This article explains what data mining is and what it is for, and explores the issues that business people need to know about data mining.
Despite being a complex concept that involves highly specialised professional profiles, data mining is increasingly closer to businesses. The growing relevance of data mining in business environments is creating a necessity. Entrepreneurs now need to understand what data mining is and what it is for. In this article we explore what every business person should know about data mining.
Data mining is an increasingly widespread procedure in the business world. An article published in Forbes in January 2021 points out that data mining has become one of the top priorities for CIOs for 2021. Among other things, Kim Hales, vice president of IT at NRG Energy in Texas, highlighted in Forbes that data mining is a fundamental tool to "capture and integrate an even wider set of data as part of its decision-making processes." Thus, data mining is now part of the business strategies definition and decisions making process.
The only problem with bringing data mining and business strategies closer together is that data mining involves very complex procedures that require very technical profiles and that, for those who are not experts in data engineering, are very difficult to understand. However, business people do not need to know how to carry out data mining processes, but they do need to understand what it is, what it is for and how it can improve business productivity.
What is data mining?
Data mining is the process of discovering patterns and correlations in data through techniques based on statistics and mathematics. It involves sifting through large amounts of data using data mining algorithms and pattern recognition technologies to transform data into comprehensive information, identify patterns, predict trends, and create rules and recommendations. Data mining uses non-traditional pattern recognition methods and the patterns and trends revealed cannot be discovered through conventional queries due to the large amount of data or the overly complex relationships between them.
In business, data mining was developed to enable business people to access useful information from large amounts of data —usually all available data is explored— without having to rely on mathematicians or statisticians.
In short, data mining is a mathematics-based process that enables the discovery of otherwise hidden information. It is used to obtain knowledge that contributes to making better business decisions. It is, therefore, an advanced form of empowering data-driven decisions.
What to consider before investing in data mining
Data mining procedures are complex, involve multiple phases and can easily lead to errors. Because of this, they not only need to be carried out by professionals, but also require specific technologies that, if possible, should incorporate graphical user interfaces to increase the productivity of the processes and avoid errors. In addition, it is essential that the patterns discovered are validated, as well as to ensure that they will be valuable when applied to real business scenarios. On the contrary, technicians might find patterns that are not of use for business improvement.
Nowadays, the market has plenty of technological tools that enable and support data mining processes through graphical interfaces that facilitate processes and productivity, such as Microsoft SQL Server Data Mining, a complete data mining solution that includes several tools in a single environment specially designed to work with data mining models.
Business benefits of data mining
In addition to finding unique information and developing knowledge for making better business decisions and optimising more effective strategies and actions, data mining can be applied to a large number of concrete business actions and strategies related to business intelligence. For example, it can be used for:
- Making predictions such as sales estimates or forecasts of server load and downtime.
- Customer segmentation and behavioural forecasting: Correlations found in the data can be used to find affinities between customer groups and to classify them, for example, into groups according to the products or services they have purchased. It is also possible to predict what they will buy in the future and the frequency and amount of their purchases.
- Probability models: Data mining is capable of estimating probabilities and predicting risks, allowing risks to be avoided or minimised and business operations to be adjusted to the probabilities found.
- Generating recommendations: Data mining can be used to detect correlations between the products and services that make up a company's offering in order to apply cross-selling and up-selling strategies or determine which items are likely to be sold together.
- Improving the customer experience: Data mining can be used to find points of satisfaction and dissatisfaction in the customer journey, as well as to discover customer needs, preferences and pain points.
4 success stories where data mining has increased a company's profitability and efficiency
Far from being an abstract idea, data mining has been the key to success for some organisations that have seen their productivity increase thanks to the gaining of knowledge. An Forbes magazine article published in 2018 lists 4 success stories related to data mining:
1. The first example tells the story of a retailer who, through data mining, discovered which of its customers were likely to become long-term customers and which were not. He then optimised his marketing strategy and sales efforts, adapting them to the lifecycle of his customers. What this retailer achieved after a lot of hard work, is precisely what ABC Client Analysis does. Bismart's ABC Client Analysis is an easy-to-implement solution that automates this process and identifies profitable customers, unprofitable customers, strategic customers, customers with growth potential and the diversification and concentration of the customers portfolio.
2. An insurance company used data mining to identify which of its offices handled certain types of regular claims more efficiently, recognising good practices and applying them to its other branches. The company was able to reduce costs and provide a faster and more efficient service to its customers.
3. A police institution put data mining into practice to analyse the rules applied to prioritise police cases. The analysis revealed that cases were being prioritised randomly. Following the finding, the agency was able to transform the allocation of cases by replacing the previous system with a more functional and productive one.
4. Finally, a chemical producer took advantage of data mining to identify warning signs in chemical spills. He was able to establish accident prevention measures, reduce costs and investments, and implement new standards for environmental protection.
In this article we have seen some of the reasons for the expansive implementation of data mining in the business world. Data mining allows companies to obtain deeper knowledge —business insights— about any functional area related to the business activity, as well as to better understand their actions, strategies and processes and consequently improving them. Technology, therefore, has enabled companies to now use scientific methodologies to optimise their activity.