Data Mining Definition

At a time when everything is known, Data Mining can be a growth lever for a company. Many brands have based their marketing strategy on this branch of data science. Representing more than statistics, Big data analytics is at the origin of the best predictions. This article explains everything to you.

Data Mining or data mining

Before getting to the heart of the matter, it would perhaps be worth talking about terminology. The expression Data Mining can be translated as data mining. This French version comes close to the English meaning, but remains unclear. In fact, it is more about analyzing blocks of information extracted from intelligence silos. The person who practices this profession would look more like a scientist in a white coat than a miner smeared with coal.

Applicable in all areas, Data Mining is not just reserved for IT professionals and marketing managers. Anyone can learn to analyze raw data, transforming it into useful information. It is also possible to identify trends, or even establish rules or patterns. Many companies are therefore exploring data compilations to draw conclusions and use them as a means to boost their turnover.

A set of technologies for disparate objectives

Not so recent, Data Mining has existed since humanity knew how to carry out research. However, the algorithms and computing resources currently available have greatly facilitated the task of the person responsible for analyzing bulk information. Machine learning and artificial intelligence are now in the hands of specialists. These experts can always count on applied statistics.

Each entity has its own objective with Data Mining. Some companies aim to reduce operating costs. Good knowledge of data makes it possible to better organize logistics in e-commerce. Other companies want to improve productivity with curves and graphs. There are also those who want to stay ahead of the market and anticipate consumer behavior.

This area is based on a few major elements

The evolution of Data Mining depends on that of digital technology. The appearance of databases and powerful servers facilitates access to raw information. Then, the analysis tools became efficient thanks to unimaginable calculation speed. This whole technological race is part of a whole circuit whose main functions are:

  • The data is stored in Data Warehouses, it thickens over time.
  • Data scientists extract the blocks they need from the servers.
  • Multidimensional analysis mainly concerns transactions.
  • Figures and information are summarized in tables or graphs.
  • Succinct presentations summarize weeks of data compilation.

Mining is the analysis of enormous amounts of information

Data Mining professionals use various analytical tools. These are tailor-made software and algorithms. That said, the human brain is also essential for categorizing and summarizing information. The information is mainly relational, but data mining is not limited to the marketing domain. Health, politics and many other sectors of activity will be able to benefit from Knowledge Discovery in Data.

To better understand, here are the tasks that analytical algorithms perform:

  • Association involves grouping identical information together to derive mathematical logic.
  • Sequential analysis establishes the cause and effect relationship between two events
  • Classification: the idea is to organize heterogeneous information while waiting to find a correlation between them.
  • Clustering: this is mainly market segmentation.
  • Prediction, data mining experts are the weather gentlemen of business.

Science transforming data into useful information and knowledge

A data scientist spends his days collecting data. He is interested in potentially exploitable facts, numbers and texts. All formats are accepted. Those that cannot yet be explored are patiently waiting for technology capable of translating them into useful information to appear. The data is primarily transactional or operational. Some provide information on sales while the others relate to analytical accounting.

Compilations of numbers, keywords or facts are meaningless until analyzed. The expert uses technological means to handle them. Its mission is to associate, classify and order in order to obtain understandable information. For example, receipts can provide information on best sellers and products that need communication. Data mining leads to conclusions. These are patterns or trends which constitute essential knowledge for the future.

Mines of information

Before analysis, the data is stored in Data Warehouses. These are virtual hangars where figures, facts and sequences are stored in a raw manner. Their collection has already required significant technological resources. Barcodes and QR Codes are included in the list. That said, the forms as well as the registrations made by consumers themselves supply the data silos.

Companies are not required to set up a Data Warehouse to have forecasts. They can use data compiled by others. In addition to other companies, social networks and search engines store the slightest gesture of Internet users. Analysts will be able to access information on a specific target by paying a financial fee. The cookies offered at the entrance to websites are robots that collect data.

Multiple uses for this science

Although the commercial application remains the most widespread, Data Mining is not limited to marketing and mass distribution.

  • Higher education researchers use it every day. Scientists also sometimes use analytical applications to better understand genetics and chemistry.
  • Currently, the WHO draws conclusions on anti-covid19 vaccines by compiling daily reports from health workers from connected tablets.
  • For those who want to get started in website publishing, Web Mining is for them. Based on the analysis of interactions with visitors, it aims to identify behavioral patterns. It is even possible to quantify the remarks.
  • The human resources department can explore data to seek to understand staff. Statistics make it possible to better manage careers.
  • Large e-commerce companies rely on Data Mining to manage their targeted promotions. They also adjust their marketing mix: price, communication, distribution and the product itself.

Data analysis provides a better understanding of consumption

In the retail category, many American grocery chains rely on Oracle. The latter offers analytical tools to clarify consumer needs based on purchases. The procurement department knows exactly which products to send to which stores. The software shows that beer and diaper sales skyrocket on Thursdays and Saturdays in some cities in the mid-west United States.

The merchandisers concluded that:

  • The population replenishes their supply of drinks during the week so that they are very cold at the weekend.
  • We need to bring the beer and baby diaper aisle closer together to make it easier for consumers.

Better collaboration through transparent transactional data

A certain transparency of data allowed WalMart to better plan its restockings. The retail giant relied on this principle to manage the relationship with its suppliers. Some 3,500 of them were able to access a Data Warehouse. Thanks to software developed by Teradata, they were able to monitor stock in real time in 2,900 stores spread across 6 countries.

  • Suppliers adjust their delivery by taking into account the purchasing habits of customers in each supermarket.
  • The overall analysis allowed them to identify needs and led to the launch of new products.
  • WalMart is a pioneer in Data Mining since its computers were capable of handling up to a million complex queries as early as 1995.
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An example in the associative sector

The National Basketball Association (NBA) also does Data Mining. The team game leader analyzes video recordings of matches. It uses Advanced Scout, software that tracks player movements. The coaches of the different teams have access to the resulting information. This helps them better orchestrate strategies on the ground.

In 1995, the match played between the New York Knicks and the Cleveland Cavaliers made it possible to mathematically demonstrate that John Williams scores more baskets when Mark Price was in defensive play. A pioneer in statistics applied to sports, Advanced Scout estimates that the Cavaliers miss 51% of shots. This kind of quantified conclusion saves coaches and teams from watching hours of video.

The situation changed with the appearance of the Internet

The advent of Web 2.0 takes Data Mining to a completely different dimension than simple statistics. Things will get even more complicated with social networks and connected objects. Astronomical amounts of data are collected and analyzed. Companies are closely monitoring consumers. They pay attention to what they post, like and share on platforms.

Facebook posts can be avoided for those who want to limit their digital footprints. It is also possible to clear your web browsing history. On the other hand, it is less easy to escape recordings of credit card purchases or appearance in video surveillance. Policymakers even had to make laws about it. Since 2018, Google has been able to remove names or content from its database upon a simple request from the interested party.

A data dictate still contested

Google does not have supremacy when it comes to data storage. Other firms are exploiting the vein. All the servers relocated for the benefit of professional users and individuals form the Cloud. Businesses store raw information there to better understand their target customers. Some of them resell them to other companies. Many governments are also helping themselves. All this is done without the consent of those concerned. Now, companies are offering consumers the ability to maintain control over their digital footprint.

Leader in its field,, which has been operational since 2009. This startup offers individuals the ability to manage their own information using dedicated tools. They can collect and share them on their own terms. The “My Internet” concept allows you to sell your data yourself. deploys individualized servers with the help of Toshiba and Lenovo. The health insurance, finance and pharmaceutical industry sectors are among the most loyal customers.

The use of data for administrative purposes or recruitment

The Indian government uses data mining to track tax evasion. The local administration offers simplified payment methods to citizens. Taxpayers who are not very clean will have a little more difficulty circumventing the system. France is also deploying a similar system. The DGFIP teams include statisticians whose mission is to find VAT fraud. The figures and percentages highlight the maneuvers in certain sectors of activity.

Data mining applications are also at the service of recruitment professionals. They use digital tools to locate the most talented employees. Irish companies rely on this approach to find valuable candidates. They analyze compilations of information to hunt for young graduates with the best grades or the most productive workers. LinkedIn exploits this vein by devoting 200 full-time employees.

Fairly simple to understand operation

The main role of Data Mining is to ensure the connection between relational data and transactional information. In other words, information on customers, but also on the functioning of a company is analyzed. A huge amount of statistics-based software has emerged. Added to this is Machine Learning and neural networks. Explanations:

  • The stored data makes it possible to establish predetermined groups. Example: a fast food chain analyzes consumer habits to offer menus.
  • Organized into clusters, the data is grouped to draw a conclusion about customer preferences. This information results in market segments or affinities.
  • Sometimes, products are associated through data mining. This is the case for beer and baby diapers.
  • Sequential patterns allow you to anticipate trends. Example: a person who buys a sleeping bag can also invest in hiking shoes.

5 main tools in the hands of data scientists

data science

Currently in fashion, neural networks are programs capable of nonlinear analysis. This form of artificial intelligence allows predictions close to human intuitions.

Decision trees are also popular. The directions a business can take are complex ramifications. The Classification and Regression (CART), or Chi Square Automatic Interaction Detection (CHAID) models are the best known.

The Nearest Neighbor method is also explored. This involves drawing conclusions about trends based on similar behaviors. In law, this is called jurisprudence.

“If-then” rules are based on simple programming based on statistical significance. This is also the case for visualizing complex relationships. Multidimensional information is illustrated in a way that everyone can understand.

Genetic algorithms took off when the world suddenly became interested in medical sciences. Data scientists are also contributing to efforts to combat the Covid19 pandemic. They manipulate combinations, mutation as well as natural selection.

3 steps that remain almost the same

Data Mining changes form for each sector of activity. On the other hand, the steps to follow are almost the same.

  1. Companies provision Data Warehouses in various ways. Data is stored in local servers or the Cloud.
  2. Business analysts take over by looking for the logic of consumer behavior. They also model operating data in order to offer better organizations to business partners.
  3. All information takes the form of a graph or other summary that managers can use in their decision-making.

3 main properties are inherent to data mining

Pattern discovery is done automatically. The fruit of hard work by programmers, algorithms know how to establish logic for consumer behavior. All data formats are taken into account, but application developers especially favor a scoring system.

Outcome prediction is another branch of its own. It is not limited solely to commercial facts. Algorithms are able to determine purchasing behavior based on education or geographic location. This allows businesses to establish themselves in specific neighborhoods.

The usefulness of Data Mining is only called into question when the resulting information is unusable, even in the future. The most modern cities have teams capable of anticipating demographic movements. These computer engineers or statisticians are the civil servants responsible for steering the actions to be carried out at the municipal level.

Data Mining technologies are more accessible than before

Anyone who can understand the basics of statistics will be able to get started in Data Mining. Currently, mobile applications and SaaS-type online tools allow users of all kinds to analyze data. Some of them are free. Others have prices ranging from a few thousand to a million euros. Billing is done per terabyte used. For example, NCR can handle up to 100 billion billion bytes.

For a business, an application capable of dissecting a 50 gigabit block of data would be a good start. Everything is held in a single computer. Then, we need an infrastructure to analyze larger banks of information. Query complexity also enters the equation. Furthermore, knowledge of programming is useful at this level. Investment in digital structures Massiely Parallel Processors (MPP) is becoming essential for multinationals.

Available in several forms, Data Mining software is aimed at SMEs. In addition to merchants, many restaurants and libraries have also paid money to acquire these tools. Plus, there are open source programs. Weka, RapidMiner and Tanagra are among the most cited, but others are about to be developed. They are based on associations and sequential patterns.

What more can I say about data mining?

In the near future, companies that master data handling to perfection will enjoy certain growth. On the other hand, consumers will feel more and more observed. It is almost impossible to visit a website without the publisher offering a cookie. No wonder obesity is becoming the leading cause of death in the world…

List of entities using data mining in establishing their strategies.

  • Big data
  • France
  • Google
  • Data mining
  • Airlock
  • Spss
  • Microsoft
  • IBM
  • Weka
  • Erp
  • Oracle
  • Amazon web services
  • Netflix
  • Kdd
  • Paris
  • Microsoft analysis services
  • NCR