Data mining has opened a universe of possibilities for business. This field of computational statistics compares many isolated pieces of knowledge and is employed by companies to detect and predict consumer behavior. Its objective is to get new market opportunities.


Data mining is an automatic or semi-automatic technical process that analyses large amounts of scattered information to form sense of it and switch it into knowledge. it’s for anomalies, patterns or correlations among many 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. During this context, data processing may be a strategic practice considered important by almost 80% of organizations that apply business intelligence, consistent with Forbes.Thanks to the joint action of analytics and data processing, which mixes statistics, AI and automatic learning, companies, can create models to get connections between many records. a number of the chances of knowledge mining include:

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



The predictive capacity of knowledge mining has changed the planning of business strategies. Now, you’ll understand this to anticipate the longer term. These are some samples of data processing in current industry.

Marketing : Data mining is employed to explore increasingly large databases and to enhance market segmentation. By analyzing the relationships between parameters like customer age, gender, tastes, etc., it’s possible to guess their behavior so as to direct personalized loyalty campaigns. data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them supported their searches, or what a list should include to realize a better response rate.

Retail : Supermarkets, for instance, use joint purchasing patterns to spot product associations and choose the way to place them within the aisles and on the shelves. Data mining also detects which offers most valued by customers or increase sales at the checkout queue.

Banking : Banks use data processing to raised understand market risks. it’s normally applied to credit ratings to intelligent anti-fraud systems to analyze transactions, card transactions, purchasing patterns and customer financial data. Data mining also allows banks to find out more about our online preferences or habits to optimize 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, like medical records, physical examinations, and treatment patterns, allows simpler treatments to be prescribed. It also enables simpler, 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 also are advantages of using data processing in medicine.

Television and radio : There are networks that apply real time data mining to live their online television (IPTV) and radio audiences. These systems collect and analyze, on the fly, anonymous information from channel views, broadcasts and programming. Data mining allows networks to form personalized recommendations to radio listeners and television viewers, also get to understand their interests and activities in real time and better understand their behavior. Networks also gain valuable knowledge for his or her advertisers, who use this data to focus on their potential customers more accurately.


Today, data search, analysis and management are markets with huge employment opportunities. data mining professionals work with databases to gauge information and discard any information that’s not useful or reliable. This needs knowledge of huge data, computing and knowledge analysis, and therefore the ability to handle differing types of software.

  • Data mining
  • Identifies and extracts relevant information from large sets of data.
  • Uses different techniques supported statistics and AI .
  • Delivers specific and concrete results.
  • Creates predictive, classification or segmentation models.
  • Transforms information into knowledge.
  • Big data
  • Refers to the gathering and storage of huge amounts of knowledge.
  • Due to the volume it’s impossible to process it with conventional software.
  • Special tools are needed to capture, manage and process the knowledge.

These data groups have a reduced volume of data to form predictions. The quality of the knowledge can vary considerably and affect the results of the analysis.

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