What Is Data Mining and How Does It Benefit the Businesses?
By Şevval Begüm Bayram
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Massive amounts of data, especially raw data, surrounds us all the time. Roughly 2.5 quintillion bytes of data is produced every single day with an exponential growth rate. This means that it is getting harder and harder everyday to reach useful data, which raises the importance of data mining. Data mining is a vaguely defined data analysis that aims to find meaningful trends and patterns in large blocks of data. The vagueness of its definition comes from the wide range of application and technique options to choose from. It is used in almost all sectors from education to social media or e-commerce mainly in order to develop strategies and business plans for more profits/efficiency and to give customers a better and personalized experience while detecting anomalies.
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Data mining can be divided into two types: descriptive and predictive data mining. Descriptive data mining helps analysts to detect features and patterns to give summaries by monitoring past behaviour of the data. On the other hand, predictive data mining enables the companies to foresee the future outcomes and detect risks and opportunities in order to make more efficient decisions and create strategies. In other words, descriptive data mining deals with what has happened and why/how it happened whereas predictive data mining is concerned with what is possibly going to happen in the future. The data mining techniques vary depending on the purpose. Some examples of descriptive data mining techniques include clustering, association rules, sequence discovery, and summarization. These techniques mainly provide labels, groupings, and features of the data. Predictive techniques include classification, regression, time series analysis and prediction analysis. These techniques, however, provide estimates and trends that extend to the future.
Data mining shouldn’t be seen as a one-step process. There are several important steps prior to data mining to get the best results. Since data mining is a really wide concept that can be applied to various business settings, understanding the business has a big significance. The first step for data mining is to get to know the business, meaning determining its vision and mission. The next step would be the data collection. Companies generally already have an ongoing system to gather data from their clients, especially in the industries like technology and e-commerce. Data gathered is most usually stored in a data warehouse or a data lake, which are common repositories used in order to store big data. Data lakes store raw data – both unstructured and structured data – whereas data warehouses store processed data – filtered, structured data – with a defined purpose. Depending on the preference of the companies, cloud systems in different structure levels – unstructured, semi-structured, structured, and mixed – like SQL Data Warehouse, Amazon Redshift (data warehouse), Amazon S3 (data lake), Apache Kafka, Snowflake can be used. After that, data should be prepared for mining through cleaning. This step includes detecting and eliminating errors and ultimately increasing data quality by fixing the accuracy, completeness and consistency issues. After all these steps, it is possible to operate the data mining with confidence. Depending on what the data scientist tries to achieve, data mining can be done by building various models and/or algorithms such as k-means/k-nearest neighbour, regression models (linear, logistic, Poisson, Lasso regression etc.), and decision trees. The analysis of the findings would be done and the relationships, trends and predictions derived from the models can be depicted through data visualization techniques graphs, diagrams, and dashboards. Visualization makes it much easier to read and understand the data. After this step, it is up to business professionals and executives to evaluate and make decisions using the mined data.
Data mining is not only practiced in almost every sector, it can be applied to almost all of the areas of the business. One of the most common areas is sales and marketing. Data can reveal information on the popularity of the products, which products are bought together or how long it takes for the customer to make the purchase. It is also widely used for fraud detection, especially in banking and financial services. Data mining can identify the outliers or unexpected outcomes, which can be helpful to detect unusual transactions and manage legal regulatory compliance.
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Overall, data mining is an essential part of every modern business that deals with big chunks of data. It allows companies to discover information that wouldn’t be as obvious and run more time efficient, profitable and fault-free operations. On the other hand, it is important to point out its limitations. Data mining may not always give beneficial results simply because some data don’t provide strong trends. Models always have uncertainties and possible errors to a certain extent. Especially with predictive mining, it is crucial to keep in mind that data mining provides trends and possible outcomes but doesn’t guarantee exact results.
