What is data mining (and why does it matter)?

Companies are gathering more data than ever, and tech firms are constantly finding new ways to store and explore the hidden patterns, trends, and competitive insights that hide beneath the surface. This activity is known as data mining, and the top companies are leveraging cutting-edge technologies and techniques to explore their business faster, more accurately, and more comprehensively. 

There are many data mining examples out there, from cumbersome spreadsheet-based processes that barely scratch the surface to fully integrated platform-driven analytics leveraging AI and machine learning to deliver limitless modeling and forecasting power. And if you’re not investing in best-of-class solutions you’re probably already behind the competition. 

What is data mining?

Data mining is the process of analyzing large datasets to discover patterns, trends, and insights. It involves techniques from statistics, machine learning, and database systems to extract valuable information from vast amounts of data – data that would otherwise be too overwhelming for a human to analyze manually. 

There are many business models that generate extreme amounts of data from devices, apps, social media platforms, complex billing systems, web traffic, and a host of other sources. It may be collected periodically or in real-time and stored in ERPs, data lakes and warehouses, local or third-party systems, and Cloud storage. From there, it can be cleansed, reconciled, organized, and analyzed by data experts and business intelligence (BI) platforms. 

Benefits of data mining

Data mining is often resource-heavy and requires a lot of processing power. Businesses have to invest in data collection, storage, and maintenance to give their CFOs and analysts the raw material they need to explore the inner workings of their company and reveal valuable insights in a timely fashion. They also must find ways to minimize siloed systems and manual processes that slow down data mining techniques and pursue solutions that increase visibility and allow leaders to drill down into the numbers. 

It’s not cheap, but data mining has the potential to reveal game-changing insights that unlock growth opportunities and deliver an incredible return on the investment. Here are some of the key benefits that data mining can provide. 

Uncovering hidden patterns

Data mining can reveal trends, customer habits, inefficiencies, and potential revenue sources otherwise invisible or inaccessible to businesses relying on manual analytical strategies. Data mining platforms, integrations, and tools give data analysts and leaders the ability to slice, dice, drill through, model, and forecast every aspect of their business on any level of granularity.

Improving decision making

By carefully defining and designing data mining projects, leadership is equipped with the best and most complete view of their business when decisions are made. Ideally, reporting will be able to incorporate enterprise-wide data streams in real time to provide instant insights that can be leveraged as rapidly as possible. 

Gaining competitive advantage

The information data mining reveals can often deliver competitive advantages – especially when industry peers are unaware these opportunities exist. Reports may reveal a wide range of valuable insights into new customer segments, supply chain issues, and revenue leakage. They can also predict major shifts in market dynamics, reveal emerging opportunities, highlight inefficiencies, and steer leaders away from risky propositions. 

Data mining techniques

There are several ways analysts can use data mining to explore and model aspects of their business. Depending on the challenge at hand, they may use one or several of the following data mining techniques to interrogate relevant data sources through predictive modeling, machine learning, and business intelligence tools like Power BI.

Classification

Classification involves separating data into categories so it can be more easily interpreted and analyzed. By using machine learning to organize and classify existing data sets, models can be trained to label and sort future data sets, streamline storage, and optimize analytical processes. 

Clustering

Data mining can uncover hidden patterns by clustering similar or related data sets, even if they aren’t currently classified. This data mining technique is especially powerful for analyzing large, uncategorized storage systems like data lakes, and machine learning can train systems to identify and cluster future data sets as well. 

Association

Association is the process of creating and applying rules about how data sets are related to each other so they can be organized for analytical purposes. By understanding where, why, and how concurrent factors manifest, analysts can identify trends and explore deeper dynamics and market forces. 

Industry applications

There are many practical ways companies can use data mining techniques to solve current challenges and leverage emerging trends. The following data mining examples are some of the most powerful ways these systems are transforming business. 

• Security. Data mining can comb through vast amounts of data to uncover fraudulent transactions and illegal activities.
• Supply Chain. By interrogating factors that impact the flow of goods and services, businesses can streamline their activities, optimize orders, and explore new pathways in their order-to-cash cycle. 
• Social Media produces a wealth of information about customer preferences, feedback, and community trends. Data mining can help unpack these subjective and objective data points to create actionable insights for decision-makers. 
• Record Keeping. Customer profiles are constantly updated with information from their devices, online habits, and transaction histories. Data mining techniques can be used to organize all these streams and explore hidden patterns that inform sales, marketing, and product development decisions.
• Reporting. Many businesses rely on platforms like NetSuite reporting to power their BI tools. By optimizing and extending these systems as much as possible, data mining can help CFOs, IT specialists, and data analysts rapidly generate the most accurate and comprehensive business view available. 

Optimizing your data mining capabilities

Whether you’re in finance, healthcare, subscription-based services, or any other data-heavy environment, there are many data mining examples and techniques that can revolutionize your decision-making process and give you an unbeatable edge over the competition. Of course, to be effective, ERPs and business intelligence platforms like Power BI need to have access to all the data your business has at its disposal. Maintaining enterprise-wide data systems is the first step in giving leaders complete and intuitive visibility into your business. 

‍Smart integrations that extend your ERP’s native capabilities to generate instant insights and unlock hidden trends are a key part of optimizing your data mining strategy. With tools that deliver unprecedented speed, accuracy, transparency, and predictive modeling solutions, you’ll be able to leverage data mining to its fullest potential to drive revenue and grow your business. 

FAQs

What do you mean by data mining?

Data mining is the process of analyzing large datasets to discover hidden patterns, trends, and insights.

What are the key types of data mining?

Classification, clustering, and association are three of the most common data mining techniques for organizing and interrogating large data sets in lakes, warehouses, and cloud storage.  

How does data mining work?

Data mining uses machine learning, AI, and analytical systems to extract insights and hidden patterns from your business’s data storage and streams.  

What are the advantages and disadvantages of data mining?

Data mining can reveal trends and actionable insights that leaders can use to make decisions and gain competitive advantages in the marketplace. However, the value of the results can depend heavily on the speed, accuracy, and volume of data that’s consumed.