Precisely Software Inc.
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As agencies move to implement AI throughout their organizations, most are finding their AI efforts disrupted by fragmented, low-quality, or inaccessible data. This is a problem government shares with the private sector – a recent survey found that 79% of respondents said their AI initiatives are being hindered by limited access to data across environments.
These are not new problems for the government. There are still issues with data fragmentation and silos, poor data quality, complexities in applying governance and security requirements, and technical debt – legacy systems aren’t designed for modern analytics needs. And. of course, the pace of data generation continues to accelerate, adding to the pressure to clean and restructure massive numbers of datasets. That recent survey found that 60% of AI projects may be abandoned due to poor data readiness.
Learning Objectives:
- Outline the particular challenges in data readiness faced by your agency
- Delineate the steps to address those challenges, including prioritization and resource allocation
- Establish metrics to measure improvements in data quality so your agency datasets can be used by AI tools to produce trusted solutions