Data Mining

Forward-thinking business leaders are using data mining techniques to acquire valuable insights about their customers and their business. They are using their information assets to obtain knowledge that can be used to inspire innovation and drive transformation. Taking advantage of the rise in affordable computing power and parallel computing, companies are leveraging powerful data analytics tools and conducting data mining to sort through and extract useful information from data—to turn their raw data into actionable insights that they can use to guide their strategies for finance, HR, marketing, management, and more. They are using analytics to better understand their customers’ needs and desires, develop more innovative products and services, discover new revenue opportunities, and even anticipate future outcomes.

Using data mining to produce knowledge and improve business performance

Data mining is used to generate new knowledge from large volumes of data. Machine learning algorithms and statistical models are used to uncover hidden or previously unknown patterns, trends, and relationships in massive and diverse data sets. Companies use analytics software or big data tools to unearth valuable information about the many forces and factors affecting their business from a wide variety of data sources, which includes data from CRM systems, web analytics tools, and mobile applications.

Practiced in a broad spectrum of industries, data mining is used by financial institutions to detect fraud and determine credit scores. It is used by investment firms to predict stock performance, by marketing agencies to segment audiences and build targeted campaigns, and in pharmaceuticals to discover new compounds for treating disease. Data mining is helping businesses of all stripes streamline and improve decision making and planning processes, detect emerging trends and opportunities, obtain competitive intelligence, and optimize their marketing, sales, and customer service initiatives.

Data analysis methods and techniques used in data mining

There are several different techniques used in data mining. Below are just a few examples of data mining techniques used to parse through large amounts of data, identify important associations and patterns, and build descriptive and predictive models:

  • Cluster analysis: Applied in descriptive analytics use cases and used to segment data into different groups such that members in the same group are similar to one another and different from members of other groups
  • Association rule learning: Applied in market-basket analysis and used to count the frequency of complimentary occurrences in a given data set, then identify rules about objects found together more often than in a random sampling
  • Classification: Applied in predictive analytics use cases and used to create classification models based on previously classified data in order to predict the target category or the class of unknown objects from similar data sources
  • Regression analysis: Applied in forecasting and risk assessment and used to mathematically describe and model the relationship between one dependent variable and a series of independent variables

Using BI and analytics tools to execute and expedite data mining

The easiest way for companies to leverage data mining is through the deployment of modern business intelligence and analytics tools. Today’s best-in-class BI and analytics solutions support all parts of the data mining process, from data exploration and data cleansing to pattern recognition and data visualization. They make it easier for business analysts and non-technical business users to load and combine data from multiple sources, remove errors and inconsistencies, and apply machine learning algorithms and other advanced statistical techniques. BI and analytics tools can also be used to create compelling visualizations and reports illustrating the insights uncovered.

Cutting-edge AI-powered business analytics tools automate the more mundane and time-consuming aspects of data mining, expediting data preparation, and accelerating time to insight. With the newest generation of BI solutions, business users do not have to depend on IT or data scientists to help them dig through data. Intuitive yet powerful toolsets let the people who know your business best make impactful discoveries in data—leading to better business decisions, which create real value from your organization’s information assets.

Data mining for business users: Qlik Sense

Qlik Sense is the flagship enterprise analytics solution from the leaders in user-driven business intelligence. Engineered to handle the scope and demand of data mining, Qlik Sense is a robust BI and analytics platform that supports users through the entire analytics process from data preparation to insights discovery. Qlik Sense features a scalable cloud architecture, a one-of-a-kind associative analytics engine, and sophisticated AI-driven Cognitive Engine, It enables users of all skill levels to load and combine data across multiple sources, derive new knowledge from that data, and tackle even the most complex analytics challenges.

Qlik Sense is surprisingly easy-to-use, allowing business users to skip the coding—to explore data, create visualizations, and build rich analytics applications all through a simple drag-and-drop interface. Qlik Sense is also smart. Qlik’s Associative Engine indexes and understands all relationships in your data, exposing valuable insights to users as they point and click. Augmented Intelligence is used to accelerate and automate data preparation tasks and offer users context-aware insight suggestions. AI-driven smart visualizations reveal to the user new perspectives on their data while natural language interaction provides them with another easy way to navigate and extract value from your organization’s information assets. Qlik Sense distributes the power of data mining to every user, helping your workforce unlock the real possibilities in your data.


What is data mining and what is it used for?

Data mining refers to the practice of employing machine learning algorithms, pattern recognition technologies, and other statistical techniques in order to discover meaningful correlations, patterns, and trends in large volumes of data. Data mining is used to extract previously unknown and potentially useful information from large data sets.

What are data mining techniques?

The most common statistical methods or techniques used for data mining include clustering, association rule learning, sequential pattern discovery, classification, regression analysis, and predictive modeling.

What are the benefits of using data mining in the enterprise?

Firms that use data mining are better able to understand their customers, anticipate demand, reduce churn, and make smarter decisions. They can also use it to improve their marketing and sales efforts, provide better customer service, manage and mitigate risk, optimize resource allocation and utilization, and achieve a competitive advantage.

How is data mining related to predictive analytics?

Data mining is used to identify patterns, regularities, and predictive factors in existing data sets and develop predictive models based on the findings. These models can then be used to forecast or determine the probability of future outcomes.

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