Nowadays, worldwide, every organization stores a lot of data used for testing/development purposes. The more data an organization produces, the more difficult it becomes to make sense of it and derive meaningful insights from it. There is an ingenious solution for this issue, which is the data mining process. Data mining identifies the meaningful relationship in the raw data of an organization, and it is typically done to predict future data. Data mining deals with a large number of datasets with various techniques involved.

What is Data Mining?

Data mining is a technique used by various organizations to change raw data into proficient information involving multiple techniques and methods. Data mining techniques analyze the data based on patterns and connections present in data. It aids in researching future trends by analyzing past data and also helps in identifying the relationships and correlations among the data.

Steps in Data Mining

1. Setting Objectives – Every organization should set an objective of what data they want and how it can be organized. This is where the data scientists and stakeholders come together to define a business problem to which data mining can be applied.
2. Data Preparation – This step is to identify the correct data based on the objectives set. It is to understand the type of data source involved. The data needs to be filtered or cleaned based on the need.
3. Data Processing – Defines applying the data mining techniques/models, i.e., it identifies the relationship/patterns/correlations in the data.
4. Evaluating Results – To evaluate the results obtained from the data mining models and deploy further if required.

Data Mining Techniques

• Association: Refers to the process of finding correlations between different types of data. The goal of association rule mining, given a set of transactions, is to find the rules that allow us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction.
• Classification: It is the process of predicting new data, i.e., putting your data in buckets based on specific shared qualities and characteristics. The most challenging aspect of classification is determining which categories one should place data into.
• Clustering: Similar to classification, clustering is loosely putting data in buckets based on similarities. The difference between classification and clustering is that classification requires creating categories, while clustering is more about finding similarities regardless of a category.

Advanced Data Mining Techniques

• Artificial Intelligence: Some of the artificial intelligence techniques helps the user to classify the data. The technique mainly used is Natural Language Processing (NLP) which helps in identifying insights from larger datasets.
• Machine Learning: In data mining, machine learning refers to programming software or computer to predict future patterns and behaviours without being explicitly programmed to do so. A data analyst can use the Python and R Programming languages to use machine learning in a data mining context.

In the market, there are a lot of data privacy products available for data mining features. One of the key products in data mining is an Infosys offering, Infosys Enterprise Data Privacy Suite (iEDPS), which is a data privacy solution present in the market for over 10+ years.

iEDPS Product Details

Infosys Enterprise Data Privacy Suite (iEDPS) is a patented enterprise-class data privacy suite which will enable users to protect and de-risk sensitive data. iEDPS is a one-stop solution for the protection of confidential, sensitive, private, and personally identifiable information within enterprise repositories. It supports various databases like Oracle, SQLServer, etc., and various file types like Delimited, Fixedlength, XML, JSON, etc. iEDPS has many functionalities to identify and protect sensitive fields/data. Some of them are below:

• iEDPS identifies the sensitive fields (Discovery)
• Users can mask the data (sensitive fields) and subset it
• Data generation
• iEDPS supports more than 180+ algorithms to mask sensitive fields like encryption [MYP1] and deterministic lookup file-based algorithms
• Supported with various static and dynamic masking capabilities inbuilt

iEDPS is an easy-to-use data privacy protection that helps in automating data protection and privacy across an enterprise.

How iEDPS Helps in Data Mining

iEDPS supports the data mining feature, which is query-based. Users can create a connection to any supported database and build a template in the format of a query. Users can prepare multiple query-based templates, i.e., queries to retrieve the data, based on the criteria they need. All these templates are stored in iEDPS and can be used by the testers to retrieve the data by directly executing the template. The results will give you the filtered data.

This will result in below:

• Users/Testers don’t need to search the entire set of huge data. Instead, they can run the pre-made templates and get the correct data that is required.
• Increased self-service
• Reduces effort spent on test data preparation
• Removes dependency on personnel

Based on the above observations using iEDPS, its data mining feature will help the end user with the test data preparation and reduce the effort involved.