Data mining is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Data mining combines statistics and artificial intelligence to analyze large data sets to discover useful information. Data mining uses sophisticated mathematical algorithms to segment the data and to predict the likelihood of future events based on past events. Data mining is also known as Knowledge Discovery in Data (KDD). Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge.

• Why is Data Mining important?
• Advantages of Data Mining 
• Data Mining applications

Why is Data Mining important?

You’ve seen the staggering numbers – the volume of data produced is doubling every two years. Unstructured data alone makes up 90 percent of the digital universe. But more information does not necessarily mean more knowledge.

Data mining allows you to:

• Sift through all the chaotic and repetitive noise in your data.
• Understand what is relevant and then make good use of that information to assess likely outcomes.
• Accelerate the pace of making informed decisions.

Advantages of Data Mining 

Data is pouring into businesses in a multitude of formats at unprecedented speeds and volumes. Being a data-driven business is no longer an option; the business’ success depends on how quickly you can discover insights from big data and incorporate them into business decisions and processes, driving better actions across your enterprise. However, with so much data to manage, this can seem like an insurmountable task.

Data mining empowers businesses to optimize the future by understanding the past and present, and making accurate predictions about what is likely to happen next. 

For example, data mining can tell you which prospects are likely to become profitable customers based on past customer profiles, and which are most likely to respond to a specific offer. With this knowledge, you can increase your return on investment (ROI) by making your offer to only those prospects likely to respond and become valuable customers.

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.

As large-scale data processing technologies such as machine learning and artificial intelligence become more readily accessible, companies are now able to dig through terabytes of data in minutes or hours, rather than days or weeks, helping them innovate and grow faster.

Data Mining process

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. Data scientists describe data through their observations of patterns, associations, and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, like spam detection.

Data mining usually consists of four main steps: setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results:

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Data scientists and business stakeholders need to work together to define the business problem, which helps inform the data questions and parameters for a given project. Analysts may also need to do additional research to understand the business context appropriately.
2. Data preparation: Once the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. Once they collect the relevant data, the data will be cleaned, removing any noise, such as duplicates, missing values, and outliers. Depending on the dataset, an additional step may be taken to reduce the number of dimensions as too many features can slow down any subsequent computation. Data scientists will look to retain the most important predictors to ensure optimal accuracy within any models.

Model building and pattern mining: Depending on the type of analysis, data scientists may investigate any interesting data relationships, such as sequential patterns, association rules, or correlations. While high frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud.

Deep learning algorithms may also be applied to classify or cluster a data set depending on the available data. If the input data is labelled (i.e., supervised learning), a classification model may be used to categorize data, or alternatively, a regression may be applied to predict the likelihood of a particular assignment. If the dataset isn’t labelled (i.e., unsupervised learning), the individual data points in the training set are compared with one another to discover underlying similarities, clustering them based on those characteristics.

4. Evaluation of results and implementation of knowledge: Once the data is aggregated, the results need to be evaluated and interpreted. When finalizing results, they should be valid, novel, useful, and understandable. When this criterion is met, organizations can use this knowledge to implement new strategies, achieving their intended objectives.

Data Mining applications

Data mining techniques are widely adopted among business intelligence and data analytics teams, helping them extract knowledge for their organization and industry. Some data mining use cases include:

Telecom, Media & Technology

In an overloaded market where competition is tight, the answers are often within your consumer data. Telecom, media and technology companies can use analytic models to make sense of mountains of customers data, helping them predict customer behavior and offer highly targeted and relevant campaigns.

Insurance

With analytic know-how, insurance companies can solve complex problems concerning fraud, compliance, risk management and customer attrition. Companies have used data mining techniques to price products more effectively across business lines and find new ways to offer competitive products to their existing customer base.

Education

With unified, data-driven views of student progress, educators can predict student performance before they set foot in the classroom – and develop intervention strategies to keep them on course. Data mining helps educators access student data, predict achievement levels and pinpoint students or groups of students in need of extra attention.

Manufacturing

Aligning supply plans with demand forecasts is essential, as is early detection of problems, quality assurance and investment in brand equity. Manufacturers can predict wear of production assets and anticipate maintenance, which can maximize uptime and keep the production line on schedule.

Banking

Automated algorithms help banks understand their customer base as well as the billions of transactions at the heart of the financial system. Data mining helps financial services companies get a better view of market risks, detect fraud faster, manage regulatory compliance obligations and get optimal returns on their marketing investments.

Retail 

Large customer databases hold hidden customer insight that can help you improve relationships, optimize marketing campaigns and forecast sales. Through more accurate data models, retail companies can offer more targeted campaigns – and find the offer that makes the biggest impact on the customer.