Policy and data scientists have paid ample attention to the amount of data being collected and the challenge for policymakers to use and utilize it. However, far less attention has been paid towards the quality and coverage of this data specifically pertaining to minority groups. The paper makes the argument that while there is seemingly more data to draw on for policymakers, the quality of the data in combination with potential known or unknown data gaps limits government’s ability to create inclusive policies. In this context, the paper defines primary, secondary, and unknown data gaps that cover scenarios of knowingly or unknowingly missing data and how that is potentially compensated through alternative measures. Based on the review of the literature from various fields and a variety of examples highlighted throughout the paper, we conclude that the big data movement combined with more sophisticated methods in recent years has opened up new opportunities for government to use existing data in different ways as well as fill data gaps through innovative techniques. Focusing specifically on the representativeness of such data, however, shows that data gaps affect the economic opportunities, social mobility, and democratic participation of marginalized groups. The big data movement in policy may thus create new forms of inequality that are harder to detect and whose impact is more difficult to predict.

中文翻译：

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“为了更好的衡量”：大数据世界中的数据差距

政策和数据科学家已经充分关注收集的数据量以及政策制定者使用和利用数据的挑战。然而，很少有人关注这些数据的质量和覆盖范围，特别是与少数群体有关的数据。该论文认为，虽然似乎有更多数据可供决策者借鉴，但数据质量加上潜在的已知或未知数据差距，限制了政府制定包容性政策的能力。在这种情况下，本文定义了主要、次要和未知数据差距，涵盖了有意或无意丢失数据的情况，以及如何通过替代措施潜在地补偿这种情况。基于对各个领域的文献和整篇论文中突出显示的各种例子的回顾，我们得出的结论是，近年来大数据运动与更复杂的方法相结合，为政府以不同方式使用现有数据以及通过创新技术填补数据空白开辟了新的机会。然而，特别关注此类数据的代表性表明，数据差距会影响边缘化群体的经济机会、社会流动性和民主参与。因此，政策中的大数据运动可能会造成新的不平等形式，这些形式更难发现，其影响更难以预测。然而，特别关注此类数据的代表性表明，数据差距会影响边缘化群体的经济机会、社会流动性和民主参与。因此，政策中的大数据运动可能会造成新的不平等形式，这些形式更难发现，其影响更难以预测。然而，特别关注此类数据的代表性表明，数据差距会影响边缘化群体的经济机会、社会流动性和民主参与。因此，政策中的大数据运动可能会造成新的不平等形式，这些形式更难发现，其影响更难以预测。