Data Mining is a process of understanding data through cleaning of data, identifying patterns, creating a model, and testing that model with that cleaned dataset. All these activities can be achieved through statistics, machine learning algorithms, and databases.

Data mining has a long history. The emergency period was in between the 1960s and 1980s. Historically, Data Mining was a manual coding process – and it still involves not only coding abilities but also ability to extract, clean, process, and interpret data still today. Data Miners need some programming knowledge and statistical knowledge for performing all the steps. Fortunately, manual processes are now able to be automated through repeated flows, Machine Learning (ML) and artificial (AI) systems.

Data analytics isn’t precisely data mining

Normally, people confuse data analytics with data mining. Although data mining includes data preparation and analysis of data, but the actual process doesn’t end here. It also includes model development, testing hypothesis with those models, and publishing those models for analytics and business intelligence projects. In other words, analytics and data wrangling are indeed part of the data mining process.

Who performs data mining tasks within the organization?

Data mining is generally the responsibility of a data scientist or a data analyst within the organization. Data mining tends to require large projects with cross-functional project management, and it can ladder up to analytics or business analysis teams. Some organizations look to data mining specialists to build machine learning (ML) or artificial intelligence (AI) scripts, so proficiency and knowledge of these algorithms are often a core competency for the responsible employee. Within research organizations or in academic institutions, data mining specialists are likely to be called data scientists or analysts and they can exist either as a part of a single lab or as a part of a service center of excellence team.

Avoiding data mining mistakes

Data mining is a powerful process for exploring data to analyze and predict patterns and outcomes. Unfortunately, it’s easy to do the process incorrectly. Additionally, you shouldn’t use data mining if your leaders do not have specific analytical or statistical knowledge. If we will execute inaccurate mining techniques, then it can create incorrect models, resulting in inaccuracies. Further, if the team is using personally identifiable information in data mining activities, they must ensure they are following compliance regulations and governance standards.

Advantages of data mining

Data mining is most effective when it is deployed to serve a business objective, answer business questions, or be a part of a solution to a problem. Data mining is also suitable to make accurate predictions, recognizing patterns and outliers, and often informs forecasting. Additionally, data mining helps to identify gaps and errors in processes, like bottlenecks in supply chain and improper data entry.

How data mining works

The first part of data mining process, which most of the readers should already know, starts with data collection. Previously, structured data was the only data collected; however, unstructured data is also being collected by the analysts as well. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is an excellent guide for starting the data mining process. This standard was developed decades ago, and it is still being used as a benchmark for transforming data for devising business strategies.

The 6 CRISP-DM phases

The CRISP-DM comprises of six phases. It was designed to be a flexible standard, where analytical groups are allowed and encouraged to move back to a previous stage if needed within their project. 

  1. Business Understanding:
    Knowing the project scope and objectives is the primarily phase in this standard practice.  The business stakeholders will raise a question or describe a problem that data mining can answer or solve through data analysis.

  2. Data Understanding:
    It is the most important phase in the whole CRISP – DM because in this step, data miners collect the data relevant to the question and get a feel for the data set. This data comes from various sources, both structured and unstructured. This stage may include some exploratory analysis to reveal some preliminary patterns related to the problem at hand. At the end of this phase, the data mining team has selected the subset of data for analysis and modelling and can move on to the next phase.

  3. Data Preparation:
    This phase begins with more intensive work. In recent research conducted, it was revealed that data analysts spend more than 80% in data preparation and standardization. The techniques involved in this phase includes clustering, predictive models, classification, estimation, or a combination.  Front Health used various statistical modelling and predictive analytics techniques to decide whether to expand healthcare programs to other populations. If required, returning to the data preparation phase is advised if a modelling technique is selected that requires selecting other variables or preparing some diverse sources.

  4. Modelling:
    In this phase, you select the appropriate modeling technique for the problem in front of you. These techniques can many data mining techniques, such as: clustering, predictive models, classification, estimation, or a combination. The Data Analyst may have to return to the data preparation step if a modelling technique is selected that needs selecting some other variables within the data or preparing some diverse sources altogether.

  5. Evaluation of model:
    After creating and training the model with the given dataset you just cleaned, the next step is to evaluate the model against the given problem at hand. The model may answer facets of things not accounted for, and you may need to edit the model to fit your needs or just edit the question altogether.  This phase is designed to allow you to look at the progress made so far and ensure it covers the business goals and objectives. If it’s not, there might be a need to move backwards before a project is ready for the final deployment phases.

  6. Deployment:
    Deployment is the last phase in the CRISP-DM phase. It can occur within the organization, be shared with selected customers, or be used to generate a report for stakeholders to prove its effectiveness in resolving the problem. The work doesn’t end when the last line of code is complete; deployment requires careful thought, a plan for rolling-out solution, and a path to make sure the right people are informed in the most appropriate way possible. The data mining team is responsible for the audience’s understanding of the project.

Most Important Types of Data Mining Techniques

Data mining includes diverse types of modelling techniques. Two of the most important ones are listed below:

  1. Classification:

    The most important data mining technique is classification. For performing classification, a single variable is differentiated separately from each other based on the variable’s attributes. For example, the variable “occupation level’ can be split up into ‘entry level’, ‘junior manager’, ‘senior manager’, and more. With other variables such as age and education level, you can program the data model to not only analyze but also predict what occupation level a person is more likely to have. Insurance or financial institutions use classification to train their algorithms to flag fraud and to monitor claims within their dataset.
  2. Clustering:
    It is another common technique which is based upon grouping records, cases, or observations by similarity. There won’t be a target variable like in classification. Instead, clustering divides the dataset into multiple subgroups. This method can include dividing the dataset into subgroups based on age or geographic location.


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