Part 4 - A foray into Data Governance

To connect the dots in case you have missed our other articles, please check out:

• Part 1 - Importance of ML Governance
• Part 2 - Risks of poor ML Governance
• Part 3 - ML Governance key principles and best practices
• Part 4 - A foray into Data Governance (this article)

Data Governance

Now we spoke about ML Governance at length in the last three articles, let's take a look at Data Governance. You can understand Data Governance as the predecessor to ML Governance. It is equally important, because it is just like with growing a plant - no water will get you nowhere. The same is true for Machine Learning models: No data, no ML model. Even more so, the performance, efficacy, and reliability of your ML models utterly depends on good quality and trustworthy data.

What is Data Governance?

Data Governance is the overall management of the availability, usability, integrity, and security of the data used in an organization. It involves establishing policies and procedures to ensure that data is properly managed throughout its entire lifecycle, from creation to deletion.

The purpose of Data Governance is to ensure that the data within an organization is accurate, consistent, and secure, and that it is used appropriately and ethically. This involves creating rules and guidelines for data access, usage, and sharing, as well as defining roles and responsibilities for data management across the organization.

Data Governance also involves the implementation of technologies and processes to manage and protect data, such as data classification, data security, and data quality management. The goal of Data Governance is to establish a framework that supports effective decision-making, reduces risk, and ensures compliance with regulations and industry standards.

How does Data Governance affect Machine Learning?

As you can already guess, Data Governance plays a critical role in ML as it impacts the quality and reliability of the data used to train Machine Learning models.

So, good Data Governance practices ensure that the data used for training is accurate, complete, and consistent. The reason is straightforward: ML models perpetuate biases that exist in the data used to train them. For example, if a ML model is trained on data that is biased against certain groups of people, such as women or people of certain ethnic groups, the resulting model will be biased, respectively.?

Thus Data Governance helps to ensure that the data used for ML is ethical and complies with legal and regulatory requirements before you start training a model. This is especially important for sensitive data such as personal information or financial data, where the risk of data breaches or misuse is high.

Organizations implement Data Governance practices to increase the trust and confidence in their ML efforts, improving their ability to make better decisions, automate processes, and drive business value.

What are good Data Governance practices?

To achieve good Data Governance practices, there are a number key principles organizations should take into consideration when establishing a framework for the effective management and protection of their data:

• Establish clear policies and procedures: Develop and document clear policies and procedures for data management, including data access, usage, sharing, retention, and disposal.
• Assign data ownership: Identify and assign ownership of data assets to specific individuals or departments within the organization. This helps to ensure accountability for data quality and security.
• Define data quality standards: Establish data quality standards to ensure that data is accurate, complete, and consistent. This includes identifying and addressing data errors, inconsistencies, and duplicates.
• Ensure data security: Implement appropriate security measures to protect data from unauthorized access, modification, or disclosure. This includes controlling access to sensitive data, encrypting data in transit and at rest, and monitoring data usage and access.
• Maintain data privacy: Ensure that data is collected, used, and shared in compliance with relevant privacy regulations, such as GDPR or CCPA. This includes obtaining consent from individuals for data usage, providing transparency about data usage, and implementing appropriate controls to protect personal information.
• Provide training and education: Provide regular training and education to employees and stakeholders to ensure awareness of data governance policies and procedures, and their roles and responsibilities in data management.
• Conduct regular audits: Regularly review and audit data management processes to ensure compliance with policies and standards, identify risks and vulnerabilities, and take corrective actions as needed. This is where a Digital Chain of Custody is of tremendous help.

How does a Digital Chain of Custody help?

A Digital Chain of Custody (DCoC) is a record that tracks the movement of data through various stages of its lifecycle, from creation to deletion. These records include information such as who accessed the data, when it was accessed, and what changes were made. Every ETL for instance is considered a change as you move and transform data from one or more sources to another dataset.

Thus a DCoC can be a valuable tool for audits as it provides a detailed trail of data usage, which can help to identify any unauthorized or inappropriate access, usage, or modification of data.

By using a DCoC solution, auditors can verify the integrity and authenticity of the data being audited when leveraged in Machine Learning . This helps to establish trust in the data and ensures that any findings or conclusions drawn from the data in the form of a ML model are reliable and accurate.

Furthermore, a Digital Chain of Custody helps to identify any gaps or weaknesses in the data governance framework, allowing organizations to take corrective action and improve their data management practices.

In conclusion

Machine Learning Governance is the management of the processes and systems used to develop, test, deploy, and monitor machine learning models. Effective ML Governance requires the use of high-quality data that is well-managed and governed, and this is where Data Governance comes in.

Data Governance provides the foundation for effective ML Governance by ensuring that the data used to train and test ML models is accurate, complete, and consistent. Good Data Governance practices minimize the risk of bias, errors, and incorrect predictions in machine learning models by ensuring that the data used to develop these models is reliable and trustworthy.

In addition, Data Governance ensures that the use of data for ML is ethical and complies with legal and regulatory requirements. This is especially important for sensitive data such as personal information or financial data, where the risk of data breaches or misuse is high.

Therefore, effective Data Governance is a critical component of effective ML Governance, and organizations must establish strong Data Governance practices before implementing Machine Learning initiatives.

Follow us on OriginML for more on the importance of good ML Governance, it’s key principles and best practices. Stay tuned.

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