Before you migrate data, build a master model, create a new architecture, or start a data quality program, you need to understand your datasets. Data profiling shows how your data is structured. It provides an overview of the contents and statistics of your datasets, helping you understand what’s in them and how they can be used.

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 information about data, also known as metadata and its overall health. This could include:

• Data types: Are the values in a column numbers, text, dates, etc.?
• 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?

Why do you Need to do it?

If you jump into a data project without first understanding your data, you’re setting yourself up for failure. The truth is, only about 3% of a company’s data is actually considered high quality. Data profiling helps you tackle these problems head-on by giving you a clear picture of your data before you start using it.

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 Unfortunately, projects often fail or take much longer than expected because they rely on outdated or inaccurate information about the data.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 the specific methods differ, the overall goal remains consistent – to enhance data quality and gain a deeper understanding of your data assets.

Structure Discovery

Data profiling starts with understanding your data’s structure. This initial step, called structure discovery (or analysis), examines how your data is formatted and organized. It’s like taking a census of your data sets, identifying the number and type of fields within each dataset, along with their content. This process ensures consistency – for example, a phone number field shouldn’t contain text or an uneven number of digits.

Structure discovery utilizes various techniques to achieve this. Pattern matching helps identify inconsistencies in formatting, like missing digits in phone numbers. Additionally, basic statistical analysis like minimum, maximum values, and means are employed to assess the data’s validity. Imagine a field containing ages: a minimum value of -5 wouldn’t be realistic. By combining these methods, structure discovery ensures your data is consistent and formatted correctly, laying the foundation for further analysis.

Content Discovery

Content discovery involves examining each database’s individual fields and elements to check the contents and quality. It involves looking into individual data records to identify errors and determine which rows in a dataset contain problems or systemic issues.

For example, in a database containing user contact numbers, content discovery would identify the percentage of phone numbers lacking area codes. This process helps to better understand data by highlighting gaps and errors from both cognitive and visual perspectives. Content discovery focuses on data quality, requiring data to be formatted, standardized, and properly integrated with existing data efficiently.

Here’s a closer look at content profiling:

• Statistical summaries: Analyze minimum/maximum values for numerical fields and the frequency of values in categorical fields. This provides insights into the data range and quality.
• Null values and uniqueness: Check the number of null values, blanks, and unique values to understand data completeness, quality, and a field’s relevance.
• Systemic Errors: Look for systemic errors like misspellings or inconsistent value representations (“Doctor” vs. “Dr.”), which can disrupt analysis.

Relationship Discovery

Unlike structure and content discovery, which focus on individual datasets, relationship discovery explores how your data connects across different sources. Imagine seemingly unrelated data sets from different departments sharing a common field – a customer ID for example.

This process involves analyzing metadata – information about your data – to identify key relationships, like connections between tables in a database or references between spreadsheet cells. It could be as simple as a formula referencing another cell, or complex like a table aggregating sales data from multiple sources. By figuring out these connections, relationship discovery helps you understand what data is actively used and how it interacts. This not only improves data warehouse efficiency but also helps identify areas where data might need transformation for better alignment and overall effectiveness.

How to do Data Profiling?

Data profiling aims to create a complete picture of your data’s current state. This includes accurate metadata and comprehensive metrics, allowing you to understand the data’s true content and quality. Following the below steps will help your data analysts take necessary actions to ensure the data is fit for its intended purpose in the target system.

Step 1: Preparation

Data profiling starts by prepping the data source for analysis. Tools like DQLabs can handle massive datasets (hundreds of millions of rows) from various sources, including Amazon S3, Snowflake, Google BigQuery, ODBC, Oracle, Salesforce, and more.

Step 2: Data Discovery & Profiling

This phase focuses on three key areas: structure, content, and relationships. By analyzing these aspects, you can map out your existing data landscape. While some connections might not be immediately clear, this initial phase provides a baseline for your data profiling task.

Profiling your data is like creating a comprehensive report card for each dataset. This report details the data’s content, including data types, missing values, and the number of entries. It also helps you prioritize data storage. By understanding which data is most frequently used, you can ensure it’s readily accessible in higher-cost storage, while less critical data can be stored in lower-cost options.

With data profiling tools you can analyze your data to reveal its content, structure, and overall quality. Users can then review these findings to see if the data aligns with their business needs.

Modern tools like DQLabs offer a wide range of data quality checks, providing both basic and in-depth profiling capabilities. DQLabs’ Data Profiling gives you the flexibility to filter and drill down on specific records for better detection of problems. Additionally, tools like these will allow data analysts to perform “What-If” scenarios by integrating profiling with data quality rules such as cleansing, standardization, and parsing functionalities.

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

While some organizations address data quality issues with isolated fixes within specific applications or processes, these tactical solutions often fall short. This fragmented approach offers temporary relief for specific areas but fails to achieve lasting improvements. To truly overcome data quality challenges, a comprehensive, enterprise-wide approach is necessary. This strategy involves collaboration between IT and business teams.

Empowering data stewards, business analysts, and line-of-business managers encourages a culture of data ownership. This allows businesses to maximize the value of reliable data. Additionally, users can leverage profile comparisons over time to 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!

Techniques to do Data Profiling

Single Field Profiling

This basic technique treats each data field in isolation. Think of it like inspecting individual items in a cluttered room. 

It reveals:

• Summary Statistics: Counts data entries and calculates basic statistics like minimum, maximum, and average values.
• Data Types: Identifies if the data is categorical (like gender), continuous (like temperature), or follows specific formats (dates, timestamps).
• Data Values: Shows characteristics and patterns within data, like identifying invalid entries or verifying data against business rules.
• Distributions: Visualizes how data is spread out, helping identify outliers or unusual patterns.

Multi-Field Profiling

This technique goes beyond individual fields and explores relationships between them. Imagine finding connections between items in your room. 

It reveals:

• Relationships: Discovers if values in one field depend on or limit values in another.
• Numerical Relationships: Visualizes connections between numerical fields using graphs and heatmaps, highlighting potential correlations.

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 presents challenges – large data volumes necessitate specialized tools and trained analysts, and fragmented storage across departments adds another layer of complexity – the benefits are undeniable.

• Enhanced Clarity and Collaboration: Data profiling goes hand-in-hand with data catalogs. These catalogs act as central repositories for metadata, including data definitions, lineage (data origin), and ownership. Linking this metadata with data profiling details allows users to gain a more comprehensive understanding of their data assets. This fosters collaboration as key stakeholders can readily share information about data quality and establish robust data governance practices.
• Streamlined Workflows and Better Decisions: Data profiling tools eliminate manual tasks like data discovery and metadata management. By automating these processes, data intelligence platforms free up valuable time for data professionals. This allows them to focus on more strategic initiatives, while data profiling itself helps identify and address data quality issues. This results in cleaner, more accurate data, which enhances decision-making throughout the organization.
• Organized Data Landscape and Future Planning: Databases often contain overlapping or disparate information. Data profiling acts like a detective, tracing data back to its source and ensuring proper organization and encryption. This eliminates confusion and simplifies data management. Additionally, by analyzing the relationships between available, missing, and required data, organizations can gain valuable insights to chart their future strategies and set achievable long-term goals.

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:

• Do you need to explore cross-column profiling to identify deeper relationships?
• Should you define and add a data quality rule to the profile to enforce data quality?
• Do you need to find overlap between two tables and use cross-table profiling?
• Is adding metadata necessary to organize information within a data lake?
• 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.
8. Master Data Management (MDM): Implementing MDM involves integrating data from various sources. Data profiling helps identify all systems involved, estimate effort needed, and create cross-reference tables for faster deployment.

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.