Data Profiling

Put your data under a microscope and see it in a new light

Data profiling can uncover data issues and be used to monitor data quality over time to ensure data governance processes are working properly to keep bad data out. Melissa Data Profiler analyses data before it’s merged into your warehouse, then helps ensure consistent data quality once it’s there. Use Data Profiler to develop informed strategies on how best to manage and employ your data. Don’t just collect data—make it work for you!

Enhance data governance and data warehousing efforts to drive better business intelligence and analytics throughout the enterprise
Create a metadata repository that aids in building strategic data marts to resolve ambiguities throughout data entities
Enforce business rules on incoming records so you can maintain data standardisation, collect data, and format it in a way that is easy to understand and analyse
Showcase ongoing Data Quality improvements to stakeholders, and help convince management of the necessity for Data Quality Budget and Governance initiatives with over 100 types of detailed metadata

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What is Data Profiling?

Data profiling is the process of examining and analysing the characteristics, quality, and structure of a dataset. The primary goal of data profiling is to understand the content, relationships, and statistical properties of the data to ensure its accuracy, completeness, and consistency. This is a crucial step in data management and data quality assessment.

Types of Data Profiling - United Kingdom

The Types of Data Profiling

Data profiling applications typically analyse a database by collecting and organising information. This involves employing various data profiling techniques, including column profiling, cross-column profiling, and cross-table profiling. These profiling methods can generally be grouped into three categories:

Structure Discovery: This entails assessing whether the data is consistently formatted and adheres to the correct structure. Basic statistics are employed to gauge the validity of the data
Content Discovery: The focus here is on ensuring data quality. This involves processing data for formatting and standardiszation and integrating it efficiently with existing data. For instance, if a street address or phone number is improperly formatted, it could lead to challenges in reaching certain customers or misplacing deliveries.
Relationship Discovery: This category involves identifying connections between different datasets, shedding light on the relationships and dependencies within the data.

The Benefits of Data Profiling

Data Quality Improvement Data profiling helps identify and address data quality issues such as inaccuracies, inconsistencies, and missing values. By understanding the data's characteristics, organizations can take corrective actions to improve data quality.

Improved Data Understanding Data profiling provides a comprehensive view of the dataset, including its structure, patterns, and relationships. This enhanced understanding is valuable for data analysts, data scientists, and other stakeholders who need to work with the data for analysis or reporting purposes.

Identifying Data Dependencies Analysing relationships between different data elements or tables helps organisations understand how data is interconnected. This is crucial for designing databases and data models, and it aids in identifying and managing data dependencies.

Reduced Risk of Errors Data profiling helps identify anomalies and inconsistencies early in the data preparation process. Addressing these issues proactively reduces the risk of errors downstream in analytics, reporting, or other data-driven processes.

Tackle Two Data Quality Predicaments

Data Profiler leverages sophisticated parsing technology and every available general profiling metric to (1) identify data quality issues and (2) monitor improvements over time.

Identification
Identify data quality issues for immediate attention and ensure conformity of source data to specified requirements of pre-set limits.

General Formatting
Data Profiler ensures your input is formatted to your exact specifications. Especially useful for names, emails, postal codes, addresses, and other contact data fields.

Content Analysis

Data Profiler utilises reference data to determine if your input is consistent with expected data.

Field Analysis

Data Profiler can determine if the input data is consistently fielded using the data contained in the entire record to analyse the context of data.

Monitoring

Good data quality means constantly ensuring what you’ve collected is up-to-date. Profiler allows regexes and error thresholds to be set for full-fledged monitoring, 24/7/365.

Achieve Complete Contact Data Management

Melissa's Data Quality tools help organisations of all sizes verify and maintain data so they can effectively communicate with their customers via postal mail, email, and phone. Our additional data quality tools include

Common Questions About Data Profiling

Data Profiling is crucial for ensuring data quality, identifying anomalies, and understanding the structure of the data. It supports informed decision-making and is a foundational step in various data-related projects.

Key components include column analysis, data quality assessment, relationship discovery, value distribution analysis, statistical profiling, and pattern recognition.

Data Profiling can help identify issues such as missing values, inconsistent data, outliers, redundant information, and data dependencies.

Common techniques include column profiling, value distribution analysis, pattern recognition, relationship discovery, and statistical profiling.

The frequency of Data Profiling depends on factors such as data volatility and the criticality of the data. It is recommended to perform data profiling regularly, especially when dealing with dynamic datasets.

Helpful Resources

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