Data Mining: What Is It and Why Does Business Need It?

Over the past five years, the business world has been undergoing dramatic changes. Staying competitive and one step ahead of the game is vital for businesses in the modern era. Businesses are constantly on the lookout for any competitive advantage that would allow them to deliver services or goods at a lower price, higher quality, and faster than competitors. The path to success starts with collecting and leveraging high-quality data.

Data is the main driver of the modern business world. It plays an important role in manufacturing, retail, healthcare, customer experience, and more. Backed by data, businesses can make processes more efficient, lower production costs, plan more effective marketing campaigns, and grow profit margins. But data alone isn’t enough for revealing the secrets of business success. Data is useless if the wrong tools are used to interact with it as they will fail to extract meaningful insights. 

Data mining leverages advanced analytics tools and techniques to make use of collected data sets. There are many ways businesses can benefit from incorporating data mining into their workflows. 

What Is Data Mining?

Data mining is the process of sorting large volumes of data to identify patterns and relationships that can help solve particular business problems through data analysis. During the process of data mining, the software is actively pulling out information from the collected data and comparing it against other data. The data sets can then be used to help businesses make better-informed decisions. In turn, companies can build better marketing strategies, increase sales, find the right market fit, and so on. 

The terms machine learning, data analysis, and data mining are often confused. Though they all deal with data, these terms are different and unique. Machine learning and data mining make use of data analytics and patterns. But while data mining finds existing data patterns, machine learning predicts future trends based on the analyzed data. 

Data mining requires human intervention and decision-making whereas machine learning only needs a human to initiate its process as it continues learning on its own. These are the key differences between machine learning and data mining. In some cases, machine learning can be utilized in data mining to automate some of the processes. 

Data analysis and data mining aren’t synonymous either. Data analysis leverages data mining to collect the necessary information. Data mining is about extracting patterns from raw data sets. Data analytics then uses that data to reveal important insights. Data analysis aims to discover useful data, inform the decision-making process, and lead to more lucrative outcomes. 

According to a 2020 report by MicroStrategy, 94% of companies believe that analytics and data are essential for their future growth and digital transformations. A company can mine its data to improve many aspects of business, including the sales and customer experience. Different industries derive different benefits from their data mining. Some focus on attracting new customers, others are looking for better marketing strategies, and some work towards improving the quality of their products. The process of data mining teaches businesses how to adjust their product, analyze the new information, and continue moving forward.  

Data mining, machine learning, data analysis, and artificial intelligence are combined in business intelligence processes that help companies learn more about their customers and make well-informed decisions. 

How Does Data Mining Work?

Data mining can be performed by skilled data scientists. According to the U.S. Bureau of Labor Statistics, the demand for computer and information research scientists (data scientists) is expected to grow by 15 percent. 

Machine learning and statistical analysis are the core elements of data mining. Thanks to ML and AI-driven algorithms, a vast majority of tedious processes have been automated. These algorithms have made it easier to mine and process large data sets like transaction records, log files from the web, customer databases, sensors, and others. In fact, companies generate tons of data every day. IT Chronicles notes that all industries generate around 2,000,000,000,000,000,000 bytes of data a day. It’s almost impossible to estimate just how much time data scientists would need to process such volumes of data manually. 

Four stages of data mining:

  1. Acquisition. Data scientists identify the relevant data for further data mining processes and move it to a data lake. 
  2. Cleaning. Before the data can be mined, it needs to be prepared. Data cleansing fixes errors and improves data quality. 
  3. Mining. Once the data is cleared, a data scientist can start the process of data mining. 
  4. Analysis and interpretation. The data mining results are reused for creating analytical models to make use of the collected data. 

Benefits of Data Mining for Business 

Today’s world is data-centric. Information is the new gold rush and data mining provides us with all the means to resolve issues and problems in the informational age. It gives us the ability to uncover hidden patterns, abnormalities, trends, and correlations in data sets. Here are other benefits of data mining:

  • Faster data analysis. Data mining helps data scientists analyze large volumes of data quickly and detect all of the hidden patterns invisible to the human eye. 
  • Improved risk management. Thanks to mined data, businesses can make data-driven decisions and better assess legal, cybersecurity, and financial risks. 
  • More effective sales. Since data mining reveals so many valuable insights into customer behavior and preferences, salespeople can fine-tune marketing and advertising campaigns and improve lead conversion rates.
  • Saved costs. Data mining helps bring down operational costs through reduced waste and redundancy in corporate spending. 
  • Better customer experience. Companies can learn more about their customers and identify customer service issues faster. Thus, contact center agents can spend less time identifying an issue and more time providing better services while boosting customer loyalty. 

Take Advantage of Data Mining to Power Up Your Business

Businesses can greatly benefit from data mining and analytics. In today’s data-centric world, properly collected and analyzed data can reveal new opportunities and business horizons. It’s time to turn your collected data into insightful reports and move your business in the right direction. What’s more, the insights can prove significant in the long run and lead your business to new levels of success.