Overview of Data Profiling

Tabel of the content

• How Does Data Profiling Strengthen an Organisation?
• Data Profiling Elements
• Types of Data Profiling
• Techniques of Data Profiling
• Advantages of Data Profiling
• Best Data Profiling Tools
• Conclusion

Data profiling is inspecting and analysing a dataset to gain valuable insights regarding its content, structure, accuracy, and quality. By verifying the completeness of datasets in terms of criteria or anomalies/errors found within them, companies can make better decisions about how they use their data going forward.

When data-driven choices are essential to the success of a business, data profiling can be an invaluable tool. It helps identify patterns and trends that could quickly go overlooked and examines structured and unstructured data such as customer records, sales information, and financial statements.

With techniques like validation processes, exploration strategies and analysis tactics at its disposal, this process is one any forward-thinking enterprise should seriously consider incorporating into its operations.

How Does Data Profiling Strengthen an Organisation?

Data profiling is essential for organisations looking to optimise their data usage. It helps to identify areas of improvement in the quality and accuracy of the data and any anomalies or errors that could lead to incorrect conclusions.

Data profiling is also vital for organisations looking to comply with regulations and standards, as it helps to ensure the integrity of their data. Organisations can gain insight into their data's quality, accuracy, completeness and integrity by utilising data profiling techniques while ensuring compliance with applicable regulations and standards.

Data science certification courses are designed to provide professionals with the skills and knowledge they need to excel in this rapidly growing field. They cover many topics, including data exploration, clustering techniques, data visualisation, natural language processing (NLP), deep learning, network analysis and more.

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Data Profiling Elements

Data Discovery: Using data profiling, analysts can identify patterns and relationships in their datasets to decide what data is needed for a project.

Data Quality Assessment: Data profiling allows analysts to assess data quality by identifying missing values, duplicate records, outliers, etc. This helps ensure that the data used in a project is valid and accurate.

Data Distribution: Analysing data distribution helps analysts best understand how to use that data in their projects. It helps them identify trends and outliers to target specific market segments or customer bases.

Identify Interrelationships: Data profiling helps identify relationships between different data points so analysts can better understand how they relate.

Measure Variability: Analysing data variability allows analysts to identify the essential variables in a given situation and focus their efforts on those areas.

Types of Data Profiling

Structure discovery: It is a process of verifying data integrity and accuracy. It ensures that information follows precise guidelines and remains consistent in its format, making it easier to detect inaccuracies or errors. Additionally, this technique can spot any redundant data, which may need further examination during the analysis phase - another name for structure discovery.

Through careful analysis of the data structure, anomalies such as missing fields or incorrect data types can be quickly identified. By mathematically evaluating

Descriptive Statistics and other technical checks like sum, minimum, maximum and more via this profiling process allow understanding of the underlying relationships between items in the dataset and uncover patterns that could prove beneficial for analysis and decision-making.

Relationship Discovery: This Discovery is an innovative data profiling tool that exposes the hidden connections between entities and their associated information. This analysis can discover undiscovered patterns and correlations, allowing businesses to understand customer behaviour better or find new growth opportunities.

In addition, companies can utilise this technology to detect any fraud within their system, as it helps identify unusual activity in existing data sets. Relationship discovery can scan and analyse immense amounts of data to recognise relationships between different pieces that may not be obvious.

Example: An e-commerce company wants to understand its customer activity better. Using Relationship Discovery, they can analyse purchase data and detect correlations between customers, products, website interactions, promotional initiatives, and other factors.

By doing so, they can get an in-depth look at how their customers behave and create strategies for increasing sales.

Content Discovery: It is an invaluable data profiling resource. It assists in finding peculiarities within a dataset by thoroughly examining every record. This approach can detect mistakes like incorrect phone numbers or missing area codes that would have gone unnoticed.

When dealing with massive datasets, it benefits immensely because it rapidly spots any systemic problems embedded deep into the information, which may be challenging to identify manually. Moreover, this technique helps reduce erroneous downstream analysis since invalid records get eliminated from evaluation instantly.

Also read : What is Data Mesh?

Techniques of Data Profiling

• Column profiling is a method of evaluating individual columns of data to count the number of values in each column. It helps in pattern determination.
• Cross Column Profiling includes key analysis and dependency analysis.
• Through Key Analysis, we can search the values in a table to locate and isolate potential Primary Keys. Uncovering connections among cells in a single table, Dependency Analysis is an essential process for discerning the dependent relationships hidden within data sets.
• Cross-table profiling helps in scanning across tables to discover all potential Foreign Keys. In addition, cross-table profiling can detect and assess the similarities and discrepancies between data types and syntaxes of various tables.

Advantages of Data Profiling

Improved data credibility

Data profiling helps identify errors and inconsistencies in the data, which can improve credibility. This is especially useful when running analytics or reporting on data.

Better sorting and searchability

Data profiling allows for efficient sorting and searching of data sets. Staying out the data in a standardised format makes searching for specific items or parameters easier. This makes it easier to find relevant information quickly.

Proactive decision making

Data profiling offers a comprehensive view of the data, which helps identify patterns and trends. This enables more informed decision-making based on accurate data.

Best Data Profiling Tools

• SAP Business Objects Data Services (BODS)
• Informatica Data Explorer
• DataFlux Data Management Server
• Talend Open Studio

SAP Business Objects Data Services (BODS): It is an enterprise-grade data profiling tool that can quickly discover, analyse, and cleanse large volumes of data. It offers many features, including identifying patterns and outliers in the data, detecting duplicate records, and validating data accuracy.

Informatica Data Explorer: It is a powerful data profiling tool for big data projects. It can analyse vast amounts of structured and unstructured datasets from multiple sources and provides powerful insights into the data. It also offers various features, such as automated profiling, data exploration, and integration with machine-learning algorithms for advanced analysis.

DataFlux Data Management Server: This tool is a full-featured data profiling solution that can discover, visualise, and cleanse large volumes of data. It provides a range of features, such as data classification, profiling analysis, outlier detection, and more.

Talend Open Studio: Talend Open Studio is a free, open-source data profiling tool that can quickly identify quality issues in datasets. It offers an intuitive graphical interface and powerful features, including automated profiling and anomaly detection. Additionally, it supports a range of database technologies, making it easy to integrate with existing enterprise systems.

These data profiling tools require prior data science training to be set up and used effectively. That said, they are comprehensive solutions that can provide a great deal of insight into the quality and completeness of the data.

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Conclusion

Data Profiling is an excellent method for unlocking the potential of your data sets. With increased attention to internal structures and exploration of insights, you can ensure that accuracy and consistency are paramount. As a result, analytical reporting will be more dependable as it relies on up-to-date evidence, ensuring that accurate information always informs your decisions.

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