06 July 2023

Streamlined Citizen Data for Government Excellence

We collaborated with a leading Middle East government agency to streamline citizen data through efficient de-duplication, enhancing operational efficiency and service delivery.

Streamlined Citizen

Company Overview:

Our client, a key government entity in the Middle East, embarked on an innovative journey to offer advanced electronic services for enhanced citizen access to judicial and legal provisions. This initiative aimed to enable effective and efficient citizen engagement, contributing to overall societal well-being and satisfaction.

Major Challenges:

  • Disconnected Databases: Lack of integration among prosecution, crime, and notary databases.
  • Lengthy Decision-Making: Extended Turnaround Time (TAT) for issuing legal documents.
  • Multi-Department Dependencies: Dependencies across multiple departments for finalizing approvals.
  • Truth Access Complexity: Absence of a unified platform for accessing a single version of truth.
  • Inaccurate Decisions: Higher instances of false positives and false negatives.

Our Solution & Benefits:

  • Unified Citizen Databases: We integrated citizens’ data into a consolidated master, enhancing accessibility and reducing redundancy.
  • Data Enrichment and Standardization: Leveraging SAS Text Miner and SAS Contextual Analytics, we enriched and standardized text libraries in various languages.
  • Efficient Decision-Making: De-duplication rules were designed for prosecution, crime, and notary information, leading to quicker decisions, approvals, and reduced TAT.
  • User-Friendly Access: A user frontend was integrated to ensure swift enterprise-wide access to critical information.

Products & Services Utilized:

  • SAS Text Miner: Deployed for building text mining models from newspaper and government portal text data.
  • SAS Contextual Analytics: Utilized for text library enrichment in different languages.
  • SAS Visual Analytics: Implemented as a unified information portal for government and citizens’ sentiment analysis.