Denisa Popescu et al.
Building Block Description Actions Metrics
What is the impact of EIM on the bottom line?
Enabling Infrastructure
What is the role of technology in EIM?
Define citizen and business related metrics to measure client satisfaction, time savings, etc. Define standards for information quality and incorporate ways to measure and monitor information quality as part of project management tasks. Use metrics to identify and support productivity goals. Use a balanced scorecard to chart data quality levels of institutional systems. Implementation of foundational aspects of EIM as common shared services. The information architecture is extensible and distinguished from the application architecture. Distinct architectures for analytics, master data and unstructured content are unified at a logical level. An authoritative and managed source of master data supports transaction processing, document / information / web management and information delivery. A metadata management and semantic reconciliation infrastructure resolve inconsistencies and support serviceoriented architecture objectives. A current, valid Common Information Model is published and maintained. Data models are maintained by key systems but aligned to enterprise information architecture. A data service layer with published metadata and business rules is in place to achieve integration.
Information Quality Assessment: Build in routine data quality procedures to identify root causes of poor data quality. Collect and use data quality metrics to support improvements in business processed and productivity goals. Produce a periodic data quality index for institutional processes and systems
Data and Information Architecture Rationalized data architecture for analytics, master data, structured and unstructured content. Mandate adoption of enterprise wide taxonomies for institutional applications such as base registries. Metadata Management Integrated Metadata Repository across electronic document and records management systems. Standardized document and records classification standards as it relates to retention scheduling and disposition. Drive data retention and disposition through metadata. Integration of automated metadata extraction into document lifecycle management. Common Information Model( CIM) Maintain and publish CIM. Review project data models and analyze impact on CIM; data models are maintained locally but aligned with CIM. Document existing data flows and architectures for analytics, master data and unstructured content. Establish naming conventions and a Enterprise Data Dictionary. Formalize semantic reconciliation to resolve inconsistencies. Master Data Management On‐line metadata definitions / business rules. Subscription services. Workflow / alerts for master data management. Data Services Layer and Technical Interoperability Standards Data Service layer Data sharing standards across government and with external parties Information Security Corporate service for an electronic identity. Authoritative source for employee data.
Further, the implementation of the technology aspects of enterprise information management, such as the metadata repository, reference data and taxonomies, data services, and identity registry, can be addressed through common shared services. Shared services are now considered to be a building block of interoperability in e‐government as they can be shared and re‐used among different governmental entities when developing new systems( Janssen, 2012). Implementation of EIM through common shared services needs to be supported by a management structure that expands the standard definition of IT shared services offerings from technical infrastructure services and ERP platforms( Human Resources, Financial Management) to a broader range of
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