Mar 18 2024
Data Analytics

State and Local Agencies Can Prepare for AI Success with Strong Data Governance

Governments should ensure data accuracy and ethical use while aligning artificial intelligence with business goals.

As artificial intelligence becomes increasingly prevalent, user-friendly platforms such as ChatGPT have contributed to a shift in conversation. While concerns about AI have long dominated discussions, there’s growing excitement surrounding its many applications and efficiencies.

State and local agencies are looking for ways to leverage AI technology to help them boost productivity and enhance citizen services. These discussions are evolving, becoming more tangible as officials come to understand what defines successful AI implementation and acknowledge its potential challenges.

Data governance emerges as a critical component from the outset, offering a proactive and programmatic approach to mitigating AI-related issues.

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Fuel AI Success with Data Governance

With the growth of AI integration into government routines, concerns have shifted from a machine-generated takeover to ensuring the accuracy of generated information, safeguarding sensitive data and aligning the outcomes of adopted AI models with agency business goals.

Data governance serves as the cornerstone for responsible, ethical, secure and effective data utilization within AI systems. Safeguarding data quality, integrity and compliance significantly enhances the efficiency and precision of AI models.

Data governance plays a critical role in quality assurance for ethical AI by establishing frameworks and guidelines that govern the collection, storage, use and sharing of data. By systematically overseeing AI systems, data governance ensures that the data used to train AI models is accurate and reliable and will help mitigate bias. This accuracy is essential for dependable AI predictions, to help government agencies achieve improved decision-making, risk mitigation and compliance with federal, state and local regulations.

Data governance frameworks aim to promote transparency in the collection, processing and utilization of data for AI systems. They define roles and responsibilities and document data processes, enabling better understanding and auditing of AI systems. Ethical guidelines are also established and enforced to reduce bias and discrimination, which aids in tracing the sources and methods of AI development for better accountability and data lineage.

READ MORE: Important security considerations for embracing AI.

Accelerate AI Through Data Standards

Data governance facilitates AI acceleration by establishing standards and processes within organizations. This helps to enable self-service AI and analytics, and empowers team members to access data-driven insights, thereby democratizing data and fostering innovation across all levels. This empowerment also allows employees to resolve issues autonomously, freeing technical workers to focus on higher-level tasks.

Beyond the organizational and AI initiatives, data governance can ensure data sharing with stakeholders and citizens is reliable, consistent and secure. This dependable data is fueled by metadata, supported by accessible data catalogs, and reinforces the effectiveness of data governance measures.

Ultimately, this comprehensive approach not only accelerates AI adoption but also upholds critical aspects such as privacy standards, security, scalability and ethical considerations, aligning with broader organizational goals.

Increase Trust with Data Literacy and Lifecycle Management

For AI to be used effectively, agencies must educate all stakeholders about the organization’s data — its origin and location, and how it is being generated. Understanding data helps officials ask better questions and gain a comprehensive understanding of how to use AI to better serve the public. This is what’s known as data literacy, and it can safeguard state and local agencies from spreading misinformation while preserving their reputation as credible sources by enabling a clear understanding of data in context and through its lineage, a fundamental aspect of accurate predictive analytics.

Part of understanding data better is looking at the lifecycle of data and asking questions such as, is this data still relevant or accurate? Data lifecycle management involves continuous assessment to ensure the relevance and accuracy of data. It entails scrutinizing the data flow from its creation to utilization to retirement, aiming to identify outdated or inaccurate information that could compromise the reliability of predictions.

This proactive approach helps maintain data quality throughout its lifespan, ensuring that AI-driven insights remain dependable and aligned with organizational objectives.

WATCH: State CIOs discuss the power of AI for government.

Maximize the Impact of AI-Driven Analytics

The success of predictive analytics relies on aligning predictions with agency business goals. Predictions must support and drive business objectives forward, ensuring the relevance and usefulness of AI insights. This alignment ensures that expectations derived from predictive analytics actively contribute to advancing business goals. When predictions are closely intertwined with these objectives, they become instrumental in guiding strategic decisions, optimizing operations and fostering growth.

The quality, relevance and ethical use of data directly impact the efficacy of AI. Data governance ensures that AI isn’t just making predictions but making accurate, trustworthy and ethical predictions. It’s about laying the groundwork for a future where AI aligns seamlessly with objectives, shaping a more informed, responsible and thriving government enterprise.

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