Why ethical use of data is so important to enterprises

Why ethical data use matters

Regulatory concerns, such as hefty fines, legal action and reputational damage, are often the primary drivers of ethical data handling. However, all organizations -- regulated and non-regulated, large and small -- should prioritize ethical data handling.

Data is often an organization's most valuable asset. Although businesses already gain tremendous value from their data, there's ever-mounting pressure to extract even more value. This was the primary driving force behind the big data revolution.

However, organizations must draw a line between obtaining additional business value and outright exploitation. While important, this isn't just about addressing regulatory requirements, but meeting customer expectations. Recently, consumers have become keenly aware of how their data is collected and sold. As such, organizations face increasing customer demands for transparency and ethical data use.

As data privacy has become top of mind for consumers, it stands to reason they will gravitate toward businesses with transparent operations that avoid the temptation to use customer data in an unethical manner. When a business builds trust with its customers, it multiplies. Acquiring more customers means acquiring more data, thus adding value. This is especially true in the age of AI, as it amplifies the benefits of additional data while also increasing the risk of exposure.

However, that trust is easily broken. Selling customer data or suffering a data breach can cause massive reputational damage, and as a result, some customers might take their business to a more trustworthy organization. New customers might be harder to attract -- especially in the aftermath -- leading to lost revenue.

Strategies for ethical data use

While it's easy to advocate for ethical data handling, implementing those practices can be difficult. Keep in mind that data ethics is a continuous process. It requires data governance policies to evolve as technology changes, along with ongoing monitoring, evaluation, and adaptation.

Within the organization

For any data ethics initiative to succeed, it can't be treated solely as an IT project. If that were the case, there would be no real authority to enforce policy, especially if the organization isn't regulated. Higher-ups in the organization could demand that IT violate the policies it has established.

Taking a top-down approach in policy directives prevents this issue. C-suite executives must drive ethical data initiatives and establish organizational policies. The board, meanwhile, must hold decision makers accountable.

However, it isn't enough for the C-suite to say that the organization is committed to ethical data handling; they must formalize data governance rules. Documenting specific data usage policies is critical to making them a central part of the operational framework.

Employees must also receive training on handling sensitive data, and the training should be extended to everyone who handles data. Depending on the organization's size, it might be prudent to hire a Data Ethics Officer or even a Data Ethics Team. This person or team should conduct regular audits and assessments to ensure responsible data handling.

Outside the organization

Once documentation and implementation occur, clear communication with customers is important. Businesses must tell their customers exactly how their data is used, even if there's no legal requirement to do so. Customer disclosures should be concise and easy to read, rather than buried in lengthy Terms of Service documents that customers are likely to skip. By releasing a data use disclosure, the business is making a commitment to its customers and must carefully adhere to its terms. Failing to do so destroys trust and might leave the business legally exposed.

Customers aren't the only outside party organizations must consider. They must also subject partners and suppliers to ethical data handling requirements. While it might be impossible for businesses to operate without sharing customer data with suppliers and partners, the organization is ultimately responsible for how partners and suppliers handle it. As such, organizations must require partners and suppliers to adhere to the same strict data governance policies that they themselves follow.

Technological strategies

To prevent unauthorized access and misuse of data, implement security controls. Such controls go a long way toward preventing data breaches. When combined with zero-trust principles and least privilege access, such controls can also guard against insider risks. This helps prevent employees from circumventing data controls already in place.

A final step the organization can take in its quest for ethical data use is anonymizing data whenever possible. To do so, disassociate raw data from personally identifiable information. This enables the use of sensitive data to train AI models without risking customer privacy, thereby maintaining trust.

Brien Posey is a former 22-time Microsoft MVP and a commercial astronaut candidate. In his more than 30 years in IT, he has served as a lead network engineer for the U.S. Department of Defense and a network administrator for some of the largest insurance companies in America.