Data Mining VS Predictive Analytics: Detailed Comparison

What unites your company and, say, Apple? The answer is – the desire to attract the maximum number of customers while also retaining regular clients.

In this regard, Big Data has become more prevalent in the current business world.

Source: Unsplash by @franki

But to make the most of data oceans, companies have to make sense of all those valuable insights. Predictive analytics and data mining are what help businesses to look into the future and obtain critical information. This way, you can predict and shape customer behaviors, forecars sales patterns and other trends.

Both predictive analytics and data mining address data-driven objectives. Yet, those two notions are not the same. So today, we’re looking at the difference between data mining and predictive analytics so you can do well out of each.

What Is Data Mining?

Data mining or knowledge discovery in data (KDD) is the process of analyzing heavy volumes of data to discern trends and patterns valuable for the company. These patterns lay the ground for effective management decisions and optimization of business processes.

From a technical perspective, data mining relies on three pillars:

• Mathematical statistics – empowers most data mining techniques, e.g. cluster analysis, regression analysis, discriminant analysis, etc.;
• Artificial Intelligence – helps replicate a human neural network in digital form;
• Machine learning – a combination of statistics and artificial intelligence to help computers understand the data they are processing and choose the most suitable analytics method.

How Data Mining Works: Process Steps

KDD encompasses a holistic process that is amplified with a wide range of data techniques, including clustering, association, and sequential pattern analysis as well as decision trees. 

But no matter the algorithm, the whole lifecycle can be divided into seven main steps. 

1. Data cleaning – to discard noisy, incomplete, or erroneous data.
2. Data integration – to combine information from multiple sources.
3. Data reduction – to glean relevant and useful data from a large dataset.
4. Data transformation – to convert into forms suitable for mining.
5. Data mining – using intelligent methods to uncover patterns in the data. 
6. Pattern evaluation – to identify potentially useful, easy-to-understand patterns as well as patterns that support hypotheses.
7. Knowledge representation – presented information in an appealing way using knowledge representation and visualization techniques (reports, tables, etc.)

Real-World Use Cases Of Data Mining

Data Mining techniques prove useful in a great number of industries. E-commerce, fintech and IT are among the most popular enthusiasts of knowledge discovery. Data Mining tools enable analysts to address the following targets:

1. Customer segmentation

By tapping into customer data and identifying similar characteristics among consumers, companies can deliver personalized marketing efforts that address the unique needs of a particular customer group.

2. Market basket analysis

Data mining is used here to uncover associations between items. Thus, if you buy a new pair of jeans, you’re likely to buy a belt to go with the chosen item. 

3. Sales forecasting

KDD is also used to predict when a customer will buy a product again in the future. It is done to estimate the number of products or services a company will sell within a certain time period.

4. Fraud detection

By collecting samples of historical data (i.e. fraudulent and authentic reports), businesses will be able to spot suspicious activity before it wreaks havoc. 

What Is Predictive Analytics?

Predictive analytics is a form of intelligent data analysis that uses statistics, data science, machine learning, and other techniques to predict future trends and patterns. At first sight, the definition looks the same as data mining.

The capabilities of predictive analytics allow companies to produce future insights with high accuracy. In this case, any business can benefit from historical and current data to reliably predict patterns and trends months into the future. 

Predictive analytics entails three types of techniques: 

• Descriptive models, 
• Predictive models, 
• and decision models. 

Descriptive analytics refers to the early stage of data processing. It allows us to describe past events, answering the question, “What happened?”. At the same time, decision models help evaluate and enhance predictive models to ensure they will make a business difference.

How Does Predictive Analytics Work?

An intelligent system (predictive mathematical model) analyzes the huge amount of available data. It studies business and production processes in real time, integrates with ERP systems as well as systematizes the amount of information received, and predicts the further outcome of events. 

Based on the obtained insights, the system evaluates current operational activities and suggests improvement areas.

Real-World Use Cases Of Predictive Analytics

The application areas of predictive analytics overlap with those of data mining. For example, we can see this type of analytics in niches such as:

1. Customer attraction and retention

Based on the available customer data, clients are divided into segments according to their preferences, behavior, socio-demographic parameters, and financial capabilities. Then, historical data allows companies to build a predictive model and evaluate the viability of a particular marketing offer. 

2. Improved customer experience

Besides amplifying attraction strategies, predictive analytics also enhances the customer experience of existing clients through personalized recommendations. Typically, fine-tuned marketing efforts are done with the help of clustering methods. 

3. Churn prevention

Smart models avert companies of customers who are likely to opt-out of the company’s services. By leveraging the power of Big Data, businesses can then develop predictive models that enable proactive intervention before it’s too late.

4. Chatbots

Virtual assistants like Siri learn from interactions as well as model and predict user behavior, including future responses.

How Are Data Mining And Predictive Analytics Different?

By now, you must’ve caught up in those two notions. Indeed, both data mining and predictive analytics rely on intelligent algorithms to disclose brand-new insights that help facilitate and guide optimal business solutions.

Therefore, there’s some rationale behind the fact that Data Mining and predictive analytics are often used interchangeably. In fact, Data Mining methods and tools underpin the work of predictive analytics, but predictive analytics is a more broad and universal system than Data Mining. 

The data mining lifecycle feeds on algorithms to process and extract information that unveils unseen tendencies within the data. As for predictive analytics, it then uses uncovered data tendencies to make predictions with the help of machine learning. 

Hence, the major difference between them is that data mining explores the data but predictive analytics enhances this data and allows you to look into the future.

To Wrap It Up

According to PRNewswire, the global Big Data market is expected to reach over $234 billion by 2026. It means that a growing number of companies are investing in data solutions to usher in more intelligent business models and decisions. In particular, data mining and predictive analytics are spearheading the intelligent use of data oceans.

So, if you haven’t embraced this opportunity yet, there has never been a better time to embed data-driven practices into your operations.