Digital Digest — Data Profiling

Organisations in today’s data-driven world are faced with a massive volume of data that is constantly expanding. But maintaining the data’s integrity, completeness, and consistency is difficult given its enormous volume. Data profiling is a strategy that enables businesses to assess the quality of their data and take the necessary measures to enhance it. We shall examine the fundamentals of data profiling, as well as its advantages and methods, in this post.

What is Data Profiling?

The practice of examining and evaluating data from many sources to comprehend its structure, substance, and quality is known as data profiling. It entails going over the data to look for discrepancies, mistakes, redundant information, and other irregularities. Organisations can acquire insights into their data and find possible problems by using data profiling. Each endeavour to improve data quality must start with this procedure.

Why is Data Profiling Important?

Data profiling is crucial since it aids businesses in ensuring the accuracy of their data. Bad business decisions, higher operating costs, and missed opportunities can all be a result of poor data quality. For instance, if a customer’s name is spelt incorrectly in a database, it may cause communication problems that lower customer satisfaction. These kinds of problems are easier to spot thanks to data profiling, which also gives rise to a chance to fix them.

Benefits of Data Profiling:

1. Data profiling assists in finding discrepancies, errors, duplicates, and other irregularities in the data, which results in better data quality. Organisations may improve the quality of their data by solving these problems, which will result in better decision-making, greater efficiency, and higher customer happiness.
2. Data profiling gives firms insights into their data, which can aid in improved decision-making. Organisations can spot patterns, trends, and anomalies that can help them make decisions by analysing the data’s structure, substance, and quality.
3. Enhanced Efficiency: Businesses can save time and effort by managing their data more efficiently by improving the quality of their data. As a result, production and efficiency both increase.
4. Increased Customer Satisfaction: Data profiling aids in locating problems with customer data, including duplication, inconsistencies, and misspellings. Organisations can increase customer data accuracy and raise customer satisfaction by solving these problems.

How is Data Profiling Done?

Data profiling involves the following steps:

1. Data Gathering: Gathering data from diverse sources is the initial stage in data profiling. This can refer to spreadsheets, databases, flat files, and other sources.
2. Data analysis identifies the data’s structure, content, and quality once it has been gathered. Finding patterns, connections, and abnormalities in the data is a part of this process.
3. Data cleaning: Following data analysis, the data is cleaned to eliminate any discrepancies, mistakes, duplications, and other anomalies.
4. Data Validation: Validating the data to make sure it complies with the organisation’s needs is the last step in data profiling. Verifying the data’s accuracy, completeness, and consistency falls under this category.

The Downsides

Although data profiling is a crucial procedure for guaranteeing the quality of data, there are also potential drawbacks to take into account, such as:

1. Time-consuming: When working with huge datasets, data profiling can be a time-consuming operation. Companies must devote enough time and money to the process, which can require a large investment.
2. Costly: Establishing a data profiling process can be costly, particularly if a business needs to spend money on specialist equipment and personnel to complete the task successfully.
3. Limited Applicability: Data profiling has drawbacks and cannot find all instances of poor data quality. It may not be able to discover problems that are concealed or challenging to recognise because it can only identify problems that are obvious in the data.
4. Data privacy issues can arise because data profiling entails assessing information from a variety of sources. Businesses must make sure that they are securing sensitive data and adhering to privacy laws.
5. False Positives: When using data profiling, it’s possible to find problems that aren’t truly there in the data. As businesses attempt to handle issues that don’t exist, this can result in a waste of time and resources.

Our Solution

To combat the cons of data profiling while establishing an alternative secure data processing network on which individuals — also referred to as Data Providers — can manage, store and monetise their data.

Our proposed structure includes the use of the Digital Twin technology combined with a Data Vault that ensures the quantum-safe storage of personal information on an autonomous cloud infrastructure.

By enabling data processing and self-custody, we will be able to provide businesses — also referred to as Data Users — with the tools they need to execute their analytics, identify consumer trends and host truly personalised advertising models while the Provider receives fair and accurate compensation in return.

Conclusion

In conclusion, each organisation that works with a lot of data has to do data profiling. It aids businesses in ensuring the consistency, precision, and accuracy of their data, which promotes better judgement, greater effectiveness, and more customer satisfaction. To ensure the data satisfy the company’s needs, data profiling includes gathering, reviewing, purging, and validating the data. Businesses may increase the quality of their data and accomplish their objectives by putting in place a data profiling procedure.

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External References:

1. “Data Profiling.” IBM. https://www.ibm.com/analytics/data-quality/data-profiling
2. “What is Data Profiling?” Talend. https://www.talend.com/resources/what-is-data-profiling/
3. “Data