Data mining requires a class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests.
The phrase data mining is commonly misused to describe software that presents data in new ways. True data mining software doesn’t just change the presentation but also discovers previously unknown relationships among the data.
Data mining is popular in the science and mathematical fields but also is utilized increasingly by marketers trying to distill useful consumer data from websites and application traffic. Enterprise data sets are often stored in databases or storage systems.
Data mining uses algorithms, including machine learning algorithms, to detect patterns in data sets. Examples of patterns include:
- A particular product purchased by multiple people on the same day after days of not being purchased at all. Enterprises might want to analyze why that product sold on that day — was it because of a holiday, a national emergency, or simply chance? Also, who bought the product?
- Traffic from US-based users on an enterprise network, accessing a restricted business account at 1 AM CST. Because those aren’t working hours (or waking hours), this is suspicious and unusual activity that the business would want to investigate — it could suggest stolen credentials or even a ransomware attack.
As these examples illustrate, although data mining reveals potential events, further analysis or action is needed for enterprises to draw reliable conclusions or solutions from the patterns discovered.
How does data mining benefit enterprises?
Data mining allows organizations to parse through large volumes of data (for example, petabytes in a cloud database) and make connections between data points that wouldn’t otherwise be visible. Data mining is one step in the data optimization process — the combination of methods that enterprises use to derive conclusions from their data, improve their operations, and make more revenue.
For data mining to be successful, though, data needs to be accurate and clean. In other words:
- Enterprises should keep their databases or storage systems accurate. If data is outdated or no longer true, analyzing it might lead to inaccurate conclusions for the enterprise. Buying decisions from five years ago are no longer a reliable source for sales teams that need to know what customers want now.
- Enterprises should work with their data so that it doesn’t have errors. This may also include maintaining the hardware and software from which the data comes. For example, a broken IoT temperature sensor in a factory might emit data that doesn’t reflect the actual temperature of the stored materials at the factory. Also, sectors of storage devices can fail, leading to missing or inaccurate data.
If done correctly, data mining benefits include:
- Revealed connections between seemingly disparate events in the enterprise.
- Preparation for advanced data analytics, including predictions.
- Identification of strange patterns that could mean security threats or other information missed by human observation.
Where is enterprise data mining headed? Read Current Trends and Future Scope of Data Mining.
What are some data mining techniques?
Enterprises take multiple approaches to data mining, often depending on the type of data or what the mining technique is intended to find.
Anomaly detection in data mining is used to identify potential problems or threats within a data set. Often, anomaly detection is used for cybersecurity — like detecting unusual network traffic or an access attempt for an application during the middle of the night. Anomaly detection can be supervised or unsupervised. Unsupervised detection uses only algorithms to identify anomalies, while supervised detection requires human observation and work.
Association identifies data points that appear together and analyzes the probability of their relationship. The likelihood of data correlating is connected to how often that data appears together within a selected set. Association detection is useful for sales and marketing: if data points regularly occur at the same time in purchases, for example, businesses can adjust their marketing tactics based on the data.
Classification creates groups based on data characteristics, using algorithmic decision trees to study data attributes characteristics. To properly classify, the classification model must first be trained on a data set. Classifying data is useful for sorting business data into categories: for example, deciding which stage of the sales funnel leads belong to, depending on the existing data about them.
Regression models attempt to predict data values — usually numbers — using algorithms or other mathematical equations. Regression models are useful tools for calculating potential business numbers, but they can lead to inaccurate conclusions, particularly on smaller data sets. They can visualize a false relationship between random data that isn’t actually connected.
Learn more about data mining techniques.
Data mining tools
Alteryx offers data science and machine learning for uses like forecasting, predictive analytics, and natural language processing. For forecasting, Alteryx provides comparisons so that users can see the benefits of different forecasting models; predictive analytics includes techniques like neural networks and support vector machines. Text mining uses computer vision to locate text in images like PDFs, and Alteryx’s NLP includes sentiment analysis.
To learn more about sentiment analysis applications, read Top Sentiment Analysis Tools & Software.
Qlik Sense is a data analytics platform that shows the associations between data without limitations of queries: associations are visualized, regardless of what data is queried. The advanced analytics feature allows users to make visual selections of represented data and receive answers to specific questions about that data set. Businesses are also able to schedule delivery of regular PDFs to designated recipients and have multiple delivery options.
RapidMiner offers data science and machine learning to enterprises, providing tools like data preparation and data model operations. The Auto Model tool is a quick way to design predictive models, with an automated hyperparameter feature to help users choose the best parameters for their model. RapidMiner Model Ops provides dashboards with models visualized as charts and allows integrations with other IT software.
Sisense Fusion is a platform that embeds data analytics features into native enterprise applications and allows users to design applications with branded images and labels. Sisense Fusion can be deployed in the cloud or on premises. It offers predictive analytics and natural language querying. Embedding options include iFrames, SDKs, and APIs.
Talend Data Fabric
Talend’s comprehensive data platform includes data integration, integrity and governance, and application and API integration. Data integration includes a drag-and-drop interface for connecting data sources to other software. The data quality product cleanses data through standardization, deduplication, and validation. Talend Data Fabric is focused on connecting all sources of data within an organization and reducing silos.
Planning to select a data mining tool for your enterprise? Read Best Data Mining Tools & Software.