How Much Data Do We Need? Balancing Machine Learning with Security Considerations

Summary: The article discusses the tension between data scientists’ desire for large volumes of data and the need for data privacy and security. It emphasizes the importance of finding a middle ground in data retention and usage, while also highlighting the complexities of managing data in organizations and the impact of data security regulations.

 How Much Data Do We Need? Balancing Machine Learning with Security Considerations

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How Much Data Do We Need? Balancing Machine Learning with Security Considerations

For a data scientist, there’s no such thing as too much data. But when we take a broader look at the organizational context, we have to balance our goals with other considerations.

Trnava University

Data Science vs Security/IT: A Battle for the Ages

Acquiring and keeping data is the focus of a huge amount of our mental energy as data scientists. If you ask a data scientist “Can we solve this problem?” the first question most of us will ask is “Do you have data?” followed by “How much data do you have?” We want to collect data because it is the prerequisite for most of the kinds of work we want to do, in order to produce valuable models and beneficial results.

Institutional Considerations

It’s so easy for a company to become a data hoarder. You begin with a shortage of data, and you’re flying blind, collecting data about transactions, business activities, etc. as you go to help inform decisions and strategy.

An aside about regulation

The growth in data security regulations in recent years has increased the challenges of the scenario I describe for businesses. In some ways, it was our own doing — numerous data breaches, lax security, and opaque consent policies by assorted companies over recent years have led to public demand for better, and government filled the gap.

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