Lessons from Data Privacy Day: Aspiration vs. action

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Each year, Data Privacy Day highlights the growing corporate commitment to protecting user data.

While most organizations acknowledge the importance of using user data to drive insights, many underestimate the challenge of making this data available for use in an efficient way that aligns with the growing expectations of consumers and regulators. For customers to share accurate and sometimes personal details about themselves with an organization, one factor is of ultimate importance: trust.

As the conversation concerning trust and data privacy has been elevated to the mainstream, it reveals a gap that organizations are facing increasing pressure to address.

Organizations are increasingly aware that the prominence of data privacy across industries isn’t a phase, or a blip. Data protection challenges have evolved and become more complex due to the variety and volume of data and the massively increased number of people who need access to it.

Further emphasizing this gap is the rapid rate of technological growth, particularly the host of new data-driven technologies entering the market to address these challenges. This surge in our desire and ability to consume and process data comes with new risks and massive opportunities.

One of the promises of the Fourth Industrial Revolution is the shift to data-driven decision making, including powerful techniques for linking and analyzing data across silos, and enabling organizations to refine strategy, improve customer experience, evolve products and many other high value use cases. Modern data platforms, especially those built on cloud infrastructure, make it easier to link and process this data, bringing the dream of machine intelligence to life.

But organizations that share without permission or fall victim to a breach (arising from external or internal threat vectors) will feel the impact of reduced customer trust and/or regulatory penalties in many geographies. The consequences of mistrust can be wide ranging and severe, with the potential to damage an organization’s reputation and bottom line.

Data protection strategies need to be industrialized and implemented at scale to meet these challenges, reduce negative impacts, and perhaps most importantly, enable data leaders to freely embrace data-driven innovation.

Every modern data platform needs to incorporate the ability to manage and control the lifecycle of data released to users, the linkage risk between these datasets and the ability to implement appropriate, contextualized privacy controls, giving data leaders a unified view of how sensitive data is being managed across the organization. With this approach, organizations set themselves up for innovation and the ability to adopt new capabilities as they prepare to scale.

Advanced analytics and machine learning require diverse datasets, often in large volume. This creates a conflict with data minimization principles. However, privacy technologies designed to enable data science on sensitive data have evolved rapidly in parallel and offer organizations ways to safely compute on sensitive data.

Many methods are increasingly available including anonymization techniques to reduce the risk of unintended re-identification, advanced cryptography to allow computation over encrypted data, secure multi-party computation patterns and federated learning. Some of these come with severe constraints, but it’s a nascent field that is also rapidly evolving.

Being open, honest and fully transparent about how data is used – and showcasing the benefits to consumers by delivering the promised improved experiences – can go a long way for organizations to win consumer trust. By embracing modern data privacy techniques, there is an open opportunity for organizations to embrace these challenges and turn them to competitive advantage as consumers look to buy from brands they can trust.

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