Banks, Retail, Medicine: Who uses Data Mining and for what

Banks, Retail, Medicine: Who uses Data Mining and for what

What is Data Mining Data Mining (data production, intellectual data analysis, deep data analysis or just data mining) is a process used by companies to turn unprocessed big data into useful information.

Also, this technology uses the less popular term “discovery of knowledge in data” or KDD (Knowledge Discovery in Databases).

If the Big Data term means more and more data – both processed and not, then Data Mining is a deep immersion process in this data to extract key knowledge.

Innovation economy What is Big Data and why they are called “new oil” The author of the term Data Mining Grigory Pyatetskiy-Shapiro defined it as a process of detecting previously unknown, non-trivial, practically useful and affordable knowledge of the knowledge necessary for decision-making in various fields of human activity.

Using software to search for laws in large data packages, enterprises can build marketing strategies, manage credit risks, detect fraud, filter spam or even identify user moods.

Intellectual analysis of data depends on the effective collection, storage and computer processing of data.

Data Mining is considered a separate discipline in the field of data science.

The term “intellectual analysis of data” appeared in academic magazines back in 1970, but it became truly popular only in the 1990s after the appearance of the Internet.

Then the companies needed to analyze large volumes of heterogeneous data in order to find non -trivial patterns and learn to predict customer behavior.

Ordinary statistics models were unable to cope with this task.

The first Data Mining systems were intended for processing sales data in supermarkets in several parameters, including their volume by region and product type.

Data mining tasks Models of intellectual data analysis are used for several types of tasks: forecasting: sales assessment, prediction of the load of the server or its downtime; risk and probability: the choice of suitable customers for the target mailing, determining the balance point for risky scenarios, the purpose of probabilities by diagnoses or other results; Recommendations: Determination of products that will be sold together, create recommendatory messages; Search for sequences: analysis of the choice of customers during purchases, forecasting their behavior; Grouping: dividing customers or events into clusters, analysis and forecasting of common features of these clusters.

Where Data mining is used Intellectual analysis of data is mainly used by sectors serving consumers, including in the field of retail trade, in finance and marketing.

For example, Sberbank has a “Analytics collection” service, which provides data on the market industry or territories based on the analysis of cash flows, sales of goods and services and other parameters.

It can be used by both companies and state bodies to evaluate the potential of the development of the region.

Trade Data mining retail chains allows you to analyze customer baskets to improve advertising, create stocks of goods in warehouses and plan how to decompose them on windows, open new stores and identify the needs of different categories of customers.

The Russian network “Lenta” analyzed the data of loyalty cards of more than 90% of its customers and divided the audience into certain segments for customer behavior.

In particular, the retailer has identified the segment of buyers only basic products and men, who more often purchased only drinks and snacks.

This made it possible to optimize the assortment and manage the calculation and prices.

And Amazon in October 2021 announced a tool that would provide sellers with access to information about what buyers are currently looking for, and thereby will help to simplify the choice of products for sale.

Banks and telecom Data mining credit organizations allows you to identify fraud with credit cards by analyzing such transactions, as well as offer various types of services to different groups of customers.

Telecom uses data analysis to deal with spam and develop new tariffs for various groups of subscribers.

Russian cellular operators use Data Mining for internal purposes, and also offer data analysis as a product.

So, Beeline in 2020 launched a new service, which allows companies to receive demographic data of its customers through a database dates, which Vimpelcom collects.

Insurance Insurance companies analyze large amounts of data to identify risks and reduce their losses on obligations, as well as offer customers relevant services.

So, the Australian private insurance company HCF Analysis of Big Data made it possible to reduce the costs of advertising newsletters by 25%in four months.

Analysts accurately determined those customers who are most likely ready to purchase a more expensive service, and made a separate newsletter for them.

Production Enterprises analysis of big data allows you to coordinate supply plans with demand forecasts, as well as detect production problems in the early stages and successfully invest in the brand.

In addition, manufacturers can predict the wear of production assets and plan maintenance and repair so as not to stop the production line.

An example of the use of Data Mining in industry is to predict the quality of the product, depending on the parameters of the technological process.

The Russian “Infosystems Jet” offers an intellectual system for supporting decisions Jet Galatea.

It analyzes the technological instructions and data received from the sensors on the equipment, and then forms and issues recommendations for technologists on the optimal conduct of the production process.

Jet Galatea is used in metallurgy, woodworking, agricultural industry and mineral extraction to reduce raw materials consumption and increase the volume of products.

Sociology Analysis of moods based on these social networks allows us to understand how a certain group of people relates to a specific topic.

Since 2016, the Russian police have been using Zeus system in some regions of the country.

It allows you to track the user’s behavior in the social network and builds the environment schedule, establishing a possible connection between users on the basis of analysis of friends, relatives, mediated friends, places of residence, common groups, likes and reposts.

The medicine Data mining systems are also used to make medical diagnoses.

They are built on the basis of rules describing the combinations of symptoms of various diseases.

Rules help to choose treatment products.

For example, the British startup Babylon Heath collects all the information about the health of customers, their lifestyle and habits, and then the algorithm builds hypotheses and offers options for examination, treatment and even recommends specific doctors and clinic.

An example of communication Babylon Heath with a client (photo: Babylonhealth.

com) Recognition systems Similar systems are designed to offer goods or services, which are most likely interesting to people, and are also used to support customers.

They work thanks to a date-major, which is carried out in real time.

Simply put, the model is constantly updated.

So the ALEXA voice assistants from Amazon, Siri from Apple and Alice from Yandex are working.

As an example, we can also give the VIDI taxi support service, where the algorithm solves up to 60% of user requests, since they are most often similar.

Data mining technology and methods There are several stages of data production.

Formulation of the problem.

This step includes an analysis of business requirements, determining the area of the problem, metrics by which the model will be assessed, as well as determining tasks for the analysis project.

Data preparation: Association and cleaning.

This work includes not only the removal of unnecessary data, but also the search for hidden dependencies, determining the sources of the most accurate data and creating a table for analysis.

Data study.

Build models.

Research and verification of models.

The accuracy of their forecasts can be checked using special tools.

Deployment and updating of models.

When the model has earned, it must be updated as new data arrives, and then re -process it.

Stages of Data Mining (photo: Predictivesolutions.

ru) What should know and be able to Date Mainer A specialist in intellectual data processing should have deep knowledge in the field of mathematical statistics, speak foreign languages, as well as programming languages.

He processes large volumes of information and is engaged in the search for connections in it.

The specialist uses machine learning methods, creates algorithms, works with statistical analysis.

Then the Date Mainer represents the organization results of its work in an understandable format.

Based on these presentations, the company makes decisions.

Employers prefer Data Mining specialists with technical, mathematical or natural science education.

Universities offer appropriate areas of training: “Mathematics and Computer Sciences”, “Applied Mathematics and Informatics”, “Applied Informatics” and “System Analysis and Management”.

In addition, Data Mining can be studied in courses, for example, Coursera.

According to the HeadHunter portal, in October 2021, the salaries of the dates-mainers in Russia were from ₽28 thousand to ₽250 thousand.

Data mining programs There are many programs that can perform Data Mining tasks.

Here are some examples.

SAS Enterprise miner is a set of intellectual data analysis methods that is used to solve such problems as detecting cases of fraud, minimizing financial risks, evaluating and predicting resource needs, increasing marketing campaigns and reducing customer outflow.

It has a convenient and understandable interface that allows users to independently create analysis and forecasting models.

Shows high performance even when working with a huge array of disparate data.

Microsoft Analysis Services is designed for business analytics, data analysis and reports.

Services are available on different platforms, including on the Azure cloud.

A mechanism is provided for creating your own algorithms and adding them as a new function of intellectual data analysis.

SAS Customer Intelligence 360 is a platform that allows business to plan and implement marketing campaigns, analyze their results and track customer flows.

She in real time collects detailed information about the actions of customers on web pages, including anonymous users, taking into account the context.

Then the platform gives recommendations on the time and place of placement of content on the pages and in mobile applications for a particular client.

Multi -channel content delivery to SAS Customer Intelligence 360 (photo: Blogs.

sas.

com) SAS Credit Scoring – a system for evaluating credit risks and customer creditworthiness.

Especially useful for banks, companies in the financial sector and telecom.

SAS Credit Scoring analyzes the data of a potential borrower and presents ready -made recommendations for issuing a loan or providing services taking into account possible risks.

Board-combines the functions of business analytics and corporate effectiveness management.

Allows enterprises to develop and maintain complex analytical and planned applications.

Also, the tool is convenient for drawing up reports if there is access to several data sources.

Sas Revenue Optimization is a set of solutions for optimizing retail prices, which allows you to determine the optimal price in a particular place and at a particular time to form competitive sales, launching promotions and mass sales.

It is used in retail.

Rapidminer is an open platform for data extraction with the possibility of deep training in algorithms, analysis of texts and machine learning.

Rapidminer can be used both on local servers of the company and in the cloud.

The platform is popular in energy and industry, mechanical engineering and other industries.

Future Data Mining The market of Data Mining systems is growing.

This is facilitated by the activities of large corporations: SAS, IBM, Microsoft, Oracle and others.

It is expected that by 2027 the volume of the global market for expanded analytics will increase by 23.

1% and will reach $ 56.

2 billion.

The latest trends in Data Mining include the development of analysis methods with virtual and augmented reality elements, their integration with database systems, biological data for innovation in medicine, web material (data analysis on the Internet), real-time data analysis, as well as measures to protect confidentiality in data extraction.

The industry leaders believe that in the future, data mining will be used in intellectual applications that will be built into corporate data storage facilities.

The main problem of detecting patterns in the data is the time that is required to redraw information arrays.

Famous methods either artificially limit such an overkill, or build whole decisions that reduce the effectiveness of the search.

The solution to this problem remains the main goal of products for Data Mining.

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