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Building digital trust: The role of data ethics in the digital age

The accelerating digital economy needs robust ethical controls throughout data supply chains.


The digital economy is built on data—massive streams of data being created, collected, combined and shared—for which traditional governance frameworks and risk-mitigation strategies are insufficient. In the digital age, analyzing and acting on insights from data can introduce entirely new classes of risk. These include unethical or even illegal use of insights, amplifying biases that exacerbate issues of social and economic justice, and using data for purposes to which its original disclosers would not have agreed, and without their consent. These and other practices can permanently damage consumer trust in a brand.

In the past, the scope for digital risk was limited to cybersecurity threats but leading organizations must now also recognize risks from lackluster ethical data practices. Mitigating these internal threats is critical for every player in the digital economy, and cannot be addressed with strong cybersecurity alone.

Accenture Labs launched a research collaboration with leading thinkers on data ethics to help provide guidelines for security executives and data practitioners and enable development of robust ethical controls throughout data supply chains.


Data Ethics Across the Supply Chain

In the digital era, data is the fundamental currency. How organizations handle it throughout the data supply chain—from collection, aggregation, sharing and analysis, to monetization, storage and disposal—can have a decisive impact on their reputation and effectiveness. A data supply chain framework helps practitioners evaluate current ethical practices and implement appropriate ethical controls at each step.

Developing a Code of Data Ethics

Guiding Ethical Decisions

How can organizations design and reinforce ethical decision-making across the data supply chain? A data ethics framework helps guide ethical reviews at each step of the supply chain. Ethical design reviews can fit seamlessly into existing best practices for project management and service design:

Ethical Algorithms and Automation

New risks and challenges in the digital economy extend to various types of automation that are powered by data insights. Sense and respond (S&R) systems have been in use for decades, responding to their environment in real time with little or no human input.   

As the prevalence and decision-making capabilities of S&R systems continue to increase, there’s potential for ethical failures with wider impact. If ethics are not properly considered during S&R design, implementation, and use, they can propagate unwanted biases and erode human trust in both the systems and the organizations that deploy them. Practitioners designing S&R systems that will become a key part of consumer life must take these risks into account.




New vectors of risk are scattered throughout the data supply chain. How businesses, governments and NGOs address this risk is critical to their ability to operate. As ethical data concerns grow, organizations need to find a new way forward, and should embrace the opportunity: This new ethical frontier offers a way to engender trust and provide vital differentiation in a crowded marketplace. Organizations should reduce their exposure to digital risk by integrating a wide array of data ethics practices throughout their data supply chains. In doing so, they’ll gain the trust of stakeholders, reap business benefits and position themselves for prolonged success.


Data Ethics Research Initiative

Launched by Accenture’s Technology Vision team, the Data Ethics Research Initiative brings together leading thinkers and researchers from Accenture Labs and over a dozen external organizations to explore the most pertinent issues of data ethics in the digital economy. The goal of this research initiative is to outline strategic guidelines and tactical actions businesses, government agencies and NGOs can take to adopt ethical practices throughout their data supply chains.