Data Mining vs Data Analysis – An Easy Guide In Just 3 Points
Introduction
Data Mining vs Data Analysis. There is an undeniable fact that data surround us on every corner. Today’s era is fortunate enough to see the growth of the internet and every benefit which comes with accessible information sharing. To put things into perspective, all the clicks made by you, your online presence, the websites you visit, the amount of time you spend on each of the websites you visit, etc., are the data you generate.
With the proper instruments and processing capability, the generated data can then be refined and transformed into relevant insights leading to large corporations’ decisions and dictating their profits. People serving in these fields would find it easy to catch terms like data mining and data analysis. However, for those who are not in these fields, gaining a basic understanding of these terms can probably be confusing.
Data Mining and Data analysis are crucial steps in any data-driven project and are needed to be done with perfection to ensure the project’s success. The exponential expansion in the amount of data has resulted in an information and knowledge revolution. Nowadays, it is a key facet of research and strategy development to gather significant information and in-depth knowledge from available data.
All this information is retained in a data warehouse, which is then used for Business Intelligence purposes. There are numerous definitions and views, but all would agree that Data Analysis and Data mining are two subsets of Business Intelligence. Complying with both fields’ closeness can create finding the difference between data mining and data analysis quite challenging. Before we are in a state to understand data mining vs data analysis comparison, we must first understand the two fields very closely.
1) What is data mining?
Data mining is a process of extracting usable data from a larger set of raw data. It is a subset of data analysis. It implies an efficient and continuous method of recognizing and discovering hidden patterns and data throughout a huge dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence. It uses sophisticated mathematical algorithms for segmenting the data and evaluating the probability of future events. Data mining is also termed as Knowledge Discovery in Data.
To understand what is data mining, you require a pattern recognition frame of mind and an ability for coding to make a mark in data mining. Data Mining specialist generally constructs algorithms to determine relevant structure in the data. A data mining specialist is still a data analyst with comprehensive knowledge of inductive learning and hands-on coding. Data mining can be a cause for concern when a company uses only selected information, which is not representative of the overall sample group, to prove a certain hypothesis.
Data mining helps businesses understand which advertising campaigns will likely create the most involvement, display customised commercials, categorize customers, and optimize advertising spend. It also assists businesses to discover fraudulent activity and anticipate possible fraud. Data mining not only enhances external market performance but can also be used to figure out employee behaviour, anticipate attrition, and evaluate human resources policies.
2) What is data analysis?
Data Analysis is a process of extraction, cleaning, transformation, modelling, and visualization of data with an aim to extract significant and useful information that can be beneficial in deriving conclusions and forming decisions. It is a superset of data mining. Data analysis can be divided into exploratory data analysis, descriptive statistics, and confirmatory data analysis in statistical applications.
To understand what is data analysis, you require a more analytical approach to deal with data analytics. A data analyst usually cannot be a single person. The job profile includes the formulation of raw data, it’s cleansing, transforming and modelling, and eventually its presentation in the form of chart/non-chart-based visualizations. Data analysis is used in business to help organizations make better business decisions.
Whether it is market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, analyzing data will provide insights that organizations need in order to make the right choices. Data analysis is important for businesses today because data-driven choices are the only way to be truly confident in business decisions.
3) Difference between data mining and data analysis
Although data mining and data analysis are two distinct names and processes, there are some views where people use them interchangeably. The usage and the meaning behind the terms depend highly on the context and the company in question. To establish their particular identities such that it will be easier to differentiate between the two, we are highlighting the significant contrasting points between them, which are as follows:
1. Data mining is used in discovering hidden patterns in raw data sets. In data analysis, all the operations are involved in examining data sets to fine conclusions.
2. Data mining studies are mainly performed on structured data, whereas data analysis can be performed on structured, unstructured, or semi-structured data.
3. Data Mining aims to make data more functional while data analysis helps prove a hypothesis or make business decisions.
4. Data mining usually does not include visualization tools. Whereas, data analysis is constantly led by the visualization of results.
5. The output of a data mining task is data trends and patterns while the output of Data Analysis is a verified hypothesis or insight on the data.
6. Data mining involves the intersection of machine learning, statistics, and databases. Whereas, data analysis requires the knowledge of computer science, statistics, mathematics, subject knowledge, AI/Machine Learning.
7. Data mining is based on Mathematical and scientific models to identify patterns or trends. On the other hand, data analysis uses business intelligence and analytics models.
8. Data mining is responsible for extracting and discovering meaningful patterns and structures in the data. However, data analysis is responsible for developing models, explanations, testing, and proposing hypotheses using analytical methods.
9. One of the major applications as an example of Data mining is in the E-Commerce sector where websites display the option of “those who purchased this also viewed”. Whereas, an example of Data Analysis could be “time-series study of unemployment during the last 10 years”.
10. Data mining is also termed as Knowledge discovery in databases. On the contrary, data analysis can be divided into exploratory data analysis, descriptive statistics, and confirmatory data analysis.
Conclusion
The term Data Mining and Data Analysis have been around for a long time. Both data mining and data analytics are essential to be performed perfectly. In whichever arena you move, you cannot deny the significance of both in a data-driven domain of the 21st century. They have been used exchangeably by some group of users while a few have made an apparent distinction in both areas.
Data mining is generally a part of data analysis where the objective or intention remains determining or discovering merely the pattern from a dataset. On the other hand, data analysis occurs as an entire package for making sense from the database that may or may not include data mining. Both areas require distinct skill sets, capabilities, and expertise. In subsequent years, both fields will perceive considerable demand in both the data, resources, and jobs.
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