Top 10 Ways to Harness your DG Platform for Data Privacy

In a recent UK Case, the England and Wales High Court court ruled that the use of a vendor’s API to access and extract the customer’s own data constituted a use of, not just the API, but also the vendor’s underlying application. As a result, the customer is required to have a separate applic…

It’s no secret the global pandemic has upended every industry, but the health care and data communities have arguably faced the most significant challenges. Innovation in health care has been forced to accelerate at a much faster pace. There is obviously a critical need for personal health care data to both understand and combat the pandemic, which has inevitably led to rising concerns around privacy. This comes back to the same question I have posed in my last article: How do we balance our privacy and healthcare needs amid COVID-19?

Data-driven decision making has enabled companies to significantly harness value from data in all aspects of business. Chief data officers’ (CDOs’) leading digital transformation efforts are seeing the importance of data from collection to processing to learning insights from datasets. With innovations in hardware, cloud and algorithms and availability of large datasets and analytical tools, the adoption of data science, artificial intelligence (AI) and machine learning (ML) is exploding. In addition, the AI/ML revolution has provided an impetus to ensure the collection, processing, storage and governance of data are robust and well-managed within the organization. Recognizing the value of data to the enterprise, data governance efforts have scaled up to identify, address and mitigate risks associated with data. As more and more decisions become automated, AI/ML models — which rely heavily on data — face challenges associated with interpretability, bias, explainability, fairness and model governance. Most organizations undergoing digital transformation efforts recognize that data governance and AI/ML model governance are heavily intertwined but continue to treat data and model governance in two separate silos.