Following on from our beginner’s guide to data science, we look at one of the subsets of the discipline: data mining.
Data mining is the process of trawling and analyzing enormous sets of data and summarizing it into information that can be useful for your organization. It is focused on the discovery of patterns or correlations, with the emphasis placed on robust, data-driven, scalable techniques, with little interest for finding causes or interpretability. Data mining tools, such as IBM SPSS Modeler and Oracle Data Mining, search databases looking for patterns, often those that experts have missed because they are external to their expectations. This knowledge is then used to predict behaviors and future trends, from which it creates actionable information, giving businesses the ability to make proactive, knowledge-driven decisions.
Data mining produces useful information to tell you important things that you didn't know to be happening, or things that are likely to happen in the future, using a process known as modeling. Modeling is the act of building a model based on data from situations where the answer is known, and then applying the model to other situations where the answers are unknown.
There are four different kinds of relationships found through data mining. Firstly, it finds data in predetermined groups, known as classes. Clusters of data can also be found by mining. This includes items that are grouped together based on logical relationships or consumer preferences. Data can be also be mined to identify associations, as well as sequential patterns.
While data mining can be used by firms across all walks of life, it is especially popular among companies that are more customer focused. It allows them to pinpoint the relationships between internal factors such as price and staff skills, external factors such as competition and customer demographics, and also the impact that these factors have on a firm’s objectives, sales, and corporate profits.
The costs of data mining vary, with a number of free and open source data mining tools available, although whether the results are accurate or not is a different story. The same is true of the more expensive software programs, which may produce information at greater speed, but its usefulness and accuracy may still not be a good as necessary. This risk, the central one in data mining, is only really mitigated by employing a solid team that has a thorough understanding of the technology to oversee the process.