12 Steps to Building Digital TrustBy Guest Author Print
Weak ethical data practices can damage consumer trust in a brand, so companies must embed strong data ethics practices throughout decision-making processes.
· Don’t collect data just for the sake of having more data. The power and peril of data analytics is that data collected today may be useful for unpredictable purposes in the future. Give due consideration to the possibility that less data might result in both better analyses and fewer risks.
· Data can be a tool of both inclusion and exclusion. While everyone should have access to the social and economic benefits of data, the processes of data collection, correlation and prediction can affect different people differently. Data professionals should listen to the concerns of affected communities and mitigate the disparate impacts of their products.
· Explain methods for analysis and marketing to data disclosers. Maximizing transparency at the point of data collection can minimize the more significant risks that arise as data travels through the data supply chain.
· Data scientists and practitioners should accurately represent their qualifications, adhere to professional standards and strive for peer accountability. The long-term success of this discipline depends on public and client trust. Data professionals should develop practices for holding themselves and their peers accountable to shared standards.
· Design practices that incorporate transparency, configurability, accountability and auditability. Not all ethical dilemmas have design solutions, but paying close attention to design practices can break down many of the practical barriers that stand in the way of shared, robust ethical standards. Data ethics is an engineering challenge worthy of the best minds in the field.
· Products and research practices should be subject to internal (and potentially external) ethical review. Organizations should prioritize establishing consistent, efficient and actionable ethics review practices for new products, services and research programs. Internal peer review practices help to mitigate risk, and an external review board can contribute significantly to public trust.
· Governance practices should be robust, known to all team members and regularly reviewed. Data ethics poses organizational challenges that can’t be resolved by compliance regimes alone. Because the regulatory, social and engineering terrains are in flux, organizations engaged in data analytics need collaborative, routine and transparent practices for ethical governance.
By integrating data ethics practices throughout their data supply chains, organizations can reduce their exposure to digital risk. That can help them gain the trust of stakeholders, reap business benefits, and position themselves for prolonged success in the digital economy.
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