Data Mining vs Data Exploration

There are two main methodologies or techniques used to retrieve relevant data from large, unorganized pools. They are manual and automatic methods. The manual method is another name for data exploration, while the automatic method is also known as data mining.

Data mining generally refers to gathering relevant data from large databases. On the other hand, data exploration generally refers to a data user finding their way through large amounts of data to gather necessary information. Let's study both methods in detail and compare their differences.

What is Data Exploration?

Data exploration refers to the initial step in data analysis. Data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, to understand the nature of the data better.

Data exploration techniques include both manual analysis and automated data exploration software solutions that visually explore and identify relationships between different data variables, the structure of the dataset, the presence of outliers, and the distribution of data values to reveal patterns and points of interest, enabling data analysts to gain greater insight into the raw data.

Data is often gathered in large, unstructured volumes from various sources. Data analysts must first understand and develop a comprehensive view of the data before extracting relevant data for further analysis, such as univariate, bivariate, multivariate, and principal components analysis.

Why is Data Exploration Important?

Humans process visual data better than numerical data. Therefore it is extremely challenging for data scientists and data analysts to assign meaning to thousands of rows and columns of data points and communicate that meaning without any visual components.

Data visualization in data exploration leverages familiar visual cues such as shapes, dimensions, colors, lines, points, and angles so that data analysts can effectively visualize and define the metadata and then perform data cleansing. Performing the initial step of data exploration enables data analysts to understand better and visually identify anomalies and relationships that might otherwise go undetected.

Data Exploration Tools

Manual data exploration methods entail writing scripts to analyze raw data or manually filtering data into spreadsheets. Automated data exploration tools, such as data visualization software, help data scientists easily monitor data sources and perform big data exploration on otherwise overwhelmingly large datasets. Graphical displays of data, such as bar charts and scatter plots, are valuable tools in visual data exploration.

A popular tool for manual data exploration is Microsoft Excel spreadsheets, which can create basic charts for data exploration, view raw data, and identify the correlation between variables. To identify the correlation between two continuous variables in Excel, use the CORREL() function to return the correlation. To identify the correlation between two categorical variables in Excel, the two-way table method, the stacked column chart method, and the chi-square test are effective.

There is a wide variety of proprietary automated data exploration solutions, including business intelligence tools, data visualization software, data preparation software vendors, and data exploration platforms. There are also open-source data exploration tools that include regression capabilities and visualization features, which can help businesses, integrate diverse data sources to enable faster data exploration. Most data analytics software includes data visualization tools.

What can Data Exploration Do?

In general, the goals of data Exploration come into these three categories.

1. Archival: Data Exploration can convert data from physical formats (such as books, newspapers, and invoices) into digital formats (such as databases) for backup.
2. Transfer the data format: If you want to transfer the data from your current website into a new website under development, you can collect data from your own website by extracting it.
3. Data analysis: As the most common goal, the extracted data can be further analyzed to generate insights. This may sound similar to the data analysis process in data mining, but note that data analysis is the goal of data Exploration, not part of its process. What's more, the data is analyzed differently. One example is that e-store owners extract product details from eCommerce websites like Amazon to monitor competitors' strategies.

Use Cases of Data Exploration

Data Exploration has been widely used in multiple industries serving different purposes. Besides monitoring prices in eCommerce, data Exploration can help in individual paper research, news aggregation, marketing, real estate, travel and tourism, consulting, finance, and many more.

• Lead generation: Companies can extract data from directories like Yelp, Crunchbase, and Yellowpages and generate leads for business development. You can check out this video to see how to extract data from Yellowpages with a web scraping template.
• Content & news aggregation: Content aggregation websites can get regular data feeds from multiple sources and keep their sites fresh and up-to-date.
• Sentiment analysis: After extracting the online reviews/comments/feedback from social media websites like Instagram and Twitter, people can analyze the underlying attitudes and understand how they perceive a brand, product, or phenomenon.

What is Data Mining?

Data mining could be called a subset of Data Analysis. It explores and analyzes huge knowledge to find important patterns and rules.

Data mining could also be a systematic and successive method of identifying and discovering hidden patterns and data throughout a big dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence.

What Can Data Mining Do?

Data mining tools can sweep through the databases and identify hidden patterns efficiently by automating the mining process. For businesses, data mining is often used to discover patterns and relationships in data to help make optimal business decisions.

Use Cases of Data Mining

After data mining became widespread in the 1990s, companies in various industries - including retail, finance, healthcare, transportation, telecommunication, E-commerce, etc., started to use data mining techniques to generate insights from data. Data mining can help segment customers, detect fraud, forecast sales, etc. Specific uses of data mining include:

• Customer segmentation: Through mining customer data and identifying the characteristics of target customers, companies can align them into a distinct group and provide special offers that cater to their needs.
• Market basket analysis: This is a technique based on the theory that you are likely to buy another group of products if you buy a certain group of products. One famous example is that when fathers buy diapers for their infants, they tend to buy beers together with the diapers.
• Forecasting sales: It may sound similar to market basket analysis, but data mining is used to predict when a customer will buy a product again in the future. For instance, a coach buys a bucket of protein powder that should last 9 months. The store that sold the protein powder would plan to release new protein powder 9 months later so that the coach would buy it again.
• Detecting frauds: Data mining aids in building models to detect fraud. By collecting samples of fraudulent and non-fraudulent reports, businesses are empowered to identify which transactions are suspicious.
• Discover patterns in manufacturing: In the manufacturing industry, data mining is used to help design systems by uncovering the relationships between product architecture, portfolio, and customer needs. It can also predict future product development time and costs.

Difference between Data Exploration and Data Mining

There are two primary methods for extracting data from disparate sources in data science: data exploration and data mining. Data Exploration can be part of data mining, where the aim is to collect and integrate data from different sources. As a relatively complex process, data mining comes as discovering patterns to make sense of data and predict the future. Both require different skill sets and expertise, yet the increasing popularity of non-coding data Exploration tools and data mining tools greatly enhances productivity and makes people's lives much easier.