Policy by Algorithm (Jeff Henig), Part 2
Jeff Henig is a professor of political science and education at Teachers College, Columbia University. This post appeared July 27, 2011 on Rick Hess’s blog in Education Week.
There is a satisfying solidity to the term “data-based” decision-making. But basing decisions on data is not the same thing as basing them on knowledge. Data are collections of nuggets of information. Compared with “soft” rationales for action–opinion, intuition, conventional wisdom, common practice–they are hard, descriptive, often quantitative.
When rich and high quality sets of data are mined by sophisticated and dynamically-adjusted algorithms, the
There is a satisfying solidity to the term “data-based” decision-making. But basing decisions on data is not the same thing as basing them on knowledge. Data are collections of nuggets of information. Compared with “soft” rationales for action–opinion, intuition, conventional wisdom, common practice–they are hard, descriptive, often quantitative.
When rich and high quality sets of data are mined by sophisticated and dynamically-adjusted algorithms, the
