As agencies navigate how to maximize the ROI of their AI, data preparation and access are paramount. Agencies often spend more time wrangling their data than using it, devoting countless hours to cleaning and reconciling data without a clear way to validate its trustworthiness and validity. In an age of rapidly advancing AI capabilities and potentialities, establishing a strong data foundation is vital to aligning these advances with agency missions.
A robust data foundation is built on secure connectivity to diverse data sources, real-time hybrid pipelines, and built-in governance enhanced by AI-driven data quality scoring. By laying this groundwork, agencies can then automate the majority of data preparation and verification, producing data that is trusted, up-to-date, and ready to generate insights that support and enhance the mission. And with the ability to stream data in real time, leaders can respond to logistical issues, fraud, emergencies, and policy changes as they happen.
“At the end of the day, a good data foundation isn’t just about moving data, it’s about delivering trusted, analytics-ready data products. That’s what lets agencies actually use the data for dashboards, AI models, and better decision-making,” said Daniel Fager, Solutions Architect for Data Integrations at Qlik.
In this video interview, Fager explains how to create a framework for maximizing the value of your data. Topics include:
- The elements that define a strong data foundation
- How automated updates and pipelines ensure data is timely while also removing repetitive, manual work and reducing errors
- How AI can help guarantee that data is reliable
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