In the ever changing world of business, data is becoming increasingly more important. As companies become more reliant on their data than ever before, organizations must become aware of strategies to get the most out of their data. Two concepts that will help be sure to help companies get the most out of their data and provide a competitive edge against other organizations are data mining and business intelligence. 

While the concepts of data mining and business intelligence have become hallmarks of success for many companies over the last couple of decades, the concepts are still quite challenging to master when just getting started. Luckily, is here to help you better understand the concepts of data mining and business intelligence while also providing real-world cases of these concepts in action. 

Read on to learn more about data mining, business intelligence, real-life business use of these technologies, and how can help you get the most out of your experience with both data mining and business intelligence to make more data-driven decisions.


Table of Contents 

Understanding Data Mining

At its most basic level, data mining involves:

  • Sifting through large amounts of data to find the information that matters to a specific issue
  • Discovering statistical relationships between groups of data.
  • Using artificial intelligence and machine learning to identify meaningful data

Of course, that’s a surface-level definition of data mining. With a deeper look, it’s easy to see that data mining is an incredibly complex and powerful technology. Countless companies around the world are already utilizing data mining to improve the experiences of their users and build better products.  To better understand data mining, check out the five stages of data mining below. 

The 5 Stages of Data Mining 

The data mining process involves the five following stages.

  • Understanding the goals of data mining project. 
  • Understanding where your data comes from.
  • Preparing your data with ETL. 
  • Analyzing, mining, and modeling the data. 
  • Reviewing and sharing the findings across the organization.  

Common Data Mining Techniques 

The following are some of the most widely used data mining techniques for business intelligence. 

  • Classification Analysis 
  • Association Rule Learning 
  • Regression Analysis 
  • Clustering 
  • Outlier Detection 
  • Time Series Forecasting 
  • Decision Trees 
  • Neural Networks 
  • Visualization
  • Sequential Pattern Mining 

Related Reading: What Is Data Mining?

Understanding Business Intelligence or BI?

Business intelligence or BI is a catch-all phrase used to describe methods that companies use to gain insights from data. It’s the second step in making data-driven business decisions. Once you mine data, you use business intelligence tools, such as apps that generate graphs from information in your database, to gain a better understanding of your information.

There’s a lot of crossover between data mining and business intelligence, especially when you use an ETL tool to reformat data before loading it to your BI applications.

Business intelligence strategies often involve:

  • Making a BI roadmap that includes your analytics needs, industry KPI, and custom KPIs specific to your organization
  • Building a BI team that may include data scientists, data analysts, developers, and head of BI
  • Organizing your core data, peripheral data, and external data
  • Choosing applications that turn your raw data into useful insights

You have a wealth of BI apps to consider. Some favorites of ours include:

As your best options, integrates with all of these apps easily.

Related Reading: Top 17 Business Intelligence Tools Of 2021

How Data Mining and BI Are Used Per Industry

Before looking at ways specific companies use data mining and business intelligence, let’s cover some general options used across industries. Having an overview should make it easier to see the real benefits when you take a closer look at how companies really harness these technologies.

Retail and E-Commerce

Think about how Amazon and other e-commerce platforms always seem to know what you want to buy. The retail and e-commerce industries need to spot emerging trends so they can keep the right items in stock. Data mining and business intelligence can also reveal behavioral trends in current and potential customers. Once you understand your customers, you can increase sales by suggesting products that will interest them. 

Marketing and Social Media

These days, you can’t create an effective marketing or social media engagement strategy without leveraging insights from data. Business intelligence can tell you things like:

  • Which messages motivate specific demographics
  • Which platforms and ads give you the best ROI
  • Where you should dedicate most of your time and money to get the most conversions

Marketing professionals have never had the advantages of today’s data mining. As long as you know how to analyze the data, you can make choices that lead to better outreach.


Data mining isn’t all about making good business decisions. It also plays a crucial role in science. The pharmaceutical industry uses data mining and data analysis to:

  • Run simulations before administering new drugs to test subjects
  • Identify new compounds that might benefit people living with certain health conditions
  • Discover infrequent side effects that patients need to know

Data mining can save pharmaceutical companies a lot of money by helping them focus on developing medications that yield the intended results.


The finance industry needs reliable ways to measure risk and predict trends. Nothing does this better than combining data mining with exceptional analytics. While it’s impossible to predict the future, data mining makes it easier for the finance industry to determine investment risks and estimate ROIs. The technology can also help lenders determine whether they should loan money to individuals and organizations.


Data mining gives the telecommunication industry insight into how they can segment customers, streamline processes, and make data more efficient. The results can influence how people use their smartphones to access apps and online content. The industry can also use data analytics to see how customers prefer using products and services.


Most restaurants have profit margins under 5%. The industry relies on data to control costs, improve supply chains, and schedule employees. Data mining and predictive analytics can help restaurants make big improvements, such as discovering more efficient ways to move products from farms to kitchens. Technology also makes it easier for restaurants to predict how many employees they need at any given time.

Related Reading: 8 Business Intelligence Blogs You Need to Read

3 Real-World Data Mining & Business Intelligence Cases

Keeping these generalities in mind, it’s time to take a closer look at how specific businesses have benefited from data mining and business intelligence.

Feedvisor Uses Data to Serve Its Retail Clients Better

Feedvisor works with retail companies to give them actionable insights that help them improve their inventory management, pricing, advertising, and other crucial operational factors.

Feedvisor can provide insights because it uses machine learning and algorithms to analyze their clients’ data. The company improved its services by adopting’s ETL solution. makes it possible for Feedvisor to pull data from multiple sources, reformat the information, and load it to analytics software.

When Feedvisor incorporated ETL into its data mining and business analytics strategies, it was able to:

  • Get alerts by pulling information from S3 buckets and Redshift and loading it into Salesforce
  • Standardize and transform their data formats so they can use the best features from Salesforce and Totango
  • Improve their predictive accuracy by pulling data from Salesforce, transforming the information into more useful segments, and putting the processed data back into Salesforce.

Now, Feedvisor’s clients have better insights into how they can reach their target markets and improve customer satisfaction.

Penneo Used Business Intelligence to Understand Its Clients and Billing

Penneo is a computer software company in Denmark that gives clients efficient ways to manage and sign documents. Penneo’s solution to signing documents made it much easier for businesses to close deals. As a result, Penneo’s list of clients grew quickly.

As more clients flocked to Penneo, the company knew that it had to leverage the benefits of business intelligence. It also wanted to find insights by mining data from its CRM and ERP solutions. Perhaps most importantly, the company needed a better understanding of its clients’ behaviors and how it could use this knowledge to increase revenues.

Penneo had its data stored in a variety of systems. A client’s bill could vary depending on which system the company used. It needed a way to standardize the process and ensure money didn’t slip through the cracks. made it possible for Penneo to centralize data from multiple sources. Standardizing data from multiple sources gave Penneo more opportunities to leverage BI apps to continue expanding its client base and get paid for all of the features users wanted.

Brunner Uses BI to Streamline Its Processes and Exceed Client Expectations

Brunner is a marketing company that employs about 120 people at its offices in Pittsburgh and Atlanta. The company started as a small business that offered creative, boutique marketing strategies. It did such a good job that it started attracting big clients like PNC Bank, Home Depot, and Dick’s Sporting Goods.

As the marketing firm grew, data mining became more integral to its continued success. The team didn’t need much technology when it developed campaigns for small clients. Now that it worked for international corporations, it needed to access data and learn how they could benefit from analytics.

Unfortunately, Brunner didn’t have a single source of data. Diversity within databases and data formats meant that the team had to spend a lot of time just figuring out which pieces of data mattered to individual campaigns. gives Brunner a straightforward way to collect and standardize data. Now, the marketing firm can accurately analyze data to determine which approaches work best for its clients. It doesn’t have to make guesses anymore. It can generate graphs and reports that show clients how they’ve benefited from Brunner’s expertise.

The Future of Data Mining and Business Intelligence

Businesses can’t thrive without reliable data mining and business intelligence options in the modern world, and the importance of data and analytics will only become more important over the next few decades.

Companies at the forefront of data know that tools will evolve to unleash even more of information’s potential. The near future of data mining and business intelligence will include:

  • Artificial intelligence that can perform research automatically
  • Self-service business intelligence that gives companies more opportunities to learn from data without consulting with external data scientists
  • Data retention that will help companies learn more from customer interactions
  • Data governance that will require more robust security standards to protect businesses and consumers

How Can Help

If you’re looking for the right tools to conquer the concepts of data mining and business intelligence, then the platform is ready to help you in your endeavors. With a complete toolkit for building ETL data pipelines and providing seamless integration solutions, the platform will be there to help you with all your data mining and business intelligence needs. Ultimately, will help you transform the difficult task of data management into a simple one.   

Are you ready to discover more about the many benefits the platform can provide to your organization?  Contact our team today to schedule a 14-day demo or pilot and see how we can help you reach your goals.