Organizations may be related in terms of similar operational procedures, management, and supervisory agencies coordinating their operations. Supervisory agencies may be governmental or non-governmental but, in all cases, they perform oversight functions over the activities of the organizations under their control. Multiple organizations that are related in terms of oversight functions by their supervisory agencies, may differ significantly in terms of their geographical locations, aims, and objectives. To harmonize these differences such that comparative analysis will be meaningful, data about the operations of multiple organizations under one control or management can be cultivated, using a uniform format. In this format, data is easily harvested and the ease with which it is used for cross-population analysis, referred to as data comparability is enhanced. The current practice, whereby organizations under one control maintain their data in independent databases, specific to an enterprise application, greatly reduces data comparability and makes cross-population analysis a herculean task. In this paper, the collocation data model is formulated as consisting of big data technologies beyond data mining techniques and used to reduce the heterogeneity inherent in databases maintained independently across multiple organizations. The collocation data model is thus presented as capable of enhancing data comparability across multiple organizations. The model was used to cultivate the assessment scores of students in some schools for some period and used to rank the schools. The model permits data comparability across several geographical scales among which are: national, regional and global scales, where harvested data form the basis for generating analytics for insights, hindsight, and foresight about organizational problems and strategies.

组织可能在类似的操作程序，管理和监督机构之间进行协调。监督机构可以是政府机构，也可以是非政府机构，但在所有情况下，它们都对受其控制的组织的活动执行监督职能。多个由其监督机构进行监督职能相关的组织，在地理位置，宗旨和目标方面可能存在很大差异。为了协调这些差异，使比较分析变得有意义，可以使用统一格式来培养有关多个组织在一个控制或管理下的运营数据。采用这种格式，可以轻松收集数据，并且可以轻松地将其用于跨种群分析，所谓的数据可比性得到了增强。当前的做法是，组织处于一个控制之下，将其数据保存在特定于企业应用程序的独立数据库中，这大大降低了数据可比性，并使跨人口分析成为一项艰巨的任务。在本文中，并置数据模型被公式化为由数据挖掘技术以外的大数据技术组成，用于减少跨多个组织独立维护的数据库固有的异构性。因此，表示并置数据模型能够增强多个组织之间的数据可比性。该模型用于培养一段时间内某些学校的学生评估分数，并用于对学校进行排名。该模型允许跨多个地理范围的数据可比性，其中包括：