The City is one of the top-ranked metropolitan areas in the United States. The City’s regional economy is versatile and spread across various verticals, with a robust emphasis on life sciences, agribusiness education and research, logistics, manufacturing, aerospace, and professional services. Big and little companies located in and around the City are connected to plenty of transportation options, research and talent development programs, competitive land options, and a supportive business environment. The City strives to become a “Smart City” and as such promotes the use of data to enhance the “Internet of Everything.”
As with all smart city initiatives, the goal is to harness data to promote and develop things that make urban life better and cheaper and more intuitive. As a part of this initiative — City Administrators were looking for an end-to-end, comprehensive, enterprise-scale, Master Data Management solution that can promote government transparency, accountability and provide citizens with information that encourages and invites public participation and feedback. The solution should ingest large volumes of data from various sources in real-time and build an MDM Citizens model so as to ingest reference data back into originating systems plus enhance meaningful transparency reports.
As one of the top metropolitan areas in the country, the City’s administration has to deal with an enormous amount of critical data. The City’s IT department carries different information including Criminal Justice Information (CJI), Payment Card Industry (PCI), Health Insurance Portability and Accountability (HIPAA), Federal Energy Regulatory Commission (FERC) and other PII. The administration has to deal with critical data in a more clustered and inefficient approach making it nearly impossible to obtain operational analytics and real-time reports out of it. The challenge for the City was streamlining efficiencies in sharing frequently requested city datasets from various sources that are needed for analytics and reporting purposes by standardizing a method of publishing and disseminating the data through a central location.
All datasets of the City have structured, unstructured data this was a major challenge to maintain a single version of truth data. We used the DQLabs to abstract the entire process of extract, transform, load, (ETL) and achieved credibility and consistent data through data quality and master data management services. This process included merging a variety of data sources, validating the quality of the sources up to the level of each attributes, cleansing and then going through a process of consolidation, linking and de-duplicate before the golden key records are created. Almost 2/3 of the duplicate data was removed using smart logic of consolidations upon curation resulting in an unified view of the citizens model. Further, the profile and curation process included AI/ML based supervised and unsupervised algorithms and the entire platform was built based on patent pending DataSense logic. This enabled to understand the pattern, trends and its relevant data quality rules and the calculation of Data Quality Score (DQScore) at each attribute level. Each of the curated data sources were available via a single repository in Azure DataLake as an output plus also accessible via PowerBI using the same permissioning provided in the portal for ease of Data Governance. Now with the integrated golden record view, now any reporting data could be overlaid for reporting, and analytics.
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