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Data Profiling: Types, Techniques and Best Practices

Mariam Anwar

Product Marketer

January 31st, 2024

Clean and accurate data is the foundation of an organization’s decision-making processes. However, studies reveal that only 3% of the data in an organization meets basic data quality standards, making it necessary to prepare data effectively before analysis. This is where data profiling comes into play.

It provides organizations with a comprehensive overview of errors and inconsistencies in their data. This insight enables them to promptly rectify issues, make informed decisions, and enhance operational efficiency.

Let’s dive into the specifics of data profiling and how it helps in data preparation.

What is Data Profiling?

In simple terms, data profiling ensures that the data is in good health and fit for its intended use.

Data profiling is essentially the first step in the process of managing and using data. It’s a method used to diagnose the data’s health by thoroughly examining its structure, content, and relationships. It ensures that the data is accurate, consistent, and unique before it’s used for ETL and data analytics.

Data profiling can also uncover a range of data quality issues, such as missing data, duplication, and inaccuracies. It can also highlight patterns, rules, and trends within the data. This information is crucial as it helps organizations improve data quality, streamline data transformation, and make informed decisions.

Data profiling in Astera Data Stack

Types of Data Profiling

Data profiling can be classified into three primary types:

  1. Structure Discovery: This process focuses on identifying the organization and metadata of data, such as tables, columns, and data types. This certifies that the data is consistent and formatted properly. For instance, in a healthcare database, structure discovery reveals the presence of tables like “Patients” and “Appointments” with columns such as “PatientID,” “AppointmentDate,” and data types like “integer” and “date.”
  2. Content Discovery: This involves a deep dive into the actual content of the data. It examines individual data records to identify errors. For example, in a customer database, content discovery reveals that the “Phone Number” column contains numerous missing values, highlighting incomplete contact information for certain customers.
  3. Relationship Discovery: This process identifies the relationships and dependencies between different data elements. For instance, in a retail database, relationship discovery would analyze the associations between different fields and tables, such as the relationship between the ‘Customers’ table and the ‘Orders’ table, understanding how different data elements are interconnected and how they influence each other.

Data Profiling Techniques

Profiling data encompasses a variety of techniques that help analyze, assess, and understand data. Some of them are:

  1. Column Profiling: This technique analyzes each column in a database. It looks at the type of data in the column, how long the data is, and if there are any empty values. A crucial part of this process is frequency analysis, which counts how often each value appears, helping to spot patterns and unusual values.
  2. Cross-Column Profiling: Here, the focus is on the relationships between different columns within the same table. It includes key and dependency analysis. Key analysis finds columns where each row has a unique value, while dependency analysis looks at how values in one column depend on values in another column. This can help find connections, overlaps, and inconsistencies between columns.
  3. Cross-Table Profiling: This method looks at relationships between different tables in a database. It includes foreign key analysis, which finds columns in one table that match up with unique key columns in another table. This helps show how data in one table is related to data in another table and can provide important information about the structure and accuracy of the database.
  4. Data Validation: This approach involves verifying the accuracy and quality of data against specific criteria or standards. It includes format checks, range checks, and consistency checks to ensure data is clean, correct, and logically consistent.

Understanding the Difference: Data Profiling vs. Data Mining

Data profiling and data mining are two distinct processes with different objectives and methodologies.

Data profiling is the initial step in data preparation, focusing on understanding the data’s basic characteristics, quality, and structure. It helps identify data issues like missing values or anomalies. This helps ensure that data is clean and reliable for further use.

In contrast, data mining involves exploring the data to discover hidden patterns, trends, and valuable insights using advanced techniques like machine learning. It’s the process of extracting meaningful information from the data. Data mining is a valuable tool for predictive modeling, anomaly detection, and business intelligence.

Aspect Data Profiling Data Mining
Purpose Assess data quality and characteristics Discover patterns, trends, and insights
Goal Understand data structure and cleanliness Extract valuable information and knowledge
Methods Basic statistical analysis, data type identification, anomaly detection Advanced techniques like machine learning, clustering, classification
Use cases Data preparation and cleansing Predictive modeling, anomaly detection, business intelligence

Data Profiling Benefits

Data profiling offers a multitude of specific benefits that can significantly enhance an organization’s data management strategy. Here are some of the distinct advantages of data profiling:

  • Informed Decision-Making: Data profiling provides a clear understanding of the available data, its quality, and its structure. This knowledge aids in making informed, data-driven decisions, thereby improving strategic planning and operational efficiency.
  • Increased Operational Efficiency: It helps in identifying and eliminating redundant or irrelevant data. This leads to improved efficiency of data processing and analysis, resulting in faster insights, improved productivity, and a better bottom line.
  • Risk Mitigation: Data profiling can help businesses identify potential risks and issues in their data, such as compliance violations or security threats. By addressing these issues proactively, businesses can mitigate risks and avoid costly penalties or damage to their reputation.
  • Cost Savings: By improving data quality and efficiency, data profiling can lead to significant cost savings. Businesses can avoid the costs associated with poor-quality data, such as inaccurate decisions, wasted resources, and lost opportunities.
  • Compliance Assurance: Data profiling can help businesses ensure compliance with industry regulations and standards. By addressing compliance issues, businesses can avoid legal complications and maintain their credibility in the market.

Applications of Data Profiling

Data profiling finds applications in various areas and domains, including:

  • Data Integration and Data Warehousing: Data profiling facilitates the integration of multiple datasets into a centralized data warehouse, ensuring data accuracy, consistency, and compatibility between sources.
  • Data Migration and System Development: Before migrating data from one system to another or developing new software systems, data profiling helps identify potential data issues and ensures seamless data transfer and system interoperability.
  • Data Governance and Compliance: Data profiling plays a vital role in ensuring compliance with regulatory requirements, industry standards, and data governance frameworks, minimizing legal and financial risks associated with data mismanagement.
  • Data Analytics and Business Intelligence: By understanding the quality, structure, and relationships within data, data profiling empowers organizations to generate more accurate insights, make data-driven decisions, and enhance overall business intelligence.

6 Best Practices

When performing data profiling, organizations should follow some best practices to ensure accurate results and efficient analysis:

  • Define Clear Objectives: Clearly define the goals, objectives, and expectations to ensure its aligned with business needs and requirements.
  • Choose Relevant Data Sources: Select relevant data sources based on their importance, relevance, and potential impact on decision-making processes.
  • Establish Data Quality Metrics: Define appropriate metrics and validation rules to assess the quality and accuracy of data based on business requirements and industry standards.
  • Collaborate with Data Stakeholders: Involve data owners, subject matter experts, and stakeholders throughout the data profiling process to gain valuable insights and ensure cross-functional alignment.
  • Document Data Profiling Results: Document and communicate the findings, recommendations, and actions taken during data profiling to facilitate understanding, accountability, and compliance.
  • Regularly Monitor Data Quality: Implement regular data quality monitoring processes to ensure data consistency, accuracy, and compliance over time.

Conclusion

As organizations continue to harness the power of data for competitive advantage, data profiling remains critical in ensuring data quality. By systematically examining and evaluating data, organizations can ensure data accuracy, reliability, and compliance, leading to more informed decision-making and better business outcomes.

To ensure that high-quality data is being used for analysis, it’s crucial to invest in advanced data profiling tools.

Astera stands out as a comprehensive solution that offers advanced data profiling, cleansing, and validation capabilities. It provides real-time health checks that continuously monitor your data quality as you work, providing immediate feedback on its overall health.

Astera’s capabilities extend to both global and field-level data analysis, enabling early identification of irregularities, missing values, or anomalies. This proactive approach to data quality allows for timely measures to be taken to rectify any issues.

Astera’s drag-and-drop visual interface empowers business users to examine and evaluate the data, facilitating necessary adjustments as needed. Therefore, Astera simplifies the data profiling process and enhances data accuracy, reliability, and overall quality, enabling improved operational efficiency and better business outcomes.

Want to learn more about data profiling and how Astera streamlines the entire data prep process? Download your free whitepaper now!

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