What are the disadvantages of data mining
Data mining: definition, benefits and examples
Data mining is not an invention of the digital age. The underlying concept has been around for over a century, but came more into the public eye in the 1930s. One of the first examples of data mining dates back to 1936: The British scientist Alan Turing presented the idea of a universal machine that could perform calculations similar to modern computers.
Far-reaching developments have taken place since then - companies are now using data mining and machine learning to improve their sales processes and interpret financial data for investment purposes.
In the following, you will find out how data mining is to be defined, which advantages result from the process and how international companies can benefit from it.
What is data mining?
Data mining refers to data analysis and the semi-automatic evaluation of huge amounts of data. Various statistical methods are used to reveal relationships, patterns and trends in databases that would otherwise remain hidden. Such insights enable companies to solve problems, reduce risks and seize new opportunities. In addition, data mining can be used to predict future developments - and based on these forecasts, well-founded business decisions can be made.
The term data mining has established itself for data science due to its similarities to mining. While in the latter case, natural resources such as coal and iron ore are specifically extracted, the digital process of data mining is intended to bring valuable and relevant information to light. In both cases, it is important to first sift through large amounts of material in order to find hidden treasures underneath.
Data mining is used in various areas of the economy as well as in research - for example in sales and marketing, in product development, in health and education. When used correctly, companies can gain significant advantages over the competition with the help of data mining: With the help of the data mining process, they get to know their customers better, can adapt their marketing strategy to their needs and thus increase their sales in the long term.
Data mining methods - components of data science
Getting the best results from data mining requires a number of methods and tools. Some of the most important are presented below:
- Data cleansing and data preparation : Here, data is converted into a form (e.g. into a specific file type) that is suitable for further analysis and processing. Missing and damaged data should be detected in the course of this process and removed if necessary.
- Artificial intelligence : Systems of this type perform analytical activities associated with human intelligence, such as: B. Planning, learning, reasoning and problem solving.
- Association analysis: Association rule learning, also known as shopping basket analysis, looks for relationships between variables within a data set. This can be used, for example, to determine which products are usually bought together.
- Clustering: Data records are each divided into several meaningful clusters. This enables users to grasp and understand the structures of databases more quickly.
- Classification: With this method, individual elements of a data set are assigned to target categories or classes. In this way, the target class for data should be able to be precisely predicted in the future.
- Data analysis : The process of evaluating digital information so that it can be used for business intelligence purposes.
- Data warehousing : A large collection of business data intended to aid companies and organizations in making decisions. This is the basic component of most large-scale data mining projects.
- Machine learning : A programming technique based on statistical probabilities. Computers are thus given the ability to “learn” independently, instead of “knowledge” having to be applied to them individually.
- Regression: A technique used to get a series of numeric values, such as: Predict sales, temperatures, or stock prices based on a specific set of data.
Data mining - reasons and advantages at a glance
Data in many different formats flows into companies in large quantities and at high speeds. Extracting substantial information from this and managing it sensibly may seem like an impossible task at first. However, data mining provides a remedy: The individual methods can be used to gain insights from Big Data, which enable better decisions and measures throughout the company. With data mining, almost any problem in the business environment can be solved. This has the following advantages:
- Increase in sales
- Understanding of customer segments and preferences
- Acquisition of new customers
- Optimization of cross-selling and up-selling
- Stronger customer loyalty and loyalty
- Increase the ROI of marketing campaigns
- Detecting Fraud
- Identification of credit risks
- Monitoring operational performance
Another general advantage of data mining is that business decisions are based on real business intelligence instead of instinct or your own gut feeling. In addition, there is a significant time saving: The technologies for processing large amounts of data, such as More and more companies, such as machine learning and artificial intelligence, are becoming easily accessible. You only need a few minutes or hours to sift through terabytes of data - instead of several days or even weeks, as was previously the case.
Advantage through data mining - example
In principle, data mining enables companies to develop optimized action measures by first evaluating data from the past and the present. This enables predictions to be made as to how individual business areas will develop in concrete terms in the future.
Using data mining methods, for example, it is possible to identify which prospects could grow into profitable and long-term customers. The previous customer profiles (filled with data from the past) serve as the basis. They tell you which customers are most likely to react to a specific future offer. If companies offer their product or service primarily to this group of people, they can increase their return on investment (ROI) in a predictable and sustainable manner.
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How does data mining work?
Typically, a data mining project begins by asking relevant, business-related questions that the company wants to answer using the methodology. For this purpose, the relevant data is collected and prepared for analysis. The data quality is crucial here - because how successful the data mining process is depends on what happens in the early phases. Accordingly, poor quality data can lead to poor or unusable results.
Data mining professionals typically achieve quick and reliable results by following a structured, repeatable process that includes the following six steps:
- Business understanding: Develop a deep understanding of the project parameters, including the current business situation, the primary business objective of the project and the success criteria
- Understanding of data: Determine the data needed to solve the problem and collect the data from all available sources
- Data preparation: Preparing and converting the data into the appropriate file format needed to answer the business questions, as well as resolving data quality issues such as: B. Missing or duplicate data
- Pattern recognition: Using algorithms to identify patterns in the data
- evaluation: Determine whether and how well the results provided by a particular model are contributing to the achievement of the business objective. Often there is an iterative phase to find the best algorithm and thus to achieve the best result
- Summary: Making the project results available to decision-makers
During this process, close collaboration between business experts and those who carry out the data mining process is essential. This is the only way to fully understand the significance of the data mining results for the business issues examined.
Data mining examples - how companies use the methods
With the help of data mining, international companies from all sectors gain relevant knowledge that will help them to achieve further success. Depending on the orientation of the company, there may be different questions that need to be answered with precise data. For what purposes and how data mining methods are used in practice, the following example companies show.
Data mining example # 1 - Groupon
The US company Groupon operates websites with discount offers. One of the biggest challenges is handling the huge amounts of data required for the e-commerce marketplace. Every day, the company processes more than a terabyte of customer-related raw data in real time and stores this information in various database systems. Data mining enables Groupon to precisely record the needs of its customers and to coordinate its marketing activities even better. Trends and future developments can also be identified with the help of data mining and enable Groupon to optimize its marketing.
Data mining example # 2 - Air France KLM
The airline Air France KLM responds to the travel preferences of its customers. It uses data mining methods to create 360-degree customer views by incorporating and crawling data from travel searches, bookings and flight operations with web, social media, call centers and airport lounge interactions. The airline uses the in-depth insights that Air France KLM receives through data mining to create personalized travel experiences.
Data mining example # 3 - Bayer
Bayer uses data mining to support farmers in sustainable food production. These weeds that attack their crops have always been a major problem for these weeds. One solution is to use a specific narrow-range herbicide that has to be tailored to the specific type of weed. This should have as few side effects as possible. However, in order for it to be produced, farmers must first accurately identify the weeds in their fields. Using Talend Real-Time Big Data, Bayer Digital Farming developed WEEDSCOUT, an application that farmers can download for free. The app is based on artificial intelligence and machine learning. You can thereby inter alia. Compare photos of weeds from a Bayer database with those that farmers take and upload to. According to this, data mining methods enable farmers to make more precise predictions about the effects of their actions - e.g. B. related to the choice of seed variety, the amount of pesticides or the time of harvest.
Data Mining Example # 4 - Domino’s Pizza
Domino’s would like to bake the perfect pizza for its customers and is also using data mining. The world's largest pizza company collects data from 85,000 structured and unstructured data sources, including point-of-sales systems and 26 supply chain centers. Domino’s uses all channels for this - including Text messaging, social media, and Amazon Echo. Relationships that exist between the large amount of data and that are made visible with the help of data mining enable Dominos to further optimize their business performance.
These are just a few examples of how data mining methods can help data-driven companies increase their efficiency, streamline processes, reduce costs over the long term and improve profitability.
The future of data mining
It is already becoming apparent that data mining and data science will become even more important in the future. Because the amount of data in the business environment is increasing sharply - more and more companies need intelligent data mining methods to analyze data and work out relevant information.
While in the past only organizations like NASA could carry out data analysis on supercomputers, this is now possible for all kinds of companies. Before, the cost of storing and processing such large amounts of data was simply too high. Today this is no longer the case. Many companies use data mining processes and rely on machine learning technologies, artificial intelligence and deep learning with cloud-based data lakes.
The Internet of Things in particular generates innumerable amounts of data available in cloud systems. It has turned devices and people - with the help of wearables - into data-generating machines. This creates a greater need for flexible, scalable data mining tools that can process large amounts of information from different data sets.
Data mining tools - software for better analysis and results
Data mining has the potential to transform businesses. A prerequisite for this is a highly functional data mining software that meets the requirements of all those involved. However, this claim in particular can lead to the selection being delayed. Because the offer is diverse and each tool has different advantages and disadvantages:
- Cloud-based analysis tools make it easy for companies to access extensive data and computing resources. They are also cheaper. Cloud computing helps companies to quickly collect, process and analyze data from sales, marketing, the Internet, production and inventory systems and other sources.
- Open source data mining tools offer users a new level of performance and flexibility. They meet analytical requirements in a way that many conventional solutions cannot. They are also connected to large analyst and developer communities where users can work together on projects. In addition, advanced technologies like machine learning and AI are now available to almost every company.
Basically, however, there are a few criteria that can be helpful when deciding on a data mining tool. For example, it is known that companies that derive a great deal of value from data mining i. d. Usually choose a platform that ...
- ... incorporates best practices from the respective industry. For example, healthcare companies have different requirements than e-commerce companies.
- ... manages the entire data mining lifecycle, from data exploration to production.
- ... matches the rest of the business applications, including BI systems, CRM, ERP, finance, and other business software it needs to work with for maximum return on investment.
- ... is compatible with established open source languages and enables developers and data scientists to work flexibly and provides the right tools for collaboration in order to create innovative applications.
- ... meets the needs of IT, data scientists and analysts while meeting the reporting and visualization needs of business users.
The Talend Big Data Platform is a complete suite including data management and data integration functions to help data mining teams respond more quickly to business needs.
Intelligent data mining software from Talend
Companies are flooded with huge amounts of internal and external data. It is therefore advisable to use data mining software. With this, the most important findings can be drawn from the extensive raw material - and at the pace set by the respective company.
Regardless of the industry, numerous companies rely on the intelligent and flexible tools from Talend - also when it comes to obtaining quick and precise results with data mining. Our modern platform for data integration enables users, for example, to work together efficiently in teams.
Discover now how Talend's big data tools can also support your company in processes such as data mining - with the free trial version.
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