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.
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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.
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:
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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:
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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:
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:
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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.
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.
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.
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.
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.
Standard Reporting, Query/Drill Down, and Ad-hoc Reporting are the operations performed in the Descriptive approach, and they can generate a response of:
Predictive Mining carries out tasks such as Forecasting, Simulation, and Alerting. These are the key outcomes that are fulfilled by Predictive Data Mining:
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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Harshitha Balasankula Marketing Content Analyst, Hevo DataHarshitha is a dedicated data analysis fanatic with a strong passion for data, software architecture, and technical writing. Her commitment to advancing the field motivates her to produce comprehensive articles on a wide range of topics within the data industry.