Data Mining vs. Machine Learning- 4 Key Differences
Business expansion relies on actionable insights that uncover ways to improve processes and performance through extensive research. Oftentimes these insights can be gathered by conducting analytics such as data mining and machine learning. While these two techniques are related, there are key differences in how it gathers, reports, and adapts to evolving information.
Therefore, organizations should understand the methodologies behind data mining vs machine learning, as well as their individual capabilities to decide which process would best suit their objectives.
What is Data Mining?
Data mining is an analytics strategy used to filter through data warehouses to discover previously unrecognized patterns, discrepancies, and relationships between components. By using software and algorithms, this method can automize information extraction and consolidation.
Data sets could include structured statistics, quantitative figures, and qualitative information relevant to internal processes. The data mining technique enables businesses to access enlightening insights within their existing systems that would have otherwise gone unknown.
For example, a business with multiple inventory warehouses possesses several systems, such as inventory tracking and point of sale software, that translate data into inventory turnover and replenishments metrics. However, the volume of this information can quickly become overwhelming for employees regulating the data manually.
With automated data mining, the business would be able to discover unknown relationships between specific items, sales, profit margins, and inventory costs. Management can then convert these metrics into actionable insights to optimize supply chain operations, minimizing expenses, and boosting profits.
What is Machine Learning?
Machine learning is a sub-category of artificial intelligence (AI), which uses computer analyses to sift through extensive data sets for relevant trends. The machine learning tool will then learn these patterns to forecast new information. This advanced solution has sophisticated programming allowing it to function entirely without human interaction.
In other words, machine learning teaches computers to intercept and learn from data as humans would but without the risk of human error. This tool can recognize business successes and failures, understand what needs improvement, and generate insights.
Machine learning progresses with time; therefore, the more experience the device gets, the better it will perform. This makes machine learning particularly useful for predictive analytics, where historical data and patterns are used to predict future outcomes.
Data Mining vs. Machine Learning - 4 Key Differences
Although the mining process and machine learning are both data analytics methods, there are key elements that differentiate the two, including-
1. Data Usage
A significant difference between machine learning and data mining is how and when they are used to extract data. However, the machine learning technique often uses the mining process to collect information.
Data mining is highly versatile and can be used in a variety of projects, fields, and industries, including financial research. Investors often use the mining process to scour the internet for big data on start-ups, investment opportunities, and other useful information to determine who they want to fund.
Businesses can also use the mining technique to gather and cross-examine raw data on sales and marketing trends. Besides monitoring internal metrics, data mining can sift through external websites such as social media, review sites, and digital assets to gain insights into customer perception and potential sales leads.
On the other hand, machine learning has higher functionality than data mining as it can perform unsupervised learning. This tool can automatically establish relationships between data and quickly alter its algorithms to tailor to the new trends.
For example, machine learning is the technology behind self-driving cars that can instantly react to the surrounding environment. Netflix also uses this technique to recommend new shows and movies to viewers based on their watch history.
2. Learning Foundation
Another critical difference between the two solutions is how they learn from a data foundation. While data mining and machine learning may pull from the same data warehouse, they have different plans of action once the information is extracted.
Data scientists often use the mining technique to pull data from existing information and create connections. With the discovered trends, data scientists can then develop actionable insights to aid in the decision-making process. Retailers can also use this method to track the most popular items and discover which products were returned the most to determine the next season's floor layout.
However, machine learning can not only learn from existing information but continuously adapt from real-time data as well. This service can quickly alter algorithms and patterns based on new information, while data mining is only used as a tool for machine learning to reference.
3. Pattern Recognition
Once the data is gathered, the solutions are responsible for recognizing actionable trends. Pattern recognition is the most crucial part of data mining and machine learning as businesses rely on this analysis to make evidence-based decisions.
While data mining can discover patterns and relationships between data sets, the solution relies heavily on human analysis for detailed examination. Therefore, once the mining tool generates relevant trends, analysts must convert the metrics into reports and actionable insights manually or through the use of additional tools.
As a more advanced solution, machine learning can take the algorithms used by data mining processes, make alterations, and conduct further investigation. This functionality enables machine learning to continuously incorporate new information to transform patterns, whereas data mining relies on unchanging algorithms.
While data mining can recognize broad relations between data sets to establish trends, its organizational skills limit how many detailed connections it can make. This limitation can overlook small but critical patterns that could significantly reduce a business's data analysis accuracy.
However, the advanced ever-changing algorithms within machine learning ensure all new information is considered when generating reports. While historical data is still referenced, the solution prioritizes incorporating further information to provide the most accurate and up to date data analytics. This allows organizations to take advantage of predictive analytics and focus on promoting business expansion.
Data analysis can be used in any business to promote informed decision-making and process optimization. However, as organizations continue to grow, they must evaluate what analytics tools they should implement to enhance performance and further expansion. With the necessary solutions, companies can optimize internal operations and data management.
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