What Is Data Ethics and How Can Storage Improve Ethics Best Practices?

Digital transformation is all about leveraging data. But in this data-driven world, the creation, collection, analysis, and sharing of massive amounts of data across different platforms can create new risks for organizations. Organizations use this data to improve services and offer better customer experiences, but customers may have concerns about how and when their personal data is being used. 

Data ethics is concerned with how data is managed, handled, and stored and the moral issues related to its usage. It explores the answers to the questions, “How can our organization collect, store, and use data ethically?” and “What customer rights must we protect?”

It’s important for organizations to understand the risks related to the ethical use of data in the enterprise and put measures in place to mitigate them.

Data Ethics: What Is It?

Defined by Gartner as “a system of values and moral principles related to the responsible collection, use, and sharing of data,” data ethics is concerned with the moral issues related to data practices that could negatively affect individual people. Data ethics focuses on data in all its phases, including the generation, collection, analysis, and dissemination of it. 

Data ethics addresses behaviors related to the use of general and personal data and guides organizations on how to use data, algorithms in AI and machine learning, and other technologies to prevent bias. 

Further, it ensures that online users consent to sharing their data and that organizations adhere to compliance and privacy regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the California Consumer Privacy Act (CCPA), and the Payment Card Industry Data Security Standard (PCI DSS).

Why Is Data Ethics Important?

With cyberattacks on the rise and analytics programs backed by AI technologies driving the demand for more data, online users are becoming more aware of the issues surrounding the privacy of their data. 

Adhering to data ethics helps organizations manage the risks associated with data privacy and continue to improve user experiences without violating personal data privacy. By employing data ethics principles, organizations can:

• Create a universal framework that defines what organizations can or cannot do with data derived from technology and serves as a code of conduct for using this data responsibly.
• Protect customer data privacy by remaining compliant with GDPR, CCPA, and other data regulations as they emerge and develop. This includes data retention and deletion policies.
• Reduce bias when using information derived from artificial intelligence and machine learning.
• Build customer trust by operating by a known code of conduct based on the practice of data ethics that assures them that their data is not being used in unethical ways.

Data Ethics and Data Governance

Data governance is the management of data availability, usability, integrity, consistency, and security to ensure that high-quality data is used across the organization. It’s also concerned with the risks and penalties associated with data. 

Data governance focuses on the proper use of data to avoid the introduction of data errors into a system and prevent potential misuse of personal data. Policies and procedures are established to monitor data usage and help maintain data security, compliance, and transparency. These principles are essential to ensuring the ethical use of an individual’s information.

What Is an Ethical Framework for Data Use?

A data ethics framework is a set of ethical principles that guides the appropriate and responsible use of data in an organization. It sets guidelines that help business leaders, stakeholders, and employees understand the ethical considerations related to data. It makes clear, in an easily digestible manner, organizational practices related to data use. 

The framework should be used by anyone in the organization who is directly or indirectly working with data. This can include organizational leaders, policymakers, operational employees, analysts, statisticians, and anyone else producing data-driven insights.

Data ethics frameworks are typically based on three principles:

• Transparency: The organization's policies, actions, and processes related to data are freely and easily accessible to the public in a clear and concise format so they can understand how the company is using their personal data.
• Accountability: There are effective governance and oversight mechanisms for any project.
• Fairness: It ensures that the organization’s use of data is just and doesn’t discriminate against individuals and social groups.

Organizations can use existing frameworks, such as the GDPR’s Privacy by Design (PbD); Fairness, Transparency, and Accountability (FT&A) for ML and AI; and Fair Information Practices (FIP), as the foundation for a tailored organizational framework for ethical data use. 

What Projects or Initiatives Require Data Ethics?

The dilemma of data ethics is becoming more urgent as business leaders look to data and analytics programs to increase business value. Organizations are using data to deliver innovative solutions and AI and machine learning play a key role in many organizations’ efforts to automate processes, increase efficiency, and reduce costs. 

But AI is only as good as the data that feeds it. Algorithms for AI and machine learning learn from users’ feedback based on training data that may include biases when preference is given to certain features and characteristics.

Organizations need to be cognizant of the origins of the reference data used by algorithms to avoid putting bias into their platforms. Interpretability, which explains how a trained model reaches a particular decision and identifies sources of error to improve model accuracy, can help organizations identify and reduce bias.  

How Storage Can Help Improve Data Ethics Best Practices

Effective data ethics and governance rests on your ability to store and manage an individual’s sensitive data and the enormous volumes of input and training data required by AI and ML algorithms, securely.

A robust data storage solution is essential to meeting the security and data compliance requirements and regulations suggested by governance laws. Features should include:

• Access control: Helps ensure data access is strictly controlled and audited so that anyone accessing data can be easily and accurately tracked.
• Data encryption: Is critical for protecting against risks like ransomware.
• Data integrity: Helps protect data from corruption and manipulation.
• Data retention: Sets retention periods based on your data retention policy, which outlines exactly what data you can store or archive, where, why, for how long, and how it’s deleted or moved.

Support Data Ethics with Modern Data Protection Solutions  

Today’s organizations face complex ethical considerations related to the collection, disclosure, and analysis of data. Pure Storage® offers several modern data protection solutions that allow you to safeguard your data to support your organization’s ethical data framework.

• Reduce the risk of ransomware by using SafeMode™ snapshots, which provide read-only snapshots that can’t be modified, deleted, or encrypted.
• Expand data backup to the cloud with Purity CloudSnap™ for effortless, built-in cloud-based data protection for off-premises flexibility, longer-term retention, and lower costs.

Recover from an attack with Rapid Restore with FlashBlade®, which provides protection of both data and associated metadata catalogs and integrates with a diverse portfolio of backup software partners.