Distributed-to-Centralized Data Management Through Data LifeCycle Models for Zero Emission Neighborhoods

DaLiF: a data lifecycle framework for data-driven governments

“…There are numerous benefits deriving from designing and implementing in a consistent way a data lifecycle for PAs. These benefits include, but not limited to the followings: (i) ease in planning and handling complexity of data management in all data life phases [15,[42][43][44][53][54][55], (ii) identifying and illustrating a sequence of all essential activities related to data, (iii) support organizations for the preparation of data products for the data users [42-44, 54, 55], (iv) help data users to have a well understanding of the data assets available to them [56], (v) effective gathering of data including metadata from various (internal and external) sources [53,57,58], (vi) implementation of the once-only principle [59], (vii) creation of a homogeneous set of data through consolidation [6,60], (viii) identify, remove noise, uncertainty, and errors in collected data, and maintain data quality [56,61,62], (ix) addition of appropriate data for completion and improvement [61,63], (x) better analysis of data to extract knowledge and discover new insights so that policymakers use this knowledge to generate desire value [42][43][44]61] (xi) visualize data for a better understanding of a common person and its usage for future course of actions [58,64], (xii) support to adopt appropriate data storage approach to ensure the data availability and scalability [15,65], (xiii) assistance to promote the use of data with the consent of the owner of data [66,67], (xiv) create an opportunity to the stakeholders to offer their viewpoints on the data [49,52], (xv) aid PAs to ensure the protection of big data, including personal data, and promote effective governance [6...…”

“…The other existing data lifecycles include: Data Lifecycle for HPC Scientific data perspective [114], Data lifecycle for cloud automation tools [40], Data Lifecycle for Telco networks data management [115], Energy big data lifecycle [116], Data lifecycle for the Tobacco industry [117], Data lifecycle for cloud computing [118];Data lifecycle for cloud data [119], Data lifecycle for IoTs [120], Personal data lifecycle [121], Data lifecycle about the coal mine industry [122], Data lifecycle for smart healthcare [123], Data Lifecycle Model for NSF [94], Data lifecycle cycle for smart cities [13], Storage data lifecycle [124], Research data lifecycle [125], lifecycle for CENS Data [126], data lifecycles for industry [127,128], a lifecycle for big scholarly data [129], a lifecycle for social and economic data [46], Data lifecycle for manufacturing [130], Research data lifecycle [131], a lifecycle for big healthcare data [23,132], data lifecycle [133], a lifecycle for environmental research data [134], a lifecycle for big data value creation [26], a lifecycle for big data analytics for psychologists [135], the information pyramid of Reynolds and Busby lifecycle [17], Yuri Demchenko data lifecycle [10], Data lifecycle [58], SCC-data lifecycle [44,54], Data value cycle [136], Lifecycle in databases [137], Knowledge process-lifecycle [138], CMM for Scienti...…”

“…• Adherence to the once-only principle, in public sector organizations, to collect data once from citizens, and business community [59]. • Manage the ranges of valid and trusted data sources for data collection [42][43][44]54]. • Manage the massive amount of data in any format to handle big data volume challenges and search and discover new sources for data collection [42][43][44]54].…”

“…• Manage the ranges of valid and trusted data sources for data collection [42][43][44]54]. • Manage the massive amount of data in any format to handle big data volume challenges and search and discover new sources for data collection [42][43][44]54]. • Consider specific resources to manage the big data 'Velocity' challenge that refers to the speed rate of data stream generation and the consequent ability to process it well [42][43][44].…”

“…The analysis phase includes analysis of unstructured data [91]. The output of the data analysis phase includes knowledge, the discovery of new insights, new data, interpretations and/or new datasets [42,54,61,165].…”