Understanding data mining: Techniques, applications, and tools

Introduction to data mining

The concept of data mining has been with us since long before the digital age. The idea of applying data to knowledge discovery has been around for centuries, starting with manual formulas for statistical modeling and regression analysis. In the 1930s, Alan Turing introduced the idea of a universal computing machine that could perform complex computations. This marked the rise of the electromechanical computer — and with it, the ever-expanding explosion of digital information that continues to this very day.

We’ve come a long way since then. Data has become a part of every facet of business and life. Companies today can harness data mining applications and machine learning for everything from improving their sales processes to interpreting financials for investment purposes. As a result, data scientists have become vital to organizations all over the world as companies seek to achieve bigger goals than ever before. Data scientists use data mining to extract the insights needed for meaningful business decisions.

What is data mining?

Data mining is the process of analyzing massive volumes of data to discover business intelligence that can help companies solve problems, mitigate risks, and seize new opportunities. This branch of data science derives its name from the similarities between the process of searching through large datasets for valuable information and the process of mining the earth for precious metals, stones, and ore. Both processes require sifting through tremendous amounts of raw material to find hidden value.

Data mining and knowledge discovery in databases (KDD)

Data mining can answer business questions that were traditionally impossible to answer because they were too time-consuming to resolve manually. Using powerful computers and algorithms to execute a range of statistical techniques that analyze data in different ways, users can identify patterns, trends, and relationships they might otherwise miss.

Knowledge discovery in databases (KDD) is another term for data mining that refers to identifying and extracting previously unknown but useful knowledge from large databases. These terms are used interchangeably.

Relationship between data mining, big data, and artificial intelligence

In data science, data is collected and stored in data lakes, data warehouses, and databases. This collection of data is referred to as big data. Big data has the potential to deliver important insights to data scientists that can then be used for business intelligence and better decision-making. Big data is the foundational source of data for artificial intelligence (AI). Data mining identifies and extracts particular data from big data and can be used to train AI models.

Benefits of data mining

Data mining is used in many areas of business and research, including sales and marketing, product development, healthcare, and education. When used correctly, data mining can give organizations an advantage over competitors by making it possible to learn more about customers, develop effective marketing strategies, increase revenue, and decrease costs.

Data mining gives businesses an opportunity to optimize operations by understanding the past and present to make accurate predictions about what is likely to happen next.

For example, sales and marketing teams can use data mining to predict which prospects are likely to become profitable customers. Based on past customer demographics, they can establish a profile of the type of prospect who would be most likely to respond to a specific offer. With this knowledge, they can increase return on investment (ROI) by targeting only those prospects likely to respond and become valuable customers.

Data mining can help solve almost any business problem that involves data, including:

  • Increasing revenue
  • Understanding customer segments and preferences
  • Acquiring new customers
  • Improving cross-selling and upselling
  • Retaining customers and increasing loyalty
  • Increasing ROI from marketing campaigns
  • Detecting and preventing fraud
  • Identifying credit risks
  • Monitoring operational performance

The data mining process

Any data mining project must start by establishing the business question(s) the organization is trying to answer. Without a clear focus on a meaningful business outcome, it’s easy to pore over the same set of data over and over without turning up any useful information at all. Once there is clarity on the problem that needs to be solved, it’s time to collect the right data to answer it — usually by ingesting data from multiple sources into a central data lake or data warehouse — and preparing that data for analysis.

Success in the later phases is dependent on what occurs in the earlier phases. Poor data quality will lead to poor results, which is why data miners must ensure the quality of the data they use as input for analysis.

CRISP-DM: A standard methodology for data mining projects

For a successful data mining process that delivers timely, reliable results, data scientists follow a structured, repeatable approach. The Cross Industry Standard Process for Data Mining (CRISP-DM) is a standardized process model that has six sequential phases for data mining:

  1. Business understanding. Develop a thorough understanding of the project parameters, including the current business situation, the primary business objective of the project, and the criteria for success.
  2. Data understanding and data collection. Determine the data that will be needed to solve the problem and gather it from all available sources.
  3. Data preparation. This includes cleaning the data, fixing any data quality issues such as missing or duplicate data, and transforming the data into the right format.
  4. Modeling. Use algorithms to identify patterns within the data and apply those patterns to a predictive model.
  5. Evaluation. Determine whether and how well the results delivered by a given model will help achieve the business goal. There is often an iterative phase in which the algorithm is fine-tuned in order to achieve the best result.
  6. Deployment. Run the analysis and make the results of the project available to decision-makers.

Throughout this iterative process, close collaboration between domain experts and data miners is essential to understand the significance of data mining results to the business question being explored.

Key data mining techniques and algorithms

There are several common approaches and techniques used in data mining. Some of the most common are defined as:

Regression analysis and predictive modeling

Organizations across industries are achieving transformative results from data mining.

Regression analysis is used to develop predictive modeling. It is a statistical technique to predict a range of numeric values, such as sales, temperatures, or stock prices, based on a particular dataset.

Association rule learning and market basket analysis

Association rule learning, also known as market basket analysis, looks for interesting relationships between variables in a dataset that might not be immediately apparent, such as determining which products are typically purchased together. This can be incredibly valuable for long-term planning.

Decision trees, neural networks, and deep learning

Decision trees are another method for categorizing data. This method asks a series of cascading questions to sort items in the dataset into relevant classes.

A neural networks is an assembly of nodes that works similarly to the human brain. It includes an input layer, a hidden layer, and on output layer. These networks contain layers of data.

Deep learning is comprised of several hidden layers of neural networks to perform complex operations on large amounts of structured and unstructured data.

Outlier detection and anomaly detection

Outliers are observations that are far from the mean of a distribution; they do not necessarily mean abnormal behavior. An outlier is considered an unlikely event (an improbability).

Anomalies are data patterns that are created by different processes. Anomalies cannot be explained given the base distribution. They are considered an impossibility. Detecting these in data mining is important to verify the accuracy of information and to provide a true interpretation of the data.

Machine learning algorithms

Data mining uses machine learning algorithms, which are sets of heuristics and calculations that create a model from the data. These ML algorithms are based on patterns and trends in data.

Data mining applications

Data mining has multiple applications, including, but not limited to:

  • Real-time data analytics and business intelligence
  • E-commerce and retail: personalization and marketing campaigns
  • Fraud detection in finance, cybersecurity, and other industries
  • Social media and sentiment analysis
  • Supply chain optimization and demand forecasting

Through the application of data mining techniques, decisions can be based on real business intelligence — rather than instinct or gut reactions — and deliver consistent results that keep businesses ahead of the competition.

Data mining use cases

Organizations across industries are achieving transformative results from data mining:

  • Groupon aligns marketing activities — One of Groupon’s key challenges is processing the massive volume of data it uses to provide its shopping service. Every day, the company processes more than a terabyte of raw data in real time and stores this information in various database systems. Data mining allows Groupon to align marketing activities more closely with customer preferences, analyzing that terabyte or more of customer data in real time to help the company identify trends as they emerge.
  • Air France KLM caters to customer travel preferences — The airline uses data mining techniques to create a 360-degree customer view by integrating data from trip searches, bookings, and flight operations with web, social media, call center, and airport lounge interactions. They use this deep customer insight to create personalized travel experiences.
  • Domino’s helps customers build the perfect pizza — The largest pizza company in the world collects 85,000 structured and unstructured data sources, including point of sales systems, 26 supply chain centers, and all its channels, including text messages, social media, and Amazon Echo. This level of insight has improved business performance while enabling one-to-one buying experiences across touchpoints.

These are just a few examples of how data mining capabilities can help data-driven organizations increase efficiency, streamline operations, reduce costs, and improve profitability.

Data mining software and tools

There are a variety of data mining software and tools available to help organizations get started with a data mining initiative. They may choose between open-source solutions that are typically free or paid with managed services; or commercial solutions that integrate into the organization’s systems. Here are some of the more common tools that support data mining:

  • Python libraries. Python is easy to use and has powerful modules, which makes it a useful tool for data mining and analysis. Pandas (Python data analysis) is one of the most popular used for data science and data mining.
  • Visualization tools. There are numerous visualization tools for data mining that make it easy to see and comprehend data. These include Google Charts, Tableau, Grafana, and others.
  • Data mining platforms. A data mining platform helps users analyze data from different sources. Talend is an integration platform that makes it easy to identify, connect, transform, and analyze data, fully supporting the data mining process.

Get started with data mining

As organizations continue to be inundated with massive amounts of internal and external data, they need the ability to distill that raw material down to actionable insights at the speed their business requires.

Businesses in every industry rely on Talend to help them accelerate insights from data mining. Our modern data integration platform empowers users to work smarter and faster across teams, enabling them to develop and deploy end-to-end data integration jobs ten times faster than hand coding — at fraction of the cost of other solutions.

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