What are the best tools and methods for data discovery and data profiling in your industry or domain?

1 Data discovery and data profiling basics

Data discovery is the process of finding and cataloging data sources, such as databases, files, applications, or APIs, across your organization. Data profiling is the process of analyzing the content, structure, and metadata of each data source, such as data types, formats, values, relationships, and anomalies. Together, these processes help you assess the quality, usability, and relevance of your data for your business goals and data governance policies.

2 Data discovery and data profiling challenges

Depending on your industry or domain, you may face different challenges when performing data discovery and data profiling. For example, if you work in a highly regulated sector, such as finance, healthcare, or government, you may need to comply with strict data privacy and security standards, such as GDPR, HIPAA, or FISMA. This means you need to identify and protect sensitive data, such as personal information, financial transactions, or health records, and ensure that only authorized users can access and process them. Alternatively, if you work in a dynamic and innovative sector, such as e-commerce, media, or education, you may need to deal with diverse and rapidly changing data sources, such as social media, web analytics, or online learning platforms. This means you need to keep track of new and evolving data types, formats, and schemas, and ensure that they are consistent and compatible with your existing data systems and applications.

3 Data discovery and data profiling methods

To address these challenges, it's important to adopt effective and efficient methods for data discovery and profiling. Data inventory is one such method, which involves creating and maintaining a comprehensive list of all data sources, along with their locations, owners, descriptions, and classifications. This helps to gain visibility and control over data assets, as well as prioritize and plan data governance activities. Data lineage is another method that traces and documents the origin, transformation, and destination of each data element across pipelines and workflows. This helps to understand the provenance, quality, and impact of data. Furthermore, data quality measures and improves accuracy, completeness, consistency, timeliness, and validity of data according to business rules and standards. Finally, a data glossary defines and standardizes the meaning, context, and usage of data terms across an organization. This helps to establish a common language and understanding of data which facilitates communication and collaboration.

5 Data discovery and data profiling best practices

To maximize the benefits of data discovery and data profiling tools and methods, best practices should be followed. This includes aligning activities with business goals and data governance policies, as well as involving stakeholders and users in the process. Additionally, it is important to continuously update and refine your data inventory, data lineage, data glossary, and data quality metrics. Doing so will help ensure that your data is always accurate, complete, consistent, timely, and valid while also adhering to quality, security, and compliance standards. Furthermore, involving stakeholders will provide insights from different perspectives and roles to meet their needs and expectations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?