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. Show
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?Image Source 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:
To learn more about Data Mining, visit here. Image Source Key Benefits of Data Mining
Image Source 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:
Data Mining Applications
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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:
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Key Differences Between Descriptive and Predictive Data Mining
Descriptive and Predictive Data Mining: DefinitionDescriptive 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 ApproachIt’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: PrecisenessBecause 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: TasksThe 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: RequirementsData 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 MethodsStandard 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:
ConclusionThis blog explains the key differences between Descriptive and Predictive Data Mining. It also gives an overview of Data Mining and its applications. Integrating and analyzing your data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo is a No-code Data Pipeline and has awesome 100+ pre-built integrations that you can choose from. Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data and create your Dashboards. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure. What is the difference between descriptive and prescriptive analytics?Descriptive Analytics tells you what happened in the past. Diagnostic Analytics helps you understand why something happened in the past. Predictive Analytics predicts what is most likely to happen in the future. Prescriptive Analytics recommends actions you can take to affect those outcomes.
What is the difference between descriptive and predictive analytics quizlet?While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.
What is the difference between data analytics and predictive analytics?Data analytics is 'general' form of Analytics used in businesses to make decisions which are data driven. Predictive analytics is 'specialized' form of Analytics used by businesses to predict future based outcomes. Data Analytics consists of data collection and data analysis in general and could have one or more usage.
Which of the following best describes the key difference between descriptive and predictive analytics?c. In descriptive analytics you identify what happened and determine how you're performing against that plan; whereas, in predictive analytics you use patterns to predict future trends. Descriptive Analytics tells you what happened in the past.
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