Data Mining And Predictive Analytics: Head to Head Comparison

In 2021, companies are going with all guns to pique customer interest. To do that, they have to establish a clear understanding of their clients, including preferences, needs, and pain points.

And oceans of data drive the success or failure of any company. However, data comes in different flavors.

Overall, with big data topping the business ladder, a lot of data terms get thrown around. Data science, data mining, and data analytics – they all sound the same.

But the devil is in the details. A profound understanding of the difference between data mining and predictive analytics will help provide you with a deeper insight into using data science to benefit your company.

With that said, let’s have a closer look at predictive analytics and data mining.

A Few Words About Predictive Analytics

This type of analytics refers to a class of data analysis techniques that focus on predicting the future behavior of objects in order to help businesses make optimal decisions.

 

In layman’s terms, predictive analytics relies on the combo of Artificial Intelligence, Data Science, and historical data to estimate the possibility of this or that outcome.

 

The basis of predictive analytics is Big Data. Common examples of Big data include:

  • Internet-generated data – website traffic, online shopping data, etc;
  • Corporate information – transactions, reports, the number of buyers;
  • IoT readings – data from various sensors, telemetry data;
  • Economic indicators, etc.

 

All this data can lay the ground for effective estimation and amplify your business operations with new insights.

What Is The State Of Predictive Analytics Market?

Back in 2018, the global market size stood at $5.7 billion. During the period of 2019-2025, it is projected to hit a CAGR of 23.2%.

 

A gamut of factors contribute to this swell in demand:

  • The market is growing as companies become more aware of the data they collect and its potential to anticipate future outcomes utilizing predictive analytic solutions.
  • The growing use of the online space, combined with the advent of multiple ways to access it, has resulted in a tremendous boom in the amount of data generated.
  • This, in turn, ushered in the global spread of high-velocity digital services.
  • Also, the spike in IoT technologies has offered a platform for predictive analytics solutions.

How Does Predictive Analytics Work?

The workflow of predictive analysis revolves around analysis, statistical methods, and ML techniques. Combined, they produce a predictive model for future outcomes.

 

The step-by-step process includes the following steps:

  • Data collection – sourcing and exploring data from various setups;
  • Data cleaning – eliminating erroneous or incomplete information;
  • Deep data analysis – discovering patterns and trends in the data;
  • Statistical regression methods – analyzing the relationship between some output variables and one or more input variables;
  • Machine Learning techniques – assisting in building models;
  • Model construction – generating predictive models;
  • Deployment – deploying the models into existing systems.

 

Once your organization has put the model into practice, you need to perform ongoing monitoring of predictive models. New relevant data should be integrated as it becomes available to boost the effectiveness of the model.

Examples of Predictive Analytics In Business Intelligence

The application area of predictive analysis is enormous. From banking and healthcare to technology and agriculture, predictive techniques bode well for industry performance.

 

Here’s how your organization can avail itself of predictive analytics:

  • CRM operations – intelligent ad campaigns, sales, and high-quality customer services can all be made possible with forecasting. The whole customer life cycle from acquisition to retention will rely on data-driven decisions and accurate forecasting of marketing activities.
  • Detecting fraud – this type of analysis can also provide pattern detection for criminal behavior. The system can analyze current behavioral patterns and compare them against new ones to catch abnormalities.
  • Reducing risks – probabilistic risk assessment helps fetch clear forecasts and minimize operational risks.

Other applications of predictive analysis include improvements in business operations, underwriting, churn prevention, quality improvement, and others.

What Is Data Mining?

Data Mining or Knowledge Discovery In Data (KDD) refers to the process of discovering previously unknown business-critical knowledge or anomalies in raw data for decision-making in various areas of business activity.

 

Usually, data mining is applied to a large volume of data by applying detective patterns. As a highly application-driven domain, data mining has inherited a wide range of techniques from other domains. The latter include Machine Learning, data warehouses, pattern recognition,  high-performance computing, and others.

 

The main advantage of KDD is that its tools can find hidden patterns independently and also build hypotheses about relationships with no manual input.

How Does Data Mining Work?

A textbook data mining project sets off with understanding your business goals, gathering the right data to achieve them, and preparing the data for analysis. The result of each stage depends on the quality of the data. Therefore, data miners must choose only high-quality input for analysis.

 

Data Mining includes the following steps:

  • Problem definition – identifying the goal of knowledge discovery;
  • Data gathering and preparation – getting the data ready for further analysis by accessing and sampling it;
  • Model building and evaluating – creating, testing, and evaluating the model;
  • Knowledge deployment – applying the model and visualizing results.

The Applications Of Data Mining

The use cases of Knowledge Discovery overlap with those of predictive analytics:

  • Banking –  banks use KDD to better understand market risks (e.g. loan applications).
  • Retail – data mining provides predictive data on product sales trends, seasonal variations, peak traffic periods, and others.
  • Insurance – predicts fraudulent claims and costs for future disasters, based on historical data.
  • Manufacturing – KDD amplifies a lot of manufacturing stages, including fault detection, predictive maintenance, quality assurance, maintenance scheduling, and decision support systems.

Overall, the application scope of data mining is massive. Since it is applicable to any type of data, including transactional information, relational databases, multimedia databases, and others, data mining provides competitive advantages for any type of business intelligence.

What Is The Difference Between Data Mining And Predictive Analytics?

Since data mining and predictive analytics go hand in hand, we don’t see much difference between them. Indeed, the two rely on algorithms to unearth new insights for business operations. However, data mining forestalls analytics by analyzing and gathering information that automatically uncovers new patterns and relationships. 

 

Predictive analytics then uses these data patterns to provide predictions with machine learning.

In particular, machines feed on past and relevant information to tell the user what happens next.

The Final Word

Effective use of data is essential in the modern intelligent business landscape. With the neck-breaking competition, companies have to make their products and services better and more personalized for the customers.

 

On this note, data mining and predictive analytics allow businesses to gain an unbeatable competitive edge. By gathering and analyzing oceans of business data, these techniques open up a new wealth of hidden opportunities. While data mining is heavily involved in uncovering patterns, predictive analytics makes use of these relationships to provide accurate predictions.

 

 

 

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