Data Readiness is Foundational to Modernizing Government

Entity vs. Data Quality feature image - Susan Smoter

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Nov 09, 2023

Entity quality and data quality are related concepts, but they are not the same. While they both deal with the accuracy and reliability of information, they focus on different aspects within a dataset, especially in the context of e-government systems.

Entity Quality

Entity quality refers specifically to the accuracy, consistency, and reliability of information about individual entities (such as people, organizations, or objects) stored within a dataset or database. In an e-government context, entities could include citizens, businesses, government agencies, services, and more. Entity quality emphasizes the correctness and integrity of the data associated with these entities. It is concerned with ensuring that the information about these entities is up-to-date, complete, correctly formatted, and properly linked.

Data Quality

Data quality, on the other hand, is a broader concept that encompasses various attributes of data within a dataset, not limited to entities. It addresses the overall reliability, accuracy, consistency, and usability of data, including aspects beyond individual entities, such as data completeness, timeliness, validity, and relevancy. Data quality also encompasses issues related to data integration, data transformation, data cleansing, and data governance. It applies to all types of data, whether they are entities, attributes, relationships, or other forms of information.

As government considers using AI to help create modern services, it’s critical that agencies have their data ready to be used by AI, Machine Learning, Generative AI and other innovative technologies.

Data Readiness

The journey to AI for better, modernized government services begins with data readiness. Data readiness refers to all necessary steps to ensure your data is prepared for AI purposes, which consist of ensuring the foundational, operational, and transformational quality of your data to enable successful AI deployment.

  • Foundation: Your data has the appropriate infrastructure and interfaces.
  • Operation: Your data is suitable for management and governance to maintain your AI solutions.
  • Transformation: Your data is optimized to ensure success with your AI deployment.

The success of your AI is only as good as the quality of your data. Therefore, you need to understand your current level of data readiness, so you know what work lies ahead for you to prepare it for AI purposes.

Why Data Readiness is Critical to Have a Complete Understanding

AI relies on data readiness for several reasons. The first reason is to gain some control over the vast amounts of unstructured data such as in news stories, articles, intelligence case files, and emails, which accounts for 80–90 percent of all data. The value of that free-form data is a gold mine of information and provides insights that no amount of human effort could ever provide.

For government agencies that are considering AI deployments, they must invest in ways to ensure the quality and readiness of their structured data and have innovative AI tools to incorporate unstructured data to help drive achieving decision intelligence.

(This post was first published on the “AI for Better Government” newsletter on LinkedIn. Republished with the author’s permission.)

Author

  • Susan Smoter
    Susan Smoter is passionate about applying IT Services to solve real world problems using creativity, technology, and solid people skills to break through bureaucracies to create effective and meaningful change. She is all about creating new ways to do work, getting higher productivity and reducing costs by empowering the workforce. Susan publishes the "AI for Better Government" newsletter on LinkedIn, discussing the hopes and fears that come with new technology and how to make a better government.
    View all posts Quantexa

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