How to set up lineage transparency for your machine learning initiatives

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategies. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes.

Have you ever wondered how these algorithms reach their conclusions? The answer lies in the data used to train these models. And how that data is derived. In this blog post, we will explore the importance of lineage transparency for machine learning datasets and how it can help establish and ensure trust and reliability in ML conclusions.

Trust in your data is a critical element for the success of any machine learning initiative. Executives evaluating decisions made by ML algorithms must have faith in the conclusions they draw. Ultimately, these decisions can have a significant impact on business operations, customer satisfaction, and profits. But trust isn’t just important to executives. Before executive trust can be established, data scientists and citizen data scientists who create and use ML models must have trust in the data they use. Understanding the meaning, quality, and provenance of data is a key element of building trust. This discussion focuses on data provenance and genealogy.

Lineage describes the ability to track the provenance, history, movement, and transformation of data throughout its life cycle. In the context of ML, lineage transparency means tracking the source of the data used to train a model to understand how that data was transformed and identify any potential biases or errors that may have been introduced along the way.

Benefits of Genealogy Transparency

Implementing lineage transparency in ML datasets has several benefits. Here are a few:

• Improved model performance: By understanding the origins and history of the data used to train ML models, data scientists can identify potential biases or errors that may affect model performance. This allows you to make more accurate predictions and better decisions.
• Increased reliability: Lineage transparency can help build trust in ML conclusions by providing a clear understanding of how data was sourced, transformed, and used to train models. This can be especially important in industries where data privacy and security are paramount, such as healthcare and finance. Genealogy details are also required to meet regulatory guidelines.
• Solve problems faster: If something goes wrong with your ML model, lineage transparency can help data scientists quickly identify the cause of the problem. This can save time and resources by reducing the need for extensive testing and debugging.
• Improved Collaboration: Lineage transparency promotes collaboration and collaboration between data scientists and other stakeholders by providing a clear understanding of how data is being used. This improves communication, improves model performance, and increases confidence in the overall ML process.

So how can organizations implement lineage transparency for ML datasets? Let’s look at some strategies.

• Take advantage of the data catalog: A data catalog is a centralized repository that provides a list of available data assets and their associated metadata. This can help data scientists understand the origin, format, and structure of the data used to train ML models. Equally important, the catalog is designed to identify data stewards (subject matter experts on specific data items) and help companies define their data in a way that everyone in the business can understand.
• Use a solid code management strategy: Version control systems like Git help you track changes to your data and code over time. This code is often the source of the actual record of how the data was transformed as it is incorporated into the ML training dataset.
• Make it an essential practice to document all data sources.: Documenting data sources and providing a clear explanation of how the data was transformed can help build trust in ML conclusions. This can also make it easier for data scientists to understand how their data is used and identify potential bias or errors. This is critical for source data that is provisioned ad hoc or managed by non-standard or custom systems.
• Implement data lineage tools and methodologies: By parsing code, extract, transform, and load (ETL) solutions, and more, you can use tools to help your organization trace the lineage of a dataset from its final source to its destination. These tools provide a visual representation of how data is transformed and used to train models and also facilitate deep inspection of the data pipeline.

In conclusion, lineage transparency is a critical component of any successful machine learning initiative. By providing a clear understanding of how data is sourced, transformed, and used to train models, organizations can build trust in ML results and ensure the performance of their models. Implementing genealogy transparency may seem difficult, but there are several strategies and tools that can help organizations achieve this goal. By leveraging code management, data catalog, data documentation, and lineage tools, organizations can build a transparent and trusted data environment that supports their ML initiatives. With lineage transparency, data scientists can collaborate more effectively, solve problems more efficiently, and improve model performance.

Ultimately, lineage transparency is not just a nice-to-have, it’s a necessity for organizations looking to realize the full potential of their ML initiatives. If you want to take your ML initiative to the next level, start by implementing data lineage for all your data pipelines. Your data scientists, executives, and customers will thank you!

Explore IBM Manta Data Lineage today.