Alexis Madrigal reporting on research conducted at Carnegie Mellon in The Atlantic finds that reading all of the privacy policies we encounter would take 76 work days a year. And that’s 76 days for every single one of us.
In the course of writing Ethics of Big Data, it became clear that there are aspects other than privacy to consider. Identity, ownership, and reputation round out four key elements of Big Data ethics.
Privacy is clearly of direct, relevant concern to everyone. The somewhat unsurprising time and cost associated with actually reading those policies represents a major opportunity for organizations to streamline communication of their position on the use of your data.
A nice start would be to make a simple change: call them something else. Data Handling Policy, Usage Agreement, or Customer Protection Commitment all broaden the scope of what organizations can consider in their policy design in order to develop deeper engagement and build more trusting relationships with their market.
By explicitly including coverage of a broad range of concerns, organizations demonstrate proactive interest in recognizing the concerns of people who use their products and services. And reducing the long-form legalese format not only makes them more accessible, as research in The Atlantic article demonstrates, decreasing the complexity has the added benefit of reducing the opportunity cost of learning exactly what is being done with all that data.
This isn’t just about customer service. It is also about seizing an opportunity to benefit from aligning your organizational values with your actions. Both internal and external communication of which values are driving policy and action provides a range of benefits:
• Faster adoption by consumers by reducing fear of the unknown (how are you using my data?)
• Reduction of friction from legislation from a more thorough understanding of constrains and requirements
• Increased pace of innovation and collaboration derived from a sense of shared purpose generated by explicitly shared values
• Reduction in risk of unintended consequences from an overt consideration of long-term, far-reaching implications of the use of Big Data technologies
• Brand value generated from leading-by-example