Powerful Data Mining Tools, Techniques and Methods

In today’s dynamic and complex digital environment, consumers expect businesses to not only anticipate their needs but also meet them quickly and efficiently. The only way for businesses to keep up with evolving consumer expectations is to harness the powerful potential of data mining.

To learn more, check out the infographic below, created by the University of Nevada, Reno’s online Master of Science in Business Analytics program.

The Powerful Potential of Data Mining in Business

Before diving into data analytics tools, data analysts should take the time to learn about key terms and the data mining process.

Key Terms Every Data Analyst Should Know

Data mining is the process of exploring and analyzing large quantities of data to identify relevant patterns and trends. Before data analysts can begin to analyze the data, they must centralize it into one database or program through a process called warehousing. Data analysts must also clean the data by removing or fixing incorrect, corrupted, improperly formatted, duplicate or incomplete data within a dataset.

The process of changing the format, structure or values of data by performing summary operations is called data transformation. Once the data has been transformed, data analysts group similar observations into smaller groups within a larger population; this is called clustering.

Data selection is the process of retrieving data relevant for a task. Data integration, on the other hand, is the technical and business process of combining data from multiple sources to create a unified, single view of data.

The Data Mining Process

The first step in the data mining process involves setting the business objective by identifying the problem and determining what needs to be done to solve it. Next, data analysts will prepare the data and use data mining techniques to create a data model framework that will help solve the problem. They will then evaluate the results and apply their findings.

The Benefits of Data Mining

Data mining improves customer acquisition and retention by helping companies identify customer needs and meet them. It also creates highly effective targeted campaigns by delivering tailored products to a specific type of customer, and it improves risk management by helping companies identify and avoid potential risks. Data mining also supports innovation by helping companies identify lucrative opportunities.

5 Common Data Mining Techniques

Data analysts can employ a range of data mining techniques to identify relevant insights.

Need-to-Know Data Mining Techniques

Association is the process of identifying relationships among data points in a large dataset. Other data mining techniques include decision trees, which use classification or regression methods to classify or predict potential outcomes based on a set of decisions; neural networks, which mimic the interconnectivity of the human brain through layers of nodes made up of inputs, weights, a bias (or threshold) and an output; and the K-nearest neighbor (KNN) algorithm, which categorizes data points based on their associations with and closeness to other data points.

Detection of anomalies is the process of identifying unusual values in a dataset. This can be accomplished through time series data anomaly detection, a technique used to track seasonality within a dataset and identify the three types of anomalies: global outliers, contextual outliers and collective outliers.

Global outliers are data points that exist far outside the bounds of a dataset, and contextual outliers are data points that deviate from other data points that exist in the same context. Collective outliers are a subset of data points within a set that deviates from the entire dataset.

The benefits and application of anomaly detection include application performance, which involves identifying and resolving potential app performance problems before they begin to affect user experience; product quality, which involves monitoring products for behavior anomalies with every version release, A/B test, new feature, change to customer support and tweak to the sales funnel; and user experience, which involves reacting to usage lapses before they cause serious problems.

5 Modern Data Mining Tools

With so much data and so little time to analyze it, data analysts can speed up the process by using powerful data mining tools.

Top Data Mining Tools for the Modern Data Analyst

Data analysts choose RapidMiner Studio for its ability to blend structured and unstructured data, advanced visualization options, in-database processing, interactive data preparation, and process optimization. RapidMiner Studio has a robust free version that gives data analysts access to a wide range of tools and features.

Alteryx Designer allows data connectivity to more than 70 sources; extracts and cleanses data through a visual user interface to maximize value; provides access to hundreds of analytics applications; and enables the creation of repeatable, automated workflows. Benefits of this data mining tool include accessibility for users with varying levels of experience with coding, significant performance improvements and integration with larger cohesive platforms.

Sisense for Cloud Data Teams creates advanced analytics processes in any language, controls the mode and frequency of data refreshes, uses datasets to train machine learning models, and performs ad hoc analysis to explore modeled and raw data.

TIBCO Data Science is a great option for data analysts who need access to a wide range of advanced analytics functions — over 16,000. Benefits of this data analytics tool include access to insights for all users, enhanced collaboration through a messaging app and easily shared workspaces.

And finally, SAS Visual Data Mining and Machine Learning is a data mining tool that automatically recommends features for modeling, calculates supervised learning model performance statistics, and generates insights and reports.

Why Businesses Need Data Mining

Effective data mining is the key to long-term business success. It can uncover invaluable insights that empower business leaders to make informed decisions and drive growth.

Sources

Anodot, “What Is Anomaly Detection? Examining the Essentials”

Atlassian, “What Is a Knowledge Base?”

Corporate Finance Institute, “Data Mining”

Data Mining 365, “Classification in Data Mining – Various Methods in Classification”

Dimensionless, “The Concept of Cluster Analysis in Data Science”

Entrepreneur, “Five Ways Big Data Can Help Your Business Succeed”

Forbes, “Five Benefits of Big Data Analytics and How Companies Can Get Started”

GeeksforGeeks, “Association Rule”

GeeksforGeeks, “Various Terms in Data Mining”

IBM, “What Is Data Mining”

Investopedia, Data Mining

OmniSci, Data Integration

SelectHub, “The Best Data Mining Software Tools”

Statistics By Jim, “Understanding Interaction Effects in Statistics”

Stitch Data, “What Is Data Transformation: Definition, Benefits, and Uses”

Tableau, “Guide to Data Cleaning: Definition, Benefits, Components, and How to Clean Your Data”