What is Data Profiling?

Data profiling is a process that discovers, analyzes and displays the content, structure, quality and consistency of a given set of data. It is an exercise of summarizing and creating a profile of a dataset so someone can look at it and get a bird's eye view of what's in it and how it can be used.  It's very helpful for data scientists and analysts to use before diving into a new dataset to do an analysis, or a data engineer/analytics engineer who is considering how they might want to clean up a dataset.

Data Profiling Meaning

Data profiling is a set of processes and tools used to understand the contents of a dataset. A data profiler will obtain statistics on the content of a dataset, including basic summaries such as count, average, standard deviation, number of missing and empty fields, frequency distributions. Some will have more advanced information such as calculation of entropy for data element values.

Profiling is usually conducted prior to starting a major data migration or integration project. Data profiling helps the data architect understand the environment and make better decisions about how to map from one system to another.

Data profiling can also be included as part of an ongoing data quality monitoring program. A monitoring program will periodically run profiles and compare results with what was previously observed. This can help identify new problems introduced into the system (e.g., by business errors or software upgrades).

Importance of Data Profiling

Data profiling is a process that discovers, analyzes and displays the content, structure, quality and consistency of a given set of data. The main aim of data profiling is to improve data quality. Data profiling can also be used to determine whether the data has been moved to its final destination, and if not, what needs to be done before importing it into the target database. It's an essential first step when creating a data governance framework within your organization, and should be conducted on a regular basis.

Data profiling is the analysis of data from one or more data sources with the aim of understanding its content, structure, and/or quality. The process of data profiling is applied to the whole set of data in a given source, or to a sample of it.

What is the goal?

The goal of data profiling is to discover "data about the data" – that is, metadata:

• the kind of analysis performed by a database designer when designing a database schema
• the kind of analysis performed by an information systems architect when designing an integration between different databases

It is a form of metadata discovery. It's helpful in collecting a "big picture" understanding of the dataset, and such discovery informs higher level decisions when making changes to a dataset, database, or even warehouse.

How to do data profiling?

Data profiling involves a series of structured steps designed to evaluate and improve the quality of data within an organization.

1. ‍Collect Data: This initial step involves gathering the relevant datasets from various sources within the organization.
   1. Data Sources: Data can be collected from databases, data warehouses, flat files, or any other data storage systems.
   2. Scope: Determine the scope of data to be profiled, which may include specific tables, columns, or entire databases.
2. ‍Analyze the Data: This step uses various tools, algorithms, and business rules to evaluate the collected data.
   1. Tools and Software: Utilize data profiling tools such as Secoda or custom scripts.
   2. Algorithms: Apply statistical and analytical methods to assess the data.
   3. Business Rules: Implement business-specific rules to identify deviations or errors in data.‍
3. ‍Document Findings: Recording the results of your analysis is crucial for transparency and future reference.some text
   1. Reports: Create detailed reports that include the results of the data analysis.
   2. Visualizations: Use graphs, charts, and tables to represent findings clearly
   3. Documentation: Write descriptive summaries of key insights, anomalies, and patterns identified during the analysis.‍
4. ‍Establish Data Quality Rules: Based on the findings, define rules and standards to ensure data quality.
   1. Data Quality Metrics: Set metrics for acceptable data quality levels, such as thresholds for missing values or duplicate entries.
   2. Validation Rules: Develop rules to validate data accuracy, consistency, and completeness.
   3. Governance Policies: Create policies for data governance that outline how data should be managed and maintained.‍
5. ‍Monitor Data Quality: Ongoing monitoring is essential to maintain data quality over time.
   1. Automated Monitoring: Implement automated systems to regularly check data against the established quality rules.
   2. Alerts and Notifications: Set up alerts to notify data stewards of any data quality issues.
   3. Regular Audits: Conduct periodic data quality audits to ensure continued adherence to quality standards.

Key Tasks in Data Profiling

Key tasks in data profiling encompass various analytical and evaluative activities that provide detailed insights into the data's structure, quality, and metadata.

• Descriptive Statistics
  • Metrics: Collect basic statistics such as minimum, maximum, mean, median, standard deviation, count, and sum.
  • Usage: These statistics provide a quick overview of the data distribution and can highlight potential anomalies.
• Data Types
  • Identification: Determine the data types (e.g., integer, string, date) and their lengths.
  • Patterns: Identify recurring patterns, such as formats for dates or phone numbers.
• Tagging
  • Keywords: Tag data elements with relevant keywords to facilitate easier search and categorization.
  • Descriptions: Provide detailed descriptions for data elements to enhance understanding and usability.
  • Categories: Organize data into categories or groups based on common characteristics.
• Data Quality Assessment
  • Assessment Criteria: Evaluate data quality based on criteria such as accuracy, completeness, consistency, and validity.
  • Risk Evaluation: Assess the risk of performing joins or other operations on the data, identifying potential issues like referential integrity problems.
• Metadata Discovery
  • Discovery: Identify metadata, such as data source, date of creation, last update, and data owner.
  • Assessment: Evaluate the accuracy and completeness of metadata to ensure it accurately represents the data.

Data Profiling Reports

Data profiling reports present the findings of the profiling process through comprehensive visualizations and metrics, offering a clear overview of the data's quality and characteristics.

• Visualizations: Include various visual representations, such as histograms, pie charts, and scatter plots.
• Tables and Charts: Use tables and charts to display metrics like the number of rows, distinct values, and missing values in columns.
• Summary Statistics: Provide summary statistics and key findings in an easily digestible format.

Overall, detailed data profiling involves a comprehensive and systematic approach to ensure data quality and usability, ultimately supporting better business decisions and operational efficiency.

What are Results Produced by Data Profiling?

Data profiling can produce results in the form of statistics, such as numbers or values in a column, or in tabular or graphical formats. Some basic values that might be profiled include max value, min value, avg value, number of null records, max length, min length, inferred data type, pattern analysis, and range of values.

1. Tagging: This is the practice of providing further context on data with descriptions, categories, and identifying keywords.
2. Summary Statistics: Provides metrics like mean, median, mode, minimum, maximum, and standard deviation.
3. Pattern Recognition: Detects patterns and trends within the data, such as common formats or frequent values.
4. Data Relationships: Reveals relationships and correlations between different data elements.
5. Tabular or Graphical Formats: The results of data profiling can also be presented in tabular or graphical formats.
6. Metadata Generation: Produces metadata that describes the structure, content, and quality of the data.
7. Data Quality Assessment: Identifies issues such as missing values, inconsistencies, and duplicates.

How to analyze metadata for data profiling?

There are four common methods for discovering metadata for the sake of data profiling:

1. Automatic data flow

Where the system automatically determines how data moves through the enterprise by correlating operations on the database to determine which operations change the same columns in different tables (e.g. joins). This process can be assisted by user-specified mapping rules where possible. The results are shown visually as a series of connected boxes representing tables and columns with lines indicating joins or other associations between them.

2. Column profiling

Observing the number of times a value appears within a column in a dataset.

3. Cross column profiling

Observing the values or number of times a value appears across several columns and drawing analysis from doing so.

4. Cross-table profiling

Observing the values or number of values across several tables, and understanding how they compare to each other.

Data profiling is a systematic process aimed at evaluating and improving the quality of data by identifying inconsistencies, inaccuracies, and missing data. Here's a summary of the main steps and tasks involved:

Challenges in Data Profiling

Data profiling faces several challenges that can hinder its effectiveness and accuracy. One primary challenge is dealing with large volumes of data, which can be time-consuming and computationally intensive to analyze thoroughly. Additionally, data can come from disparate sources with varying formats and structures, complicating the profiling process. Inconsistent or poor-quality metadata further exacerbates these issues, making it difficult to understand the context and lineage of the data. 

Data privacy and security concerns also pose significant obstacles, as sensitive information must be handled with care to comply with regulations. Lastly, obtaining stakeholder buy-in and ensuring that data quality rules are adhered to across the organization can be difficult, requiring strong communication and governance frameworks.

How is Data Profiling Used in Business Intelligence?

Data profiling is used in projects involving data warehousing or business intelligence, especially beneficial for big data. The insights gained from data profiling can help companies improve data quality, build new products, solutions, or data pipelines. For instance, the Texas Parks and Wildlife Department used data profiling tools to enhance the customer experience.

• Data Warehousing: Data profiling is used in data warehousing projects to examine the data sets and identify their quality.
• Business Intelligence: In business intelligence projects, data profiling is used to gain insights that can help improve data quality and build new products or solutions.
• Big Data: Data profiling is especially beneficial for big data projects. It helps in identifying inconsistencies and relationships in the data, which can improve the quality of the data.

FAQs

Data profile challenges

Data profiling, a critical process for ensuring data quality, faces challenges such as system performance constraints when handling large datasets, determining the appropriate scope and level of detail for profiling, extracting meaningful insights from profiled data, handling data volume and variety from diverse sources, accounting for data quality issues that impact profiling reliability, and the dependency on suitable tools and skilled analysts. Addressing these challenges through robust profiling solutions and skilled data teams is essential for organizations to maximize the value of their data assets.

Data profiling vs data wrangling

Data profiling involves analyzing a dataset's structure, content, and quality to assess its suitability for intended use. Data wrangling, on the other hand, is the process of transforming and restructuring raw data into an analysis-ready format by cleaning, reformatting, and combining data from various sources. While profiling identifies potential data issues, wrangling addresses those issues through data transformation and preparation.

Data profiling vs data cleaning

Data cleansing and data profiling are complementary processes in data management. Data profiling is an analytical process that assesses the quality, structure, and characteristics of data, identifying issues like missing values and duplicates. It helps understand data patterns, distributions, and relationships. 

Data cleansing, on the other hand, is a remedial process that corrects or removes errors and inconsistencies within a dataset, improving its quality to meet predefined standards. This involves removing duplicates, standardizing formats, correcting mistakes, and validating data. While profiling identifies data quality issues, cleansing resolves them. Profiling provides insights into the data's state, which cleansing uses to correct and enhance the data, resulting in high-quality, reliable data for decision-making.

Data profiling for specific data tools

Depending on your stack, you will likely require specific considerations when approaching data profiling. See our list below for data profiling guides for each component of your data infrastructure:

Database / Warehouse

• Data profiling for SQL Lite
• Data profiling for Snowflake
• Data profiling for Redshift
• Data profiling for Postgres
• Data profiling for Amazon S3
• Data profiling for Oracle
• Data profiling for BigQuery
• Data profiling for Microsoft SQL
• Data profiling for mySQL
• Data profiling for Databricks
• Data profiling for Amazon Glue

Transformation

• Data profiling for dbt

BI / Visualization

• Data profiling for Tableau
• Data profiling for Looker

How do ETL and SQL data profiling help with data standardization? 

Data cleaning ensures accuracy and reliability by identifying and correcting errors, inconsistencies, and duplicates, enhancing data quality for analysis. ETL and SQL data profiling support this process by analyzing data to identify patterns, anomalies, and relationships. For more details, refer to data profiling in ETL and data profiling in SQL.

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Try Secoda

Secoda integrates with all of your data sources, providing a unified view of your entire data landscape. Its intuitive interface simplifies data exploration, cataloging, and understanding, crucial for accurate profiling. Automation capabilities reduce manual effort and proactively identify data quality issues, patterns, and anomalies, leading to better decision-making and data governance. Get started today