DATA MINING: DEFINITION, EXAMPLES AND APPLICATIONS

Discover how data mining will predict our behaviour

#informatics #business

Data mining has opened a world of possibilities for business. This field of computational statistics compares millions of isolated pieces of data and is used by companies to detect and predict consumer behaviour. Its objective is to generate new market opportunities.

WHAT IS DATA MINING?

Data mining is an automatic or semi-automatic technical process that analyses large amounts of scattered information to make sense of it and turn it into knowledge. It looks for anomalies, patterns or correlations among millions of records to predict results, as indicated by the SAS Institute, a world leader in business analytics.

In the meantime, information continues to grow and grow. A 2017 research on big data reveals that 90% of world data is from after 2014 and its volume doubles every 1.2 years. In this context, data mining is a strategic practice considered important by almost 80% of organisations that apply business intelligence, according to Forbes.

Thanks to the joint action of analytics and data mining, which combines statistics, Artificial Intelligence and automatic learning, companies can create models to discover connections between millions of records. Some of the possibilities of data mining include:

  • To clean data of noise and repetitions.
  • Extract the relevant information and use it to evaluate possible results.
  • Make better and faster business decisions.

EXAMPLES OF DATA MINING APPLICATIONS

The predictive capacity of data mining has changed the design of business strategies. Now, you can understand the present to anticipate the future. These are some examples of data mining in current industry.

  • Marketing. Data mining is used to explore increasingly large databases and to improve market segmentation. By analysing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behaviour in order to direct personalised loyalty campaigns. Data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them based on their searches, or what a mailing list should include to achieve a higher response rate.
  • Retail. Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also detects which offers are most valued by customers or increase sales at the checkout queue.
  • Banking. Banks use data mining to better understand market risks. It is commonly applied to credit ratings and to intelligent anti-fraud systems to analyse transactions, card transactions, purchasing patterns and customer financial data. Data mining also allows banks to learn more about our online preferences or habits to optimise the return on their marketing campaigns, study the performance of sales channels or manage regulatory compliance obligations.
  • Medicine. Data mining enables more accurate diagnostics. Having all of the patient's information, such as medical records, physical examinations, and treatment patterns, allows more effective treatments to be prescribed. It also enables more effective, efficient and cost-effective management of health resources by identifying risks, predicting illnesses in certain segments of the population or forecasting the length of hospital admission. Detecting fraud and irregularities, and strengthening ties with patients with an enhanced knowledge of their needs are also advantages of using data mining in medicine.
  • Television and radio. There are networks that apply real time data mining to measure their online television (IPTV) and radio audiences. These systems collect and analyse, on the fly, anonymous information from channel views, broadcasts and programming. Data mining allows networks to make personalised recommendations to radio listeners and TV viewers, as well as get to know their interests and activities in real time and better understand their behaviour. Networks also gain valuable knowledge for their advertisers, who use this data to target their potential customers more accurately.

DATA MINING: A PROFESSION OF THE FUTURE

Today, data search, analysis and management are markets with enormous employment opportunities. Data mining professionals work with databases to evaluate information and discard any information that is not useful or reliable. This requires knowledge of big data, computing and information analysis, and the ability to handle different types of software.

LinkedIn's 2017 annual report on emerging jobs noted that three of the most in-demand jobs in the United States were positions related to big data. Likewise, IBM forecasts that the demand for this type of professionals will grow by 28% between now and 2020.

Data mining vs. big data.#RRSSData mining vs. big data.

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Discover the differences between data mining and big data
 

A lot of people confuse them, but they are different fields. These are the main differences...

Big data

  • Refers to the collection and storage of large amounts of data.
  • Due to the volume it is impossible to process it with conventional software.
  • Special tools are needed to capture, manage and process the information.
  • These data groups have a reduced volume of information to make predictions.
  • The quality of the information can vary considerably and affect the result of the analysis.

Data mining

  • Identifies and extracts relevant information from large sets of data.
  • Uses different techniques based on statistics and Artificial Intelligence.
  • Delivers specific and concrete results.
  • Creates predictive, classification or segmentation models.
  • Transforms information into knowledge.

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