What is Data Profiling? Data Profiling Tools, Practices, and Techniques

Have you ever started a data project only to realize that your datasets contain inconsistencies, missing values, or duplicates? Poor data quality can derail data migration, master data management, or analytics initiatives, leading to inaccurate insights and costly errors. This is where data profiling comes in.

Data profiling provides a structured approach to understanding your datasets before you use them. It offers a comprehensive overview of data contents and statistics, helping organizations assess the quality and usability of their data.

What Is Data Profiling?

According to Gartner,

Data profiling is a technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness. This is accomplished by analyzing one or multiple data sources and collecting metadata that shows the condition of the data and enables the data steward to investigate the origin of data errors. The tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats.

Essentially, data profiling serves as a data hygiene process, resulting in a collection of metadata that reflects the overall health of your data. Key aspects of data profiling include:

• Data types: Are the values in a column numbers, text, or dates?
• Value ranges: What’s the minimum and maximum value a field can hold?
• Missing values: How many data points are missing in a specific column?
• Data distributions: How are the values distributed across a column?
• Data relationships: Are there any connections between different data points or columns?
• Data quality issues: Are there any inconsistencies, duplicates, or errors present?

By leveraging data profiling tools and techniques, businesses can proactively detect data issues and improve data quality, ensuring reliable analytics and decision-making.

Using data effectively requires a deep understanding of its content, quality, and structure before it’s integrated into applications like cloud data warehouses, CRM, or ERP systems. Unfortunately, projects often fail or take much longer than expected because they rely on outdated or inaccurate information about the data. This can waste a lot of time and effort, and ultimately put the project at risk.

The sheer complexity of databases, the massive amounts of data involved, and the difficulty of manually understanding source data all make data profiling a crucial step.

Data profiling helps ensure high-quality and reliable data by allowing businesses to verify its characteristics, identify any quality issues, and make sure it meets both statistical standards and their own specific business rules. By performing data profiling regularly, companies can build a strong foundation for data governance, ensuring their data is always reliable and trustworthy.

Types of Data Profiling

Data profiling employs three main techniques: structure discovery, content discovery, and relationship discovery. While their methods vary, the goal is the same—enhancing data quality and understanding your data assets.

Structure Discovery

Structure discovery examines how data is formatted and organized. It’s like taking a census of your dataset, identifying field types and their content. This ensures consistency—e.g., a phone number field shouldn’t contain text or an incorrect number of digits.

Techniques like pattern matching help detect formatting inconsistencies, such as missing digits in phone numbers. Basic statistical analysis (e.g., minimum, maximum, and average values) helps validate data accuracy. For example, an age field with a minimum value of -5 would be unrealistic. By combining these methods, structure discovery ensures consistency and correctness, forming a solid foundation for analysis.

Content Discovery

Content discovery focuses on examining individual fields to assess data quality. It identifies errors and determines systemic issues within datasets.

For instance, in a user database, content discovery would highlight the percentage of phone numbers missing area codes. This process improves understanding by pinpointing gaps and inconsistencies in data formatting, standardization, and integration.

Key content profiling techniques include:

• Statistical summaries: Analyzing minimum/maximum values in numerical fields and frequency distributions in categorical fields.
• Null values and uniqueness: Checking for nulls, blanks, and unique values to gauge completeness and relevance.
• Systemic errors: Detecting issues like misspellings or inconsistent formats (e.g., “Doctor” vs. “Dr.”).

Relationship Discovery

While structure and content discovery focus on individual datasets, relationship discovery analyzes connections between different data sources. For example, separate department datasets may share a common identifier, like a customer ID.

This process examines metadata to uncover links between database tables, spreadsheet references, or formulas aggregating data from multiple sources. Understanding these relationships improves data warehouse efficiency and ensures data transformation aligns with business needs.

By leveraging these three techniques, organizations gain a clearer picture of data quality, making it easier to manage and optimize their datasets.

Step 3: Standardizing

After you figure out what you have and how to find it, the next step is standardizing. Ensure that similar data matches across tables and databases. This includes standardizing formats, such as converting dates from different formats to a standard format.

Step 4: Cleansing

Data cleansing takes the next step by fixing formatting errors to match your new standardization rules. It also tackles bad data, removing corrupt or completely worthless entries. By following robust data profiling practices and maintaining backups, you can minimize the risk of further data loss during this process.

Step 5: Continuous Improvement

Fixing data issues in isolated applications provides only temporary relief. A long-term solution requires an enterprise-wide strategy, involving IT and business teams.

Empowering data stewards and analysts fosters data ownership, maximizing the value of reliable data. Profile comparisons over time help track data quality improvements and ensure ongoing data health.

DQLabs’ offers in-place profiling techniques with one of the fastest and deepest pushdown options to all modern data stack systems. Interested to know more? Let’s chat!

Metadata Discovery

Besides the traditional classification of profiling tasks, the below are the common ways to discover metadata for profiling:

• Automatic Data Flow: Automatically maps how data moves through systems, finding connections between tables and columns.
• Column Profiling: Counts how often each value appears in a specific column, revealing data frequency and patterns.
• Cross-Column Profiling: Analyzes values across multiple columns, identifying potential primary keys and dependencies between data points within a table.
• Cross-Table Profiling: Examines relationships between columns in different tables, helping detect redundancies and connections.
• Data Rule Validation: Data rule validation checks if data adheres to predefined rules, ensuring data quality.

Benefits of Data Profiling

While data profiling comes with challenges—handling large data volumes and fragmented storage—it offers undeniable benefits.

Enhanced Clarity and Collaboration

Data profiling works alongside data catalogs, which store metadata like definitions, lineage, and ownership. Linking metadata with profiling details provides a comprehensive view of data assets. This fosters collaboration, enabling stakeholders to share insights and improve data governance.

Streamlined Workflows and Better Decisions

Data profiling tools automate discovery and metadata management, eliminating manual tasks. This frees data professionals to focus on strategic initiatives while ensuring cleaner, more accurate data for better decision-making.

Organized Data Landscape and Future Planning

Databases often contain overlapping or scattered information. Data profiling traces data to its source, ensuring proper organization and eliminating confusion. Analyzing data relationships helps organizations set realistic goals and improve long-term planning.

Best Practices for Data Profiling

The key initial step is to utilize data profiling as a tool. Data experts frequently overlook its benefits when addressing quality issues or developing new data products. Once you’ve decided to implement profiling, you should:

• Focus Your Efforts: Define the scope and specific data assets you want to analyze. This ensures targeted profiling.
  
• Set Clear Rules: Establish data profiling rules to analyze the right elements, focusing on completeness, consistency, accuracy, and validity.
  
• Go Beyond Averages: Use multiple techniques like statistical analysis and pattern matching for a deeper understanding of your data.
  
• Validate Your Findings: Compare your results to expected outcomes and identify potential errors in your profiling process.
  
• Collaborate for Accuracy: Share results with your team and incorporate feedback from data stewards to refine your analysis and build trust in the data.

Your profiling technique will largely depend on your use case and objectives. After reviewing the basic profile, consider the following questions to determine your next steps:

1. Do you need to explore cross-column profiling to identify deeper relationships?
2. Should you define and add a data quality rule to the profile to enforce data quality?
3. Do you need to find overlap between two tables and use cross-table profiling?
4. Is adding metadata necessary to organize information within a data lake?
5. Do you need to ensure a smooth data migration from one system to another?

A good data profiling will help you answer the following questions:

1. Is our data good enough to finish the project on time and budget?
2. Does it align with our business needs?
3. Cost-effective to maintain and use?
4. Accurate and reflects real business needs?
5. Can data elements work together for our goals?
6. Can we create reports with this data?
7. What data needs cleaning or transforming?
8. Will the final data be reliable?

Use Cases for Data Profiling

1. Building Reliable Data Products

Before building models or analyzing data, data profiling ensures your foundation is strong. It reveals data types, formats, and potential issues, saving engineers time and improving data product quality.

2. Streamlining Data Integration

When merging datasets, understanding their relationships is crucial. Data profiling helps identify connections and inconsistencies, enabling smoother integration and accurate insights.

3. Optimizing Queries

Data profiling creates detailed statistics about your data, allowing databases to optimize queries and return results faster. This translates to quicker access to valuable information.

4. Strengthening Data Governance

Data governance encompasses data quality, security, and compliance. Data profiling helps organizations understand their data landscape, including data lineage and ownership. This helps data stewards to develop effective data policies and ensures data is used responsibly.

5. Resolving Data Quality Issues

Data profiling acts as a detective, pinpointing anomalies and data inconsistencies quickly. This allows data professionals to identify and fix problems efficiently, avoiding time-consuming “data hunts.”

6. Building and Managing Data Pipelines

Data profiling empowers data engineers by revealing data types, rules, and cleaning needs. This information helps them build efficient data pipelines and cut development time in half.

7. Facilitating Data Migration

Data profiling plays a crucial role in planning and executing data migrations. By assessing data conditions early on, developers can predict effort levels and ensure smooth transitions to new systems.

Live Examples

Following are some of the examples of companies who leveraged the advantages of data profiling to improve their operational efficiency:

Office Depot

Operating both online and in-store, Office Depot relies on seamless data integration. Data profiling ensures high-quality data across all channels – offline catalogs, websites, and call centers. This complete 360-degree customer view empowers them to tailor offerings and optimize back-office functions.

Globe Telecom

Globe Telecom, a telecommunications giant in the Philippines, needed to maximize the value of existing customers. However, data quality challenges hindered this goal. Manual data scoring, using spreadsheets and offline databases, proved insufficient. Data profiling and cleansing solutions helped Globe establish a data center of excellence. This resulted in daily data quality scores (previously monthly), a 400% increase in trusted email addresses, and a significant boost in marketing campaign ROI with cost reductions, improved conversion rates, and higher click-through rates.

Conclusion

Data profiling is no longer an optional step – it’s a fundamental requirement for any organization seeking to get the most out of their data. By providing clarity on data content, relationships, and quality, data profiling streamlines workflows, facilitates better decision-making, and lays the groundwork for strong data governance procedures.

Experience DQLabs’ on-the-fly profiling firsthand. Schedule a personalized demo today.