive analytics is to go beyond identifying and understanding what has happened, to offer the best estimation of what will happen in the future. This process extracts valuable data from an existing system, and identifies specific trends and tendencies based on the data provided by data mining. The system then makes use of AI and machine learning algorithms to model future results.

Although predictive analysis does not give 100% accurate results, businesses can still use this data to understand their consumers better, anticipate the trends they follow, and mitigate future risks. Predictive analytics helps businesses to be proactive and take the necessary action at the right time. It also allows businesses to anticipate future results and develop preemptive strategies for a wide range of future scenarios while avoiding crises.

The steps involved in predictive analysis are similar to that of data mining but the expected outcome is what differentiates them. Data mining is for insights into historical data to understand the current trends better and make better business decisions, while predictive analysis helps enterprises by forecasting upcoming trends and making them future-ready.

According to a report by Grand View Research, the global predictive analytics market size was valued at $18.89 billion in 2024 and is projected to reach $82.35 billion by 2030, growing at a CAGR of 28.3% from 2025 to 2030.

This shows the scope of predictive analytics going forward.

Industrial applications of Data Mining and Predictive Analysis

Reiterating what was said before, data mining and predictive analytics work best together as complementary technologies and both have proven to be essential business intelligence tools for enterprises of any size, segment or region. Here are a few examples for the deployment of both the technologies in some of the (but not restricted to) major industries:

Finance

‍Data mining and predictive analytics in the finance industry can be used for:

• Fraud detection
• Credit scoring
• Identifying investment opportunities
• Forecasting stock prices, and market trends
• Assessing credit risks

‍Healthcare

• Patient record analysis
• Predicting disease outbreaks
• Treatment optimization
• Predict patient outcomes
• Manage hospital resources

‍‍Retail

• ‍Improves customer segmentation
• Market basket analysis
• Inventory management
• Customer behavior prediction
• Optimize pricing strategies
• Demand forecasting

‍‍‍Marketing

• ‍Identifying target audiences
• Analyzing campaign performance
• Understanding customer sentiment
• Enables personalized marketing
• Predicting and optimizing marketing spend

So, what is the difference between data mining and predictive analytics?

Simply put, these are complementary tools rather than different entities. There could not have been predictive analytics without data mining since there would be no place to get information for further predictions.

And, in turn, data mining would be less impactful without predictive analytics because the structured information alone is insufficient without an actionable plan. Data mining demonstrates today’s picture, while predictive analytics tells you what to do with it tomorrow.

However, to make the best of these business intelligence tools, enterprises must also consider addressing its challenges like having access to clean and proper data, as the two are only as good as the data they are trained on. Given the fact that the two technologies have the potential to improve with time, there also arises the need for skilled professionals to evaluate and maintain them and train them on the right data sets.

You can talk to our experts to better understand how DeepKnit AI can help you leverage technologies like data mining and predictive analytics to scale up your business.