DATA LINEAGE. THE CRYSTAL BALL YOU ALWAYS HAVE BEEN LOOKING FOR?

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Data is king. Businesses rely on data to make decisions, understand their customers, and improve their operations. However, in complex organizations, it can be difficult to get insight into relevant data flows.

There are a number of reasons why this is the case. First, complex organizations often have a large number of data sources. This can make it difficult to track the movement of data between different systems and applications. Second, data flows in complex organizations can be complex and dynamic. This means that the path that data takes can change over time, making it difficult to keep track of. Third, data in complex organizations is often siloed. This means that data is stored in different systems and applications, making it difficult to get a holistic view of the data.

As a result of these challenges, it can be difficult to get insight into data flows in complex organizations. This can make it difficult to ensure the quality of data, comply with regulations, and make informed decisions. Despite these challenges, there are a number of things that organizations can do to get insight into data flows. Data lineage is typically a fundamental capability.

What is Data Lineage?

Data lineage is the tracking of data as it flows through an organization’s systems and processes. It provides a detailed record of the data’s origin, transformation, and destination. Data lineage is important for a number of reasons, including:

  • Data quality: Data lineage can help to ensure the quality of data by providing a way to track its history and identify any potential problems. For example, if a data point changes unexpectedly, data lineage can be used to trace back the change and identify the source of the problem.
  • Compliance: Data lineage can help organizations to comply with regulations that require them to track the movement of data. For example, the European Union’s General Data Protection Regulation (GDPR) requires organizations to track the personal data of their customers. Data lineage can help organizations to meet this requirement by providing a record of how personal data is collected, used, and stored.
  • Auditing: Data lineage can be used to audit data flows and identify potential security risks. For example, if an organization is concerned about the possibility of a data breach, data lineage can be used to track the movement of sensitive data and identify any potential vulnerabilities.
  • Business intelligence: Data lineage can help organizations to gain insights into their data by providing a way to track the relationships between different data sets. For example, data lineage can be used to identify which data sets are used to calculate a particular metric. This information can then be used to improve the accuracy of the metric and make better business decisions.

Benefits

  • Improved data governance: Data lineage can help organizations to improve their data governance practices by providing a way to track the ownership, access, and usage of data. This information can then be used to develop and enforce data policies and procedures.
  • Reduced risk of data loss: Data lineage can help to reduce the risk of data loss by providing a way to track the location of data. This information can then be used to recover data in the event of a loss or corruption.
  • Increased data agility: Data lineage can help organizations to become more data agile by providing a way to track the changes to data. This information can then be used to update data models and applications as needed.

How to Implement Data Lineage

There are a number of ways to implement data lineage. One way is to use a data lineage tool. These tools can help to automate the tracking of data flows and provide a visual representation of data lineage. Another way to implement data lineage is to manually track data flows. This can be done by creating spreadsheets or diagrams that track the movement of data.

The best way to implement data lineage will depend on the specific needs of the organization. However, all organizations should consider implementing data lineage to improve the quality and usability of their data.

A sample data lineage diagram illustrating the high-level data flow between a Tableau Workbook and a Snowflake datawarehouse.


Conclusion

In summary, data lineage is an important tool for organizations that want to ensure the quality, compliance, security, and usability of their data. By tracking the movement of data, data lineage can help organizations to identify and address problems, comply with regulations, and gain insights into their data.


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ACTIVE METADATA. SIGNIFICANT TO HELP YOU UNDERSTAND, IMPROVE AND ACTUALLY USE YOUR DATA?

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In the realm of data, metadata is widely recognized as the “data about data” – the vital information that characterizes its structure, meaning, and utility. Metadata serves as a crucial element in data management, enhancing accessibility and comprehension.

Among various metadata types, active metadata stands out as a dynamic and continuously updated entity that informs decisions and drives actions. It surpasses the notion of a static data description; instead, it thrives as a dynamic resource, revolutionizing our interaction with data.

Why Do You Need Active Metadata?

In our data-centric world, organizations generate an unprecedented volume of data. While this data holds immense value, managing it poses challenges. Active metadata emerges as an instrumental asset, empowering businesses to:

  1. Unearth deeper data insights: Active metadata provides businesses with comprehensive comprehension of their data, enabling the identification of patterns, trends, and relationships.
  2. Enhance data quality: Active metadata plays a pivotal role in elevating data quality. By pinpointing and rectifying errors, as well as ensuring data consistency and currency, businesses can significantly improve their data assets.
  3. Foster data accessibility: Active metadata promotes seamless access to data by providing users with comprehensive information and simplifying data search and utilization.
  4. Automate data-related tasks: Leveraging active metadata, businesses can automate data management tasks, freeing up valuable time for users to focus on critical endeavors. This, in turn, enhances accuracy and efficiency.

How Does Active Metadata Work?

Active metadata finds its home in a metadata repository, which can exist as a physical or virtual database hosted either on-premises or in the cloud. The repository remains interconnected with data sources, continuously monitoring data alterations. This invaluable information is then employed to update the active metadata.

The applications of active metadata are diverse. Here are a few examples:

  1. Enforcing data quality standards: Active metadata empowers the enforcement of data quality rules, ensuring utmost accuracy and consistency.
  2. Generating comprehensive reports: Active metadata serves as the foundation for generating detailed reports on data usage, allowing businesses to track patterns, trends, and usage metrics.
  3. Empowering analytical capabilities: Active metadata fuels cutting-edge analytics tools, enabling businesses to extract profound insights from their data and make well-informed decisions.

Conclusion

Active metadata emerges as a potent resource, empowering businesses to manage their data effectively. By gaining deeper insights, enhancing data quality, and fostering accessibility, active metadata equips enterprises with the ability to unlock the true potential of their data and make informed decisions.


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Want to take full advantage of your metadata? Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?
 

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