At a minimum, a data literacy program should cover:
What happens once data “leaves” the control of any one employee or user.
The impossibility of a static enterprise architecture in the age of data interdependency (due to use of third-party APIs, data partnerships and common frameworks).
Understanding of data at rest (stored data) versus data in motion (data being transformed into useful information by systems and people).
In the process of modeling potential uses for data, unspoken values will become apparent. If good feedback loops are in place, end users will be able to signal to developers where their values diverge from those of the developers, or where implementation does not bear out the intended result. Doctrine for data handling and management of consent needs to be incorporated not just at the edges of an organization’s human decision-makers, but at the edges of its computing infrastructure as well (as embodied in the algorithms used in analytics or machine-learning processes).
Doctrines, which can be defined as guidelines for effective improvisation, can be used to achieve this requirement. Coined in business usage by Mark Bonchek, the concept is sourced originally from military contexts, where commanders need to empower the soldiers on the front lines to have rules of engagement which not only specifically proscribe or restrict their actions, but also give them sufficient information to make smart decisions in line with a larger strategy, even when not able to directly communicate with the command structure.²³
Building data literacy Wise leaders will attend to management of consent and prevention of harm through good design, seeking informed consent from users, monitoring and managing consent over time and creating harm mitigation strategies. And with data literacy programs in place in their organizations, embodied in clear doctrines, their teams and partners will be able to fulfill the promises companies have made to their users—both to avoid harm and to create new value.
Further discussion of implementing a doctrinal approach can be found in “Code of Ethics.” The incorporation of values into machine teaching and learning is discussed at length in “Ethical Algorithms for Sense & Respond Systems.”