Bad Data: An Enterprise-level Threat | InformationWeek

By using tools to monitor behavior and context around data’s provenance, organizations can mitigate risks and begin to address issues that might be incentivizing deceit in the first place. Individual instances of manipulated data might have minimal impact, but a bevy of deceptions can skew business outcomes. Researchers at the University of Warwick have studied the way some Uber drivers organize simultaneous sign-offs to cause a shortage of drivers — and trigger surge pricing. Knowing that they’re participating in systems managed by algorithms, these drivers are trying to make the system work in their favor — at the expense of Uber’s efficiency.

Dynamic pricing algorithms also demonstrate the growing need for companies to understand motives for disclosing, or disguising data. For instance, product reviews on Amazon became subject to data manipulation when third-party sellers began paying people to submit fake reviews to inflate their product and seller ratings. Amazon’s response? Giving more weight to verified reviews from customers who had definitively purchased the item and banning reviews from people who received free or discounted products outside the program’s curated process.

Read complete article here:

Bad Data: An Enterprise-level Threat – InformationWeek.