Data mining and business intelligence have become hallmarks of success for competitive organizations in the 21st century. For many managers and C-suite leaders, though, these terms feel ambiguous at best.
In this post, you’ll learn more about data mining and business intelligence as concepts. Then, you’ll get a closer look at how real-life businesses use the technology to make data-driven decisions that improve efficiency, lower costs, and increase sales.
Table of Contents:
What Is Data Mining?
Without getting too complicated, 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
That’s a surface-level definition of data mining. For a deeper dive, check out An In-Depth Explanation of Data Mining. The article will teach you about the details of data mining processes, techniques, and strategies. It also covers common uses of data mining, such as:
- Understanding the public’s opinion of your brand
- Assessing risk
- Developing highly targeted advertising and marketing projects
- Identifying financial fraud before it becomes a significant problem
What Is Business Intelligence?
Business intelligence 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, Xplenty integrates with all of these apps easily.
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How Data Mining and BI is 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.
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 Xplenty’s ETL solution. Xplenty 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.
Xplenty 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.
Xplenty 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
Xplenty makes it easier for companies to keep up with all of these emerging trends in data mining and business intelligence. Start your free Xplenty trial today to learn more about how a no-code, graphics-based ETL solution makes it easier for any organization to get the most out of its data.