Ready or Not: Building & Deploying AI-Ready Data

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Ready or Not: Building & Deploying AI-Ready Data

Written by Gimmal Product Marketing

Dec 17, 2024

Artificial Intelligence (AI) is transforming industries at an unprecedented pace, offering organizations the potential to unlock new levels of efficiency and innovation. However, amid the excitement, a critical question arises: Is your data ready for AI?

In a recent webinar hosted by Gimmal and Innovative Driven, we explored the essential steps organizations must take to prepare their data for AI deployment. The discussion shed light on the challenges, risks, and practical strategies for building AI-ready data while ensuring compliance, security, and ethical considerations.

YouTube Video: Ready or Not: Building & Deploying AI-Ready Data  

The Hype vs. Reality of AI Adoption

AI is everywhere, and many organizations are eager to harness its potential. However, there’s a trend of racing to adopt AI without clear objectives or understanding the necessary groundwork. Employees are testing and experimenting with AI tools without specific parameters or desired outcomes, leading to potential risks and inefficiencies. 

It’s essential to recognize that AI isn’t a magical solution that can fix underlying data issues overnight. AI will not automatically clean or organize your data. Without proper preparation, AI initiatives may not deliver the expected value, and could even introduce new challenges. 

A survey of Chief Data Officers from MIT highlights this disconnect: 

  • 75% believe AI will transform their organizations and create new value. 
  • 62% are increasing their AI spending. 
  • Yet, 78% have not realized value from their AI investments. 

The underlying issue? Data that’s not ready for AI. 

Garbage In, Garbage Out: The Data Dilemma

Organizations today are data-rich but insights-poor. The vast amounts of data accumulated over the years has become a double-edged sword. Without proper management, this data becomes a liability rather than an asset. 

AI shines a light into the darkest corners of your data estate. Tools like Microsoft 365’s Copilot can inadvertently access and disseminate sensitive information buried within unstructured data. 

For example, if a confidential HR document containing sensitive employee information is accessible to AI tools, this data could be unintentionally included in new documents or reports. Before long, sensitive information could proliferate across the organization without anyone realizing the breach. 

AI as a Catalyst for Proactive Information Governance

The emergence of AI presents a pivotal opportunity for records and information management (RIM) professionals. AI provides a compelling business reason to enhance records and information management practices, moving beyond risk mitigation to driving organizational value. 

Effective AI deployment requires proactive data governance. This includes ensuring that data is: 

  • Structured 
  • Standardized 
  • Accessible 
  • Protected 

Without these elements, AI initiatives can lead to inaccurate insights and unreliable outcomes. By focusing on data quality and governance, organizations can harness AI to propel the business forward rather than being held back by outdated or irrelevant data. 

 

The Three Cs: A Roadmap to Data Readiness

The Three Cs: A Roadmap to Data Readiness 

To effectively prepare data for AI, we recommend the “Three Cs” approach: 

  1. Comprehend

Start by fostering good data hygiene habits among users. Educate employees on: 

  • Where to store data 
  • Proper naming conventions 
  • Sharing protocols 
  • Data retention practices 

This step focuses on change management and user behavior. By ensuring everyone understands their role in data management, organizations can begin to improve data quality without significant technological investment. 

  1. Combine

Reduce data sprawl by consolidating data storage. Many organizations have data scattered across multiple platforms—such as Box, Dropbox, Microsoft 365, and Google Drive—leading to duplication and complexity. Combining data into a unified system simplifies management, reduces risk, and provides a consistent foundation for AI initiatives. 

  1. Classify

Organize and label data to protect sensitive information. Implementing a straightforward classification system ensures that critical data is appropriately secured and handled according to its sensitivity. 

    Protecting the Crown Jewels: Beyond PII 

    While much attention is given to Personally Identifiable Information (PII) like social security numbers and credit card data, it’s crucial to also protect an organization’s “crown jewels”—its intellectual property (IP) and trade secrets.

    Sensitive data includes any information that, if compromised, could significantly impact the organization, such as:

    1. Intellectual Property (IP): Patents, proprietary algorithms, product designs, source code

    2. Trade Secrets: Business strategies, marketing plans, research and development data 

    3. Financial Information: Earnings reports, budgets, forecasts

    4. Human Resources Data: Employee records, salary details, performance evaluations

    5. Client and Partner Information: Contracts, negotiated terms, confidential communications

    By identifying and classifying this sensitive data, organizations can implement appropriate protections to prevent unauthorized access or disclosure.

    Data Access and Permissions: Setting the Right Guardrails 

    Effective data governance requires not only classifying data but also controlling access to it. Properly managing permissions is crucial to prevent unintended exposure of sensitive information. 

    Potential risks of unrestricted access include: 

    • Data Breaches: Unauthorized access can lead to data leaks, damaging reputation and incurring regulatory penalties. 
    • Inaccurate AI Outcomes: AI tools may generate outputs based on outdated or irrelevant data, leading to poor decision-making. 
    • Compliance Risks: Violations of data protection laws like GDPR or CCPA can result from mishandled data. 

    Best Practices for Access Management 

    • Implement the Principle of Least Privilege: Users should have access only to the data necessary for their role. 
    • Conduct Regular Audits: Periodically review access permissions and adjust as needed. 
    • Monitor and Set Alerts: Use systems to detect and alert on unauthorized access attempts. 
    • Educate Employees: Provide training on the importance of data security and proper handling of sensitive information. 

    Understanding how AI tools handle security, compliance, and privacy is also essential. Ensure that any AI solutions in use honor data classification labels and comply with organizational policies. 

      Continuous Governance: Making Data Management Routine 

      AI readiness is not a one-time project but an ongoing commitment. Data governance practices must be embedded into daily operations to sustain AI benefits and mitigate risks. 

      Steps for Continuous Governance 

      • Establish Routine Practices: Make information management a regular part of business processes. 
      • Implement Continuous Monitoring: Use tools that offer real-time insights into data usage and access patterns. 
      • Enforce Data Retention Policies: Regularly dispose of data that is no longer needed, in compliance with legal and business requirements. 
      • Update Training and Policies: Keep employees informed about the latest data governance practices and technologies. 
      • Collaborate Across Departments: Involve stakeholders from IT, legal, compliance, and business units to ensure a comprehensive approach. 

      By making data governance a routine aspect of operations, organizations can maintain AI readiness and adapt to evolving technological and regulatory landscapes. 

      The Expanding Role of Records and Information Management Professionals 

      As AI becomes integral to business operations, records and information management (RIM) professionals have a pivotal role to play. Their expertise in data curation, compliance, and risk management is invaluable for AI initiatives. 

      Contributions of RIM professionals include: 

      • Data Curation: Ensuring data is accurate, complete, and relevant. 
      • Compliance Assurance: Navigating complex regulatory landscapes and maintaining records in line with legal requirements. 
      • Risk Management: Identifying and mitigating risks associated with data handling and AI deployment. 
      • Documentation: Providing thorough documentation of data sources, processing steps, and AI model development. 

      Empowering RIM professionals to take an active role in AI projects can enhance data quality, ensure compliance, and drive better outcomes. 

        Starting Now: Drawing a Line in the Sand 

        One of the key messages is the importance of taking immediate action. It’s crucial to begin improving data management practices now to prepare for AI deployment. 

        Avoiding Analysis Paralysis 

        It’s easy to feel overwhelmed by the magnitude of data cleanup and governance tasks. However, prioritizing progress over perfection can make a significant difference. 

        Recommendations include: 

        • Begin with Small Steps: Implement basic data hygiene practices that can be quickly adopted. 
        • Focus on High-Risk Areas: Identify and address the most sensitive data first to mitigate the most significant risks. 
        • Leverage Technology: Utilize tools that automate and streamline data governance processes. 
        • Engage Stakeholders: Communicate the importance of data readiness and involve teams across the organization. 

        By taking proactive steps now, organizations can slow data growth, reduce risks, and position themselves to capitalize on AI’s potential. 

          Key Takeaways 

          • AI Reveals Hidden Data Risks: AI tools can inadvertently expose sensitive information if data governance is inadequate. 
          • Data Stewardship is Critical: Proactive data management is essential to harness AI’s potential safely. 
          • Protect Intellectual Property: Safeguarding IP and trade secrets is vital to maintaining a competitive advantage. 
          • Implement the Three Cs: Comprehend, Combine, and Classify your data to effectively prepare for AI deployment. 
          • Manage Access Diligently: Set appropriate permissions and continuously monitor data access. 
          • Make Governance Routine: Embed data governance practices into daily operations for sustained AI readiness. 
          • Elevate RIM Professionals: Leverage the expertise of records and information management staff in AI initiatives. 
          • Act Now: Begin taking steps immediately to mitigate risks and realize AI benefits.

          Conclusion 

          The transformative power of AI offers immense opportunities for organizations willing to invest in data readiness. By embracing proactive data governance, protecting critical assets, and fostering collaboration across departments, businesses can unlock AI’s full potential while minimizing risks. 

          The quicker organizations start taking action, the more benefits they’ll gain down the road. Preparing data for AI is not just about technology; it’s about people, processes, and a commitment to continuous improvement. 

          At Gimmal and Innovative Driven, we are dedicated to helping organizations navigate this journey. Whether you’re just beginning or looking to enhance your existing data governance framework, our teams are here to support you. 

          Contact Us

          Email: info@gimmal.com

          Phone: (877) 944-6625

          Website: www.gimmal.com

          Disclaimer: The contract details and durations are accurate as of the time of writing. Please verify all information with your procurement office or Gimmal representative.

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