How to Utilize Data Profiling for Improved Insight 

Data is regarded as ‘OIL’ in today’s day and age. The value it holds is impeccable. However, it is of no surprise that it is ‘ refined data’ where the soul of the resource lies. There are multiple ways of treating raw, unprocessed data. Different methods of analysis are applied according to the nature of the task at hand. No ‘one size fits all’ approach can be picked up when handling large amounts of data. 

Data is collected by organizations of all sizes. It is rather necessary for all kinds of organizations! The velocity at which data is collected from users and consumers is extreme and ever-growing. With advancements in technical aspects, the collection and storage of such volumes of data is now made possible. Cloud storage is deployed to deal with the storing of enormous amounts of data and enhance its processing powers. 

The first step in processing data is to understand what the expected result is. From this, a relevant form of cleaning up the noise and selecting appropriate data can be identified. This is done to further analyze the data. Data analysis is an extremely important component when dealing with any kind of database. It helps organizations and businesses gain insights and learn about patterns and performance. 

A popular technique and method used is of ‘data profiling’. It deals with and manages data at source and as it progresses further. The database a company has is the building block, the spine, and the foundational layer for awareness creation, quality lead generation, and nurturing and progression efforts of the business. While reviewing the data at source, it also understands its structure and its potential in regards to the active project. Data profiling, when opted for, unfolds its crucial aspects, such as migration projects and data conversion, data quality projects at source, and Data warehousing and business intelligence (DWBI). 

Data profiling can be summarized as the process of examining and analyzing data available in a data source to gather information about its quality, structure, and content. The main aim of data profiling can be understood as the ability to comprehend the data and identify any issues related to its accuracy, completeness, consistency, and uniqueness. Based on the analysis, data profiling then helps to generate statistical summaries and reports that can be used to improve data quality and make better business decisions. 

Through the years, various paths for data profiling have been developed. However, a few popular approaches are used globally. The three widely popular types are: 

• Structure discovery 
• Content discovery 
• Relationship discovery 

Each of these serves a unique purpose and targets the different aspects that come along through the journey of data profiling. 

1. Structure profiling involves analyzing the structure of the data, such as data types, metadata, and schema. This helps in identifying any inconsistencies or errors in the database or structure. 
2. Content profiling involves analyzing the actual data values to determine their quality, accuracy, completeness, and consistency, while simultaneously helping to identify any anomalies or issues with the data content. 
3. Relationship profiling is analysing the relationships between data elements, such as foreign keys, primary keys, and dependencies. This subset helps in identifying any inconsistencies or errors in the data relationships. 

Each of these approaches is important to gain a complete understanding of the data and to ensure that it is fit for use. Noisy and irrelevant data can increase cycle time, reducing efficient outcomes. By combining all three approaches, data profiling can provide a comprehensive view of the data, which is vital for making informed decisions. 

Data profiling can be done on any type of database, including relational databases, NoSQL databases, and data warehouses. The process involves analyzing the data at different levels, starting with the database schema and metadata, and then moving on to the actual data values. 

Some of the common data profiling techniques used for data profiling in databases include column profiling, cross-column profiling, and cross-table profiling. These techniques help in identifying quality issues, such as missing or inconsistent data, duplicate records, and incorrect data types or formats. By analyzing the data at different levels and using various profiling techniques, data profiling can provide a comprehensive view of the data, which is essential for making informed decisions.