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Data governance with AI data collection

Posted May 17, 2023
Illustration of connected dots, meant to represent governance in AI data collection.

Machine learning (ML) algorithms are only as good as the data they're trained on. In fact, collecting or creating large volumes of high-quality data can be one of the most challenging aspects of any ML project. According to a Forbes article, data scientists can spend up to 70% of their time acquiring and transforming data. Good data governance can help by ensuring your data is consistent, usable, trusted and secure. In essence, data governance is at the core of artificial intelligence (AI) governance — using accountable practices throughout the AI lifecycle to build models based on trust and ethics.

Read on to learn the key elements of a data governance strategy within your wider AI governance, including the benefits and some best practices.

What is data governance?

Data governance is closely related to, but is not synonymous with, data management. It comprises the established policies and procedures around data, while data management enacts those policies and procedures to create, collect, store, maintain, secure and use data.

A good data governance strategy helps to ensure your data is consistent, accessible and secure, and that it complies with applicable regulations regarding data protection.

Benefits of data governance

One of the key benefits of good data governance is that it helps to ensure your training data is complete and accurate. Not only does this save time for data scientists and ML engineers, it also leads to better model outcomes. The higher the quality of training data, the better the model output in terms of accuracy and reliability.

Good data governance also helps your organization adhere to regulations that protect data privacy and security. These vary across jurisdictions, and include legislation such as the General Data Protection Regulation (GDPR), which governs how personal data from citizens located in the European Union is used, processed and stored by organizations worldwide, as well as the Health Insurance Portability and Accountability Act (HIPAA), a law that protects sensitive patient health information in the United States.

There are also a number of industry best practices for data governance, such as the System and Organization Controls 2 (SOC 2) report provided by the American Institute of CPAs. It defines criteria for managing customer data and is widely followed by service organizations.

Further, governments and organizations globally are responding to advancing AI models by developing AI regulations around data privacy, inclusivity, transparency and more.

In addition to helping with compliance, robust data governance policies demonstrate your organization's commitment to transparency, which can help foster trust with your customers, partners and regulators. It also helps reduce data-related risks such as misuse and breaches by ensuring privacy and security.

The essential guide to AI training data

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Five data governance best practices for AI

Data governance helps build trust, mitigate risks and ensure responsible and ethical AI practices. Adopting and upholding data governance best practices is a vital part of ensuring your AI model is successful. Consider the following best practices.

1. Establish a data governance strategy

Outline how your organization will use data to generate value in a way that ensures data privacy, security and compliance. Your strategy should include a clear outline of all requirements, including rules, processes and responsibilities for managing data. It is important to provide a clear top-down mandate — where leaders set the standard for others to follow — in order to ensure that all levels of the organization understand that data management is everyone's responsibility.

2. Select a framework that establishes data ownership and accountability

Whatever framework your organization chooses, it should include the following four components. First, it should specify who is responsible for data stewardship. The data steward ensures all data assets are consistent and compliant. Second, it should account for data quality management, which outlines the procedures used to ensure data assets are free from inaccuracies. Third, it should define data management processes such as how data assets are to be created, stored, accessed and used. Fourth, it should outline the technology infrastructure your organization will employ. This refers to the hardware and software systems used to collect, store and manage data.

3. Create a data catalog

This is a record of all of your organization's existing data that usually includes information about data sources, usage and lineage (an outline of your data's origins and how the data changed to its final form). Data catalogs centralize information so you always know what data you have access to, as well as its sources and quality level.

4. Incorporate best-in-class cybersecurity practices

Some ways of enforcing network security controls include setting up firewalls, using virtual private networks (VPNs) and regularly monitoring network traffic. You might also conduct regular third-party security assessments and penetration tests to identify and address vulnerabilities in your network infrastructure. Routinely testing the limits of your defenses is an important part of data management and adherence to data governance policies.

5. Regularly monitor and audit data governance processes

Having a regular assessment practice in place helps to ensure potential issues can be quickly identified. Using a data fabric can be helpful: This integrated data architecture standardizes data management across platforms and provides visibility so that any issues with data access, control, protection and security can be dealt with rapidly.

Better data governance, better AI models

By taking the time to establish a thoughtful data governance strategy, you can ensure your AI models are trained using the highest quality data, serving to improve the accuracy of your algorithms.

Working with a third-party partner can help.

Look for one with the ability to scale and create custom datasets in multiple languages that emphasize data security and confidentiality. TELUS International uses advanced quality system features such as built-in validation, spot-checking and a workers seniority system to ensure the highest quality data of all types. We also offer secure onsite global delivery centers, if required. As further risk management protocols, we maintain customer anonymity at all times throughout the duration of a project, and our fraud detection team ensures that each AI Community member is authenticated. Additionally, each community member works on only a single project at any given time.

The benefit of enacting robust data governance procedures is undeniable. They help to build trust, mitigate risks and ensure responsible and ethical AI practices.

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