Descriptive and Predictive Data Mining Comparison: 6 Critical Differences

The main goal of Data Mining is to find valid, potentially useful, and easily understandable correlations and patterns in existing data. Data Mining can achieve this goal by modeling it as either Predictive or Descriptive in nature.

The Descriptive and Predictive Data Mining techniques have a lot of uses in Data Mining; they’re used to find different kinds of patterns. To mine data and specify current data on past events, Descriptive Analysis is used. Predictive Analysis, on the other hand, provides answers to all queries relating to recent or previous data that move across using historical data as the primary decision-making principle.

This article talks about the key differences between Descriptive and Predictive Data Mining. In addition to that, it also talks about Data Mining and its key benefits.

Table of Contents

• What is Data Mining?
  • Key Benefits of Data Mining
  • Why Data Mining?
  • Data Mining Applications
• What is Descriptive Data Mining?
• What is Predictive Data Mining?
• Key Differences Between Descriptive and Predictive Data Mining
  • Descriptive and Predictive Data Mining: Definition
  • Descriptive and Predictive Data Mining: Type of Approach
  • Descriptive and Predictive Data Mining: Preciseness
  • Descriptive and Predictive Data Mining: Tasks
  • Descriptive and Predictive Data Mining: Requirements
  • Descriptive and Predictive Data Mining: Practical Analysis Methods
• Conclusion 

What is Data Mining?

Data is unquestionably valuable. However, analyzing it is not easy. With the exponential expansion of data, a technique to extract relevant information that leads to usable insights is required. This is where Data Mining comes into place. Data Mining acts as the backbone for Business Intelligence and Data Analytics.

Data Mining can be defined as the process of analyzing large volumes of data to derive useful insights from it that can help businesses solve problems, seize new opportunities, and mitigate risks. It can be leveraged to answer business questions that were traditionally considered to be too time-consuming to resolve manually

It is the process of finding patterns in large volumes of data to translate them into valuable information. Data Mining Tools help you get comprehensive Business Intelligence, plan company decisions, and substantially reduce expenses. 

Due to the expanding significance of Data Mining in a wide range of industries, new tools, and software improvements are constantly being introduced to the market. As a result, selecting the appropriate Data Mining Tool becomes a challenging and time-consuming procedure. So, before making any hasty judgments, it’s critical to think about the company or research needs. There are two types of Data Mining Techniques, Descriptive and Predictive Data Mining.

By using a range of statistical techniques to analyze data in different ways, businesses can seamlessly identify patterns, relationships, and trends. For example, the world’s most popular streaming platform, Netflix, has approximately 93 million active users per month. The data pipeline of Netflix captures more than 500 billion user events per day. This includes data on various things such as video viewing activities, error logs, performance reports, etc.

The storage of this data requires approximately a storage space of 1.3 Petabytes (1 Petabyte = 1,000,000 Gigabytes) per day. The advantages of having such high volumes of data are as follows:

• It allows Netflix to plan its future releases by analyzing the kind of content viewers like.
• It allows Netflix to understand how they can make the user experience on their website and Android/iOS applications better by analyzing user behavior on these services.

To learn more about Data Mining, visit here.

Key Benefits of Data Mining

• Pattern Discovery: Automatic pattern discovery is a strategic advantage, and this technique helps in modeling and predicting future behavior.
• Trend Analysis: Understanding trends keeps you up-to-date with current developments in the industry, and helps reduce costs and timeliness to market.
• Fraud Detection: Data Mining techniques help in fraud detection by discovering anomalies in datasets. This is used to detect which insurance claims, credit card purchases, etc., are likely to be fraudulent.
• Forecasting in Financial Markets: Data Mining techniques are extensively used to model financial markets and predict likely outcomes.

Why Data Mining?

Every two years, the amount of data produced doubles. 90% of the digital universe is made up of unstructured data. However, having more information does not always imply having more knowledge.

You can use Data Mining to:

• Sift through your data to find all of the random and repetitive noise.
• Understand what’s important, and then use that knowledge to predict what will happen.
• Increase the speed with which you can make well-informed decisions.

Data Mining Applications

• Telecom, Media & Technology: In a crowded market with intense competition, the answers are frequently found in your customer data. Analytic models can help telecommunications, media, and technology companies make sense of mountains of customer data, allowing them to predict customer behavior and deliver highly targeted and relevant campaigns.
• Education: Educators can predict student performance before they enter the classroom using unified, data-driven views of their progress, and develop intervention strategies to keep them on track. Data Mining allows educators to gain access to student data, predict achievement levels, and identify students or groups of students who require additional support.
• Insurance: Insurance companies can solve complex problems like Fraud, Compliance, Risk Management, and Customer Attrition using analytic expertise. Companies have used Data Mining techniques to better price products across business lines and discover new ways to offer competitive products to their existing customer base.
• Manufacturing: It is critical to align supply plans with demand forecasts, as well as to detect problems early, ensure quality, and invest in brand equity. Manufacturers can predict asset wear and maintenance, allowing them to maximize uptime and keep the production line on schedule.
• Banking: Banks can use automated algorithms to better understand their customers as well as the billions of transactions that make up the financial system. Financial services companies can use Data Mining to gain a better understanding of market risks, detect fraud more quickly, manage regulations, and compliance obligations, and maximize the return on their marketing investments.
• Retail: Customer insight hidden in large customer databases can help you improve relationships, optimize marketing campaigns, and forecast sales. Retailers can offer more targeted campaigns and find the offer that has the greatest impact on customers by using more accurate data models.

What is Descriptive Data Mining?

Descriptive Mining, as the name implies, “describes” the data. You convert the data into a human-readable format once it has been collected.

Descriptive Analysis is used to extract information from data and to specify current information about past events.

In simple terms, Descriptive research entails identifying interesting patterns or associations among data.

Descriptive Mining is commonly used to generate correlation, cross-tabulation, frequency, and other similar results. These methods are dedicated to uncovering patterns and finding regularities in data. The other use of Descriptive Analysis is to find the most interesting subgroups in a large set of data.

Descriptive Analytics is concerned with summarising and converting data into usable information for reporting and monitoring. Furthermore, it allows for a thorough examination of the data so that questions like “what happened?” and “what is happening?” can be easily answered. 

There are four different types of Descriptive Data Mining tasks. They are as follows:

• Clustering Analysis: It is the process of determining which data sets are similar to one another. For example, to increase conversion rates, clusters of customers with similar buying habits can be grouped together with similar products.
• Summarization Analysis: It entails methods for obtaining a concise description of a dataset. For example, summarising a large number of items related to Christmas season sales provides a general description of the data, which can be extremely useful to sales and marketing managers.
• Association Rules Analysis: This method aids in the discovery of interesting relationships between various variables in large databases. The retail industry is the best example. As the holiday season approaches, retail stores stock up on chocolates, with sales increasing before the holiday, which is accomplished through Data Mining.
• Sequence Discovery Analysis: It’s all about how to do something in a specific order. For instance, a user may frequently purchase shaving gel before purchasing a razor in a store. It all comes down to the order in which the user purchases the product, and the store owner can then arrange the items accordingly.

What is Predictive Data Mining?

Predictive Data Mining is the Analysis done to predict a future event or other data or trends, as the term ‘Predictive’ means to predict something. Business Analysts can use Predictive Data Mining to make better decisions and add value to the analytics team’s efforts. Predictive Analytics is aided by Predictive Data Mining. Predictive Analytics, as we all know, is the use of data to predict outcomes.

An example of this is, Any retailer can use algorithm-based tools to look through a customer database and predict future transactions by looking at previous transactions. In other words, previous data may allow the shopkeeper to forecast what will happen in the future, allowing businesspeople to plan accordingly.

Its main goal is to predict future outcomes rather than current behavior. It predicts the target value using supervised learning functions. Classification, Time-Series Analysis, and Regression are the methods that fall under this category of Data Mining. Data Modeling is a requirement of Predictive Analysis, and it works by combining a few current variables with unknown future data values for other variables to predict the future.

There are four different types of Predictive Data-Mining tasks. They are as follows:

• Classification Analysis: It is used to retrieve critical and pertinent data and metadata. It categorizes information into various groups. Classification Analysis is best demonstrated by email providers. They use algorithms to determine whether or not a message is legitimate.
• Regression Analysis: It tries to express the interdependence of variables. Forecasting and prediction are common applications.
• Time Serious Analysis: It is a series of well-defined data points taken at regular intervals.
• Prediction Analysis: It is related to time series, but the time isn’t restricted.

Key Differences Between Descriptive and Predictive Data Mining

• Descriptive and Predictive Data Mining: Definition
• Descriptive and Predictive Data Mining: Type of Approach
• Descriptive and Predictive Data Mining: Preciseness
• Descriptive and Predictive Data Mining: Tasks
• Descriptive and Predictive Data Mining: Requirements
• Descriptive and Predictive Data Mining: Practical Analysis Methods

Descriptive and Predictive Data Mining: Definition

Descriptive Mining is frequently used to provide Correlation, Cross-Tabulation, Frequency, and other types of information. It analyses stored data to determine what happened in the past.

Predictive Data Mining is the Analysis done to predict a future event or multiple data or trends. It explains what might happen in the future as a result of past Data Analysis.

Descriptive and Predictive Data Mining: Type Of Approach

It’s crucial to remember that the amount of data available, the type of data, and the dimensions all play a role in determining which Data Mining approach to use.

Descriptive Data Mining is based on the reactive approach that is it just responds to the situation. When you want the data to respond to events after they happen, you use the reactive approach. Reactive Analysis isn’t possible for obvious reasons. It means that businesses respond to situations after the fact, which means they can’t prevent negative consequences or build on past successes. At best, this approach should be used sparingly.

Predictive Data Mining entails both controlling and responding to a situation, implying that it is based on a proactive approach. As it is used to forecast the types of data you’ll see in the future, prediction is one of the most valuable Data Mining techniques. In many cases, simply recognizing and comprehending historical trends is sufficient to make a reasonable prediction of what will occur in the future.

Descriptive and Predictive Data Mining: Preciseness

Because information is so important in a business, having accurate and reliable data to base your decisions on is critical. This is how you’ll make the right decisions and outsmart your opponents.

The Descriptive approach is more precise and accurate. It is thought to help identify variables and new hypotheses that can then be investigated further in experimental and inferential studies. It is useful because the margin for error is very small. After all, the trends are extracted directly from the data properties.

Predictive Data Mining produces outcomes without ensuring accuracy. Predictive Data Mining models have always relied on past patterns to forecast the future. It is based on previous behaviors, events, and trends that you believe will occur; however, accuracy cannot be guaranteed.

Descriptive and Predictive Data Mining: Tasks

The various types of patterns to be identified in Data Mining activities are perceived by Data Mining functionalities. Data Mining features are used to define the types of patterns that will be discovered during Data Mining activities.

Descriptive Mining tasks are used to describe the properties of data in a target data set. Descriptive Data Mining tasks are used to find data describing patterns and to extract new, significant information from a data set. A Descriptive Data Mining task could be defined as a retailer attempting to identify products that are purchased together.

Predictive Mining tasks infer from current and past data to make predictions. Predictive Data Mining tasks create a model from the available data set that can be used to predict unknown or future values in a different data set of interest.

Descriptive and Predictive Data Mining: Requirements

 Data Mining is also useful for summarising the data in such a way that the result is understandable and meaningful to end-users. This relationship is discovered through the use of linear equations, rules, clusters, graphs, and recurrent patterns in time series, among other methods. Find information in data sets that are stored in Databases, Data Warehouses, Online Analytical Processes, and other repositories.

To discover historical data, Descriptive Data Mining employs two techniques: Data Aggregation and Data Mining. To make the datasets more manageable for analysts, data is first collected and sorted by data aggregation.

Predictive Data Mining requires the use of Statistics and Data Forecasting Techniques. Predictive Data Mining is a type of advanced analytics that uses historical data, statistical modeling, Data Mining techniques, and Machine Learning to make predictions about future outcomes. Predictive analytics is used by businesses to find patterns in data and identify risks and opportunities.

Descriptive and Predictive Data Mining: Practical Analysis Methods

Standard Reporting, Query/Drill Down, and Ad-hoc Reporting are the operations performed in the Descriptive approach, and they can generate a response of:

• What went wrong?
• What exactly is the issue?
• What is the problem’s frequency?

Predictive Mining carries out tasks such as Forecasting, Simulation, and Alerting. These are the key outcomes that are fulfilled by Predictive Data Mining:

• What’s going to happen next?
• What will happen if current trends continue?
• What are the steps that must be taken?

Conclusion

This blog explains the key differences between Descriptive and Predictive Data Mining. It also gives an overview of Data Mining and its applications.

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