Data Mining is different from Machine Learning
Today in this blog we are going to look at how data mining is different when it is compared with Machine Learning (with a main focus on Data Mining.).
We are now in a data-driven era where businesses glimpse new technical terms and concepts. Now that more businesses are adapting to AI and ML, there are lots of possibilities for Big Data and Data Analytics to show wonders. Data is a crucial tool; however the more data available, the longer it takes for us and companies to get valuable insights out of them.
And This is where companies need Data mining. Data mining opens various opportunities for business since it has descriptive and predictive powers. But both data mining and machine learning fall under the general heading of data science. There is a lot of overlap when it comes to applications of data mining and machine learning which is why enterprises use them interchangeably. Now let’s take a closer look at both so we know how to catch their different ends?
What is Data Mining?
Data mining extracts critical information from a massive amount of data. People use data mining techniques to discover new, accurate, and functional patterns in the data to find the meaning and information.
As the process suggests, data mining is one efficient way to resolve complicated data challenges. Now, how else is data mining beneficial to your business?
Benefits of Data Mining in Business
It’s crucial to ascertain as many benefits from data as possible in today’s competitive business world. Here’s a broad range of benefits data mining offers to a business.
- Data mining is a cost-efficient solution as compared to traditional data approaches such as BI tools and software systems.
- Businesses also quickly initiate automated trends and behavior and make informed decisions based on rich data.
- It helps companies gather reliable information.
- It helps businesses make profitable production and operational adjustments.
- Data mining uses both new and legacy systems.
- It helps businesses make informed decisions.
Data Mining Vs. Machine Learning
Data mining uses two components (database and machine learning) for data management and data analysis techniques. It helps extract valuable data that can provide excellent insights into a product or service.
However, machine learning only uses algorithms and possesses a self-learning capability to change rules per scenario to find the solution.
Another contrasting difference lies in the human effort, as data mining requires constant human intervention but machine learning only requires humans to define the algorithm. As machine learning is an automated process, it will work on its own to produce accurate results compared to data mining.
Data mining is limited to how data is organized and collected and acts as a means to extract relevant insights from complex datasets. Machine Learning identifies the correlations between all relevant data points to deliver accurate conclusions and ultimately shape the model’s behavior.
For example, CRM systems implement machine learning procedures to enhance their relationship intelligence to understand customers better. It can analyze past actions to boost conversions and improve customer satisfaction scores. Here’s a small comparison table to help you differentiate between data mining and machine learning better.
Data mining procedures help predict the result from historical data or find a new solution from the existing data. Machine learning overcomes the problems of data mining which is helping it to grow in a much faster way.
However, it is essential to keep the data mining process as it will define the problem of a particular business. Data mining and machine learning are required to drive a business and work together in a better way.