Data first: The critical foundation for your GenAI Launchpad

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Generative AI has opened up new frontiers in how the government executes its mission, but GenAI is only as good as the data that powers it. How strong is your data foundation?

Mary Ellen Buzzelli
Mary Ellen Buzzelli
February 9, 20247 mins
Data First: The Critical Foundation for Your GenAI Launchpad

In the past year, Generative AI (GenAI) has dominated media headlines for its potential to drastically transform the way organizations design and deliver digital services. While government agencies have already embraced AI in the form of chat bots, social media monitoring, predictive policy and more, the capacity of GenAI to analyze vast amounts of data, simulate human-like reasoning, and adapt to dynamic situations has opened up new frontiers in how the government executes its mission.

In fact, in a recent survey from NASCIO, 53 percent of state CIO respondents stated GenAI will be the most impactful emerging IT area in the next 3-5 years. NASCIO’s annual list of State CIO Top Ten Priorities for 2024 even includes AI for the first time ever.

It’s clear that state and local governments are excited about GenAI, but are they ready to harness it for real value?

Building your GenAI Launchpad

According to NASCIO, only 27 percent of state CIOs stated they have mature governance over enterprise information management - a critical and foundational element for GenAI implementation. Like many emerging tech applications, GenAI is only as good as the information it’s reasoning over, and that information has to be packaged and fed to GenAI in formats it can understand. That is, structured, digitized and accessible data able to be used for analyzing, sharing and informing decision-making.

Data is the lifeblood that fuels innovation. A strong data foundation acts as the cornerstone for developing powerful and reliable GenAI models. Here are four keys to establishing a solid data foundation:

  1. Digitize and Standardize Data:
    1. Digitize Paper Records: Convert paper records into digital formats using optical character recognition (OCR) to automatically generate meaningful metadata.
    2. Standardize Digital Data: Digitized records and existing digital data should be ingested into a centralized repository. Standardization requires organizing data in a uniform manner, such as using a common file format, naming conventions, and data structure.
  2. Implement Data Quality Control:
    1. Cleanse and Validate Data: Identify and rectify errors, inconsistencies, and inaccuracies in your data. Proper digitization will provide this automatically, with a human in the loop to ensure accuracy.
    2. Enforce Data Entry Standards: Establish guidelines for entering new data, including validation rules, review processes, and day-forward paper digitization to prevent errors at the source.
  3. Create Metadata and Taxonomies:
    1. Generate Metadata: Go beyond record-level metadata to develop metadata for each dataset, including information about data origin, creation date, and any relevant contextual details. This metadata is crucial for understanding the data's meaning and lineage.
    2. Build Taxonomies: Create structured classifications or taxonomies that define relationships and categorizations within your data. This helps organize information and facilitates more efficient searching and analysis.
  4. Implement Data Governance:
    1. Define Data Ownership and Responsibility: Clearly assign ownership and responsibility for different datasets within your organization. This ensures accountability and helps maintain data quality.
    2. Establish Data Governance Policies: Develop and enforce policies regarding data access, security, and privacy. This includes defining who has permission to modify or delete data and setting guidelines for data sharing.

While each state’s AI roadmap must be tailored to its specific needs, strategic plans and priorities, including these important considerations ensures the establishment of a solid data foundation for the seamless integration of AI into state IT initiatives. As we see more states implement GenAI, we gain greater insights into the best practices in governing the technology. States including Kansas, New Jersey and Washington have already published state GenAI policies, which act as leading examples for the states that will soon follow.

The future of GenAI’s use in government holds enormous promise, but it is the responsibility of state and local leaders to prioritize the foundational data elements to make GenAI a safe and strategic asset.

To learn more about how Iron Mountain can help catalyze your GenAI journey, get in touch!

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