The Public Safety company, one of the top-ranked metropolitan areas in the United States, is on a mission to strengthen outreach, city-wide planning, and engagement services using data. The client provides a wide range of services to its residents, including public safety, utilities, transportation, and parks and recreation. To efficiently manage these services and make data-driven decisions, the city relies on a large amount of data from various sources. However, the data quality of these sources has been a challenge for the city, leading to inaccurate and inconsistent data that hinders decision-making.

Some of the primary data quality challenges faced by the Public Safety company include data silos, data inconsistency, and data completeness. The city has numerous departments and systems that store and manage data, making it difficult to integrate data from different sources. This leads to data silos, which prevent the city from having a complete view of its data. Additionally, inconsistencies in data definitions and data entry practices have led to data quality issues, such as data duplication, missing data, and incorrect data. Finally, incomplete data sets make it difficult to draw meaningful insights and inform data-driven decisions. To overcome these challenges, the client needs to implement data quality management practices and a modern data quality platform that ensures data accuracy, completeness, and consistency.