Difference Between Data Mining vs Data Analysis
The exponential increase in the volume of data has led to an information and knowledge revolution. It is now a key aspect of research and strategy building to gather meaningful information and insights from existing data. All this information is stored in a data warehouse, which is then used for Business Intelligence purpose.
There are several definitions and views but all would agree that Data Analysis and Data mining are two subsets of Business Intelligence.
Data Mining – Data mining is a systematic and sequential process of identifying and discovering hidden patterns and information in a large dataset. It is also known as Knowledge Discovery in Databases. It has been a buzz word since 1990’s
Data Analysis – Data Analysis, on the other hand, is a superset of Data Mining that involves extracting, cleaning, transforming, modeling and visualization of data with an intention to uncover meaningful and useful information that can help in deriving conclusion and take decisions. Data Analysis as a process has been around since 1960’s.
Let us find out the best Difference between the two in this post.
Head to Head Comparison Between Data Mining and Data Analysis
Below is the Top 7 Comparison between Data Mining and Data Analysis:
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Key Differences Between Data Mining and Data Analysis
Data Mining and Data Analysis are two distinct names and processes yet there are some views where people use them interchangeably. This also depends on the organization or project team undertaking such tasks where this distinction is not marked specifically. To establish their unique identities, we are highlighting the major difference between them are as follows:
- Data mining identifies and discovers a hidden pattern in large datasets. Data Analysis gives insights or tests hypothesis or model from a dataset.
- Data mining is one of the activities in Data Analysis. Data Analysis is a complete set of activities which takes care of the collection, preparation, and modeling of data for extracting meaningful insights or knowledge. Both are sometimes included as a subset of Business Intelligence.
- Data Mining studies are mostly on structured data. Data Analysis can be done on both structured, semi-structured or unstructured data.
- The goal of Data Mining is to make data more usable while the Data Analysis helps in proving a hypothesis or taking business decisions.
- Data Mining doesn’t need any preconceived hypothesis to identify the pattern or trend in the data. On the other hand, Data Analysis tests a given hypothesis.
- While Data mining is based on Mathematical and scientific methods to identify patterns or trends, Data Analysis uses business intelligence and analytics models.
- Data mining generally doesn’t involve visualization tool, Data Analysis is always accompanied by visualization of results.
Data Mining and Data Analysis Comparison Table
Given below is the comparison table between Data Mining and Data Analysis.
| Basis for Comparison | Data Mining | Data Analysis |
| Definition | It is the process of extracting a specific pattern from large datasets | It is the process of ordering and organizing raw data in order to determine useful insights and decisions. |
| Area of expertise | It involves the intersection of machine learning, statistics, and databases. | It requires the knowledge of computer science, statistics, mathematics, subject knowledge, AI/Machine Learning |
| Synonyms | It is also known as Knowledge discovery in databases | Data Analysis is of several types – exploratory, descriptive, text analytics, predictive analysis, data mining etc. |
| Work Profile | Data Mining specialist usually builds algorithms to identify meaningful structure in the data.
A data mining specialist is still a Data Analyst with extensive knowledge of inductive learning and hands-on coding |
A Data Analyst usually cannot be a single person. The job profile involves preparation of raw data, its cleansing, transformation and modeling and finally its presentation in the form of chart/non-chart-based visualizations. |
| Responsibilities | Is responsible for extracting and discovering meaningful patterns and structure in the data | Is responsible for developing models, explanations, testing and proposing hypotheses using analytical methods |
| Output | The output of a data mining task is a data pattern | The output of Data Analysis is a verified hypothesis or insight on the data |
| Examples | One of major application of Data mining is in the E-Commerce sector where websites display the option of “those who purchased this also viewed” | An example of Data Analysis could be “time-series study of unemployment during last 10 years” |
Conclusion
The term Data Mining and Data Analysis have been around for around two decades (or more). They have been used interchangeably by some user groups while some have made a clear distinction in both the activities. Data mining is usually a part of data analysis where the aim or intention remains discovering or identifying only the pattern from a dataset. Data Analysis, on the other hand, comes as a complete package for making sense from the data which may or may not involve data mining. Both require different skillset and expertise and in the following years, both areas will see high demands both data, resources, and jobs.
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