Data PrivacyData Mining

March 29, 20230


The term Data Mining was properly introduced in the 1990s along with the other terms such as Data Warehousing, Business Intelligence (hereinafter referred to as “BI”) and analytics technologies which began to emerge in order to analyze the immense amounts of data that companies were producing and gathering. Data Mining can be traced back to Bayes’ theorem and evolutionary regression which revolves around 1700 and 1800 eras respectively.

It was first used in first international conference on Knowledge Discovery and Data Mining which was held in Montreal in 1955.  Subsequently,  Data Mining and Knowledge Discovery published its first technical journal containing articles, discoveries, knowledge, techniques and practices on Data Mining in 1997. Organizations began utilizing Data Mining to analyze data, identify trends, and predict changes in interest rates, stock prices, and client demand in order to increase their customer base.


Data Mining is typically defined as procedure of extracting information from huge sets of data. It is also defined as mining knowledge from data. Data Mining techniques are to make machine learning models that enable Artificial Intelligence (hereinafter referred to as “AI”) such as search engine algorithms and recommendation systems can be stated. With the help of Data Mining techniques and technologies, enterprises can now forecast future trends and make more educated business decisions.

Data mining is a crucial component of data analytics as a whole and one of the fundamental fields in data science, which makes use of cutting-edge analytics methods to extract valuable information in data sets. It is categorized as a discipline in the field of data science.


Data mining is the process of study and analyzing huge blocks of data to discover significant patterns and trends. It can be further utilized in many different contexts including database marketing, credit risk management, fraud detection, spam email screening, and even to ascertain user emotion.

Many essential characteristics that distinguish Data Mining are discussed below:

  • Process of applying any model to new data: Data Mining and automatic recognition of patterns are the bedrock of the Data Mining models which indicate how these models are executed and how well they are constructed using well-established procedures. Applying a model to new data and evaluating its adequacy is known as the process of scoring.
  • Anticipate plausible scenarios: Many data mining forms are predictive in nature. To represent the possibility that each prediction would come true will have a certain probability. In different scenarios, the generation of rules can emerge from predictive data mining.
  • Forms of Data Mining: It places the spotlight on naturally occurring groupings within large data. For example, the main focus is put on a group of people who fall inside a certain income bracket, have a solid driving record and often rent out cars. These data can be useful to rental agencies as well as insurance companies.


Generally, data analysts follow a structure in order to have a better understanding of the Data Mining process.   In order to avoid any kind of  mishap data mining process is divided into six steps which are:

1. Catching the drift of Business: The first step in any data mining project is to comprehend the core of any organization and the project at hand before touching, extracting, cleaning or analyzing any data. To achieve the objectives at the end, the mining process should be well understood and necessary compliances should be done.

2. Data preprocessing: The second phase involves the selection, cleaning, enrichment, reduction, and transformation of databases. Once the business problem and strategies are resolved, it’s time to process the data. This step also evaluates the restrictions on data, storage, security and collecting and considers how these may affect the data mining procedure.

3. Preparation of data: This method entails statistical analysis of the data followed by the preparation of a graphical visualization of the data in order to obtain estimates of the value. During this stage, data is extracted, transformed, uploaded and calculated and then it is cleaned, standardized, evaluated for errors and reviewed for reasonableness.

4. Model Building: After the data from the previous phase has been acquired, it is time to compute the numbers. Based on the previous data analysis, a suitable model, such as clustering or regression analysis, is chosen. The data can also be fed into predictive models to see how previous data correlates with future outcomes..

5. Evaluating the results: Once the model is ready and all the values of data are uploaded, the results should be properly assessed and verified if the objectives set in the very first stage of the process is fulfilled or not. The outcomes from the analysis be presented to the decision-makers with the aggregated and interpreted results. By determining the findings of the data model, the data-centered aspect of data mining can be concluded.

6. Model update: The last step of the process would be to implement the changes and strategically pivot based on findings. This process can be concluded with management taking necessary steps in accordance to the results of the analysis.

Moreover, it is important to note that Data Mining process models may be different from other models and the steps can be reduced or increased as per the functioning of each model. For example, the Knowledge Discovery Databases model has nine steps, the Cross Industry Standard Process for Data Mining (hereinafter referred to as CRISP-DM) model has six steps, and the Sample, Explore, Modify, Model, and Assess (hereinafter referred to as SEMMA) process model has five steps.


The enhanced ability to find hidden patterns, trends, correlations, and anomalies in data sets is what gives Data Mining an advantage and it can be further used to draw business conclusions and strategic planning through conventional data analysis and predictive analytics. Benefits of data mining includes following:

  • Reliable data: The process of explicitly identifying a problem, compiling facts pertinent to the problem, and attempting to design a solution is frequently stricter and more structured. Therefore, Data Mining not only ensures a company collecting and analyzing reliable data but also helps in becoming more profitable, efficient and operationally stronger.
  • Easy to use: As mentioner earlier, the process of Data Mining can be different for different models but the overall process can be used with almost any new application. With the help of Data Mining, information can be gathered and analyzed and nearly almost every problem depending on qualifiable evidence can be addressed.
  • Finding correlation: It enables a business to generate value with the information at hand that might otherwise not be readily evident but the end goal of it is to collect information in bits and pieces and accordingly, determine if there is any correlation among the data.
  • Preserve data: Data Mining helps in preserving data which is irrelevant today but may be important in the future. All the information is stored in data warehouse or data lake for the easy accessibility.
  • Good idea for the insights: With irrelevant information will likely to produce unactionable insights. In order to draw the best outcomes for the development of the business, facts must be proper and the type of information must be defined for better operations of Data Mining.
  • Recognize outliers: Outliers are an important source of insight in addition to trends and patterns. Data Mining technique not only discovers anomalies in the data but also reports on the most prevalent features within a data collection, particularly when those anomalies are pertinent to the business objectives.
  • Stronger Risk Management: Risk managers and business executives can reduce risk such as financial, legal, cybersecurity with the help of Data Mining.
  • Better customer service: Data Mining allows companies can anticipate possible customer service issues more quickly and provide contact center personnel with up-to-date information to utilize when speaking with consumers over the phone or in online chats.


 To achieve the desired results using Data Mining, data scientists and organizations have to face several challenges which are listed below:

  • Noisy data: A data set containing corrupt or poorly structured information or if it contains data that can be beneficial for the specific purpose but is mixed with certain irrelevant information then that set of data are deemed to be as ‘noisy’. In those circumstances, a data analyst must either discover a technique to extract the useful data before mining it or search for different tool of disregarding the irrelevant data.
  • Scalability: In contrast to on-premise data warehouses with fixed hardware configurations, scalability is not a concern for those enterprises hosting their data infrastructure on a cloud platform that can scale up or down as and when required. Yet, big data sets can place significant demands on these facilities. It has been assessed by the analyst that for Data Mining the bigger the data set will be, the more resources it will require.
  • Incomplete data: It is impossible to say that every data set will be complete with all the required information.. Before mining the data, data engineers and analysts should ideally complete the data set by including such omitted data.

In the event that this is not practicable, they could lessen the impact of incomplete data by highlighting its absence in their reports or by interpreting trends from the outcomes of the available data. To furnish the missing or incomplete data, the process becomes tedious and time-consuming as data analysts have to assess or search for each and every information and compare the same with the data in order to find or complete that sets of data.

  • Complexity: Due to the complexity in the process of Data Mining, it becomes one of the largest disadvantages as data analytics constantly require technical skill sets and different tools for running a software. This complexity can be proven as a major setback as smaller companies having limited capital may find this costly and there will be barrier which is too difficult to overlook.
  • Expensive: Data Mining is a cost component variant as it constantly requires costly subscriptions and other data which may be expensive to obtain. Further, additional Information Technology (hereinafter referred to as “IT”) infrastructure will be required in order to maintain the security and privacy of data.
  • No guarantee of Accurate results: Data Mining is a computerized process and every time it cannot provide guaranteed accurate results. A company performing statistical analysis and making conclusions based on strong data, implementing required changes and still having no profits will be a major limitation of Data Mining.


 The bottom line is modern businesses or agencies have the ability to gather information on customers, products, manufacturing lines, employees among many others but with the help of Data Mining techniques and tools it can be brought together to drive a new value.

Data Mining was introduced with the intention of helping to draw conclusions by evaluating the massive amount of data in order to contribute the improvement and growth of the business. The objective is to find repetitive patterns, trends, or rules that explain the behaviour of the data collected over time.  Data collection, analysis, and operational strategy implementation will be the ultimate goal of the Data Mining process.

– Team AMLEGALS assisted by Ms. Juhi Bansal (Intern)

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