Organizations invest a great deal of resources in collecting and storing data. Now imagine the data collected is processed to make critical strategic business decisions, only to find that it was inaccurate, incomplete, outdated, and simply of poor quality. This is precisely where data profiling jumps in.
As data sprawls across the digital landscape, traversing through on-premises, cloud, SaaS, data lakes, and hybrid cloud environments, gaining data context is crucial. Data discovery and classification are just the first steps in identifying data assets, but it’s data profiling that adds structure, context, and quality to them.
As regulations tighten data governance requirements, and organizations increasingly rely on new data generation and collection to power strategic initiatives and everyday operations, understanding the quality, lineage, accuracy and reliability of your data is no longer optional.
This guide explores what data profiling is, why organizations need it, the different types and techniques involved, the process, and the benefits it provides to modern enterprises.
What is Data Profiling?
As the name suggests, data profiling is all about building a profile of your data to understand what’s in it and whether it can be trusted and leveraged in a business environment. IBM defines data profiling as the process of reviewing and cleansing data to better understand its structure and maintain data quality standards within an organization.
Profiling involves conducting a detailed examination and analysis of data to understand its structure, context, information, quality, reliability, connections, and other attributes. Think of data profiling as segregating data into smaller blocks or metadata in an effort to understand what type and nature of data exists within the corporate environment.
For example, when profiling employee records, organizations can better understand the percentage of records that are accurate and which aren’t. Other details could include incomplete email addresses, house addresses, contact information, billing information, etc.
Why Do You Need Data Profiling?
Data profiling provides the contextual understanding organizations require for their data assets. Since modern enterprises often have data distributed across multiple data environments, such as on-premises systems, cloud, SaaS applications, business departments, and geographic locations, data can be duplicated, resulting in inconsistencies, inaccurate or incomplete information, formatting differences, and other data anomalies.
Data profiling helps address data complexities by providing granular insights and greater transparency into the actual state of enterprise data. Data profiling is extremely useful when organizations are struggling to assess data quality, develop a data wireframe, assess data lineage and establish relationships between datasets. More reasons include:
- As organizations migrate data to the cloud and other data environments
- Disclosing data context as part of a data merger, acquisition or technology upgrade
- Establishing a uniform data architecture to be utilized across the enterprise
What are the Different Kinds of Data Profiling?
Data profiling has three main categories, including:
1. Structure Discovery
As the name suggests, structure discovery aims to examine the data structure and assess whether the data format is correct and meets structural requirements. For example, whether the date format follows an expected format such as DD, MM, YYYY, whether the dates mentioned are correct, whether they align in the correct column, etc.
2. Content Discovery
Content discovery examines individual data fields within the dataset. It helps identify inconsistencies, missing information, duplicates, and abnormal statistical patterns. For example, profiling an Age field could reveal unusual values, such as negative or out-of-range values, indicating data quality issues.
3. Relationship Discovery
Relationship discovery identifies connections between datasets. This could include direct and indirect relationships, as well as any dependencies among fields, tables, columns, or data sources.
Data Profiling Techniques
Data profiling techniques are the core methods organizations use to develop a comprehensive understanding of their data assets. Core techniques include:
A. Column Profiling
Column profiling scans individual column fields to understand their values and characteristics. This helps identify data types, any null counts, specific patterns, unique trends, and minimum and maximum values.
B. Cross-column Profiling
Cross-column profiling consists of key analysis and dependency analysis. By searching for a potential primary key, the key analysis procedure examines the array of attribute values. The aim of the dependency analysis procedure is to identify relationships or patterns in the data set.
C. Cross-table Profiling
Cross-table profiling identifies stray data. To investigate relationships among column sets across multiple tables, the foreign key analysis identifies orphaned data or other discrepancies.
D. Data Rule Validation
To confirm that data sets adhere to set requirements, this method evaluates them against established norms and standards.
Data Profiling vs. Data Mining
Data profiling helps organizations understand and assess the data. On the other hand, data mining discovers insights from the data. Both involve comprehensive data analysis but serve distinct purposes.
Data Profiling
|
Data Mining
|
| Examines data structure and quality |
Assess data comprehensively and provide contextual data insights |
| Helps organizations understand data structure, quality, reliability, connections, and other attributes |
Sorts massive datasets to identify hidden patterns, any correlations and relationships |
| Aimed at providing organizations with confidence prior to data migration, acquisition, or any integration |
Aimed at providing organizations with a contextual understanding of data and building confidence to support decision-making |
| Answers whether the data is reliable |
Answers what organizations can learn from data |
Data Profiling Process
Data profiling is a straightforward process. However, its implementation varies depending on the organization's data environment and business objectives. Key stages of a typical data profiling process include:
A. Identify the Data
Data is scattered everywhere and not every dataset demands urgent attention. Begin by identifying the databases that need to be profiled.
B. Collect Data
Gather data and metadata for profiling (databases, files, tables and other structural metadata) into a centralized analysis environment.
C. Analyze Data Characteristics
Once the data is accumulated into a centralized environment, apply a profiling technique to assess whether the data characteristics are present. For example, check for data completeness, formatting, value range check, patterns, relationships, etc.
D. Identify Anomalies and Quality Issues
During profiling, assess characteristics against preestablished rules and standards. Look out for any anomalies, wrong data relationships, duplicates, inconsistencies, missing data values or formats, or data quality issues.
E. Document and Continuously Monitor Data
Record and share profiling results to support governance, remediation, and data-quality decisions. Continuous profiling helps detect changes and emerging quality issues over time.
Benefits of Data Profiling
Unlike other techniques, data profiling provides an in-depth analysis of data. Benefits include:
A. Improved Data Quality
Profiling helps identify data errors such as missing data, invalid relationships, outdated or duplicated data, inconsistent values, and invalid or anomalous data. Identification helps organizations provide a clear picture of data quality and prioritize remediation efforts and data quality controls.
B. Assists with Data Migration
Prior to any data migration to cloud services or another data platform, organizations need holistic insights into what data they are moving. Profiling helps identify data quality, minimizing the likelihood that existing data issues are simply transferred to the new data environment.
C. Improves Data Governance
A robust data governance framework requires an in-depth understanding of enterprise data. Profiling enriches metadata with information about data characteristics, enabling data owners, stewards, and governance teams to make more informed strategic decisions.
D. Reliable Analytics and AI
Analytics and AI are data-hungry systems that depend heavily on the data they receive. Profiling helps determine whether datasets exhibit attributes and patterns that could compromise data accuracy and reliability when used in downstream systems and in model development and deployment.
E. Regulatory Compliance
Organizations are expected to be better custodians of their data assets and regulatory requirements mandate strict data privacy and security requirements. To meet those needs, organizations must conduct data discovery and classification, and profiling provides additional context about data characteristics.
How Securiti Supports Data Profiling
As enterprise data environments increase at an unprecedented rate and become distributed, organizations need more than a basic understanding of data. They need data profiling to gain context on its characteristics, relationships, quality, and suitability for safe use.
Securiti DataAI Command Platform helps organizations automatically profile and classify data to understand its structure, characteristics, semantic meaning, and trustworthiness. Securiti Data Catalog enables users to easily find, understand, trust and access the data they need, as well as secure and govern the processes around their data.
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