But those principles could pose challenges for researchers who want to draw on Big Data, says Federal Trade Commission Commissioner Maureen Ohlhausen.
“Maximizing the benefits of big data may create tension with the notice and the purpose limitation principles,” she writes in a letter sent to the National Telecommunications and Information Administration last week.
Fair information practice principles generally call for companies to explain why they're collecting data and obtain consumers' consent. The principles also require companies to limit their collection and retention of data.
The problem, Ohlhausen suggests, is that Big Data can yield insights that no one expected when the information was gathered. “Companies cannot give notice at the time of collection for unanticipated uses,” she writes.
What's more, she says, requiring companies to destroy unnecessary data could hinder researchers' abilities to make unexpected connections. “Part of the promise of big data is to pull knowledge from data points whose value was previously unknown,” she writes. “Strictly limiting the collection of data to the particular task currently at hand and disposing of it afterwards would handicap the data scientist’s ability to find new information to address future tasks.”
Ohlhausen was among the commenters who responded to the NTIA's call for opinions about whether new laws are needed to address some of the potential pitfalls of Big Data.
Some privacy advocates who weighed in called for new restrictions on companies' ability to collect data -- particularly information about health, race and age. But industry groups like the Interactive Advertising Bureau and Direct Marketing Association say no new legislation is needed. “Regardless of quantity, data is data -- it may be used for good or for harm,” the DMA said in its comments.
For her part, Ohlhausen says there are strategies -- like “de-identification” of data -- that could allow researchers to draw on data while also complying with fair information practices. “I believe FIPPs remains a solid framework and is flexible enough to accommodate a robust big data industry, but we have some work to do to resolve these tensions,” she writes.