31 January 2020
The introduction of new privacy legislation – such as GDPR in Europe and the CCPA in California – is (rightfully) giving individuals the ability to dictate what personal data about them is held by companies and how it is used. And while much of the focus has been on companies making sure their marketing complies with the new rules, there are other less obvious, but equally important data-driven activities which are seeing impact from the changes. One key area for retailers and their analytics’ partners is the practice and application of data science to develop customer insights that can inform assortments, pricing and customer strategies. Let’s look at three examples where privacy legislation is having an impact:
It’s not enough to just “let the computer decide”. With the new privacy legislation requiring that algorithm-based decisions are fair, the data scientist or author of the algorithm needs to take responsibility for ensuring it delivers fair results.
But to be fair and unbiased there are some key considerations when applying data science:
Some legislation has given individuals the right to erase and rectify their data, which extends to any copies of individual-level data that may have been used to build models.
This has the following implications for the data scientist:
As it becomes more important to keep privacy in mind from the outset of any project, there are interesting science approaches that aim to analyse data without ever knowing the data attributable to any individual. For example, differential privacy introduces a random element so that the algorithm can never know any particular data point with certainty, but at the aggregate level the error reduces allowing for accurate high-level statistics. This is especially valuable in cases where consumers are asserting ownership of their own data and wanting services that are both personalised and private.
So, the changes to global privacy laws are clearly influencing the world of data science and where it intersects with retail, but in a positive way. Not only does this introduce a required update to best practice when it comes to developing customer insights from data, but it also forces the data scientist to consider how they utilise data in predictive modelling and to ensure that their approaches are fair.