A must have New Year’s resolution: Get your data AI-ready

In the excitement surrounding AI, data readiness is an oft-overlooked but crucial step. The viability, scalability, and success of AI depend on accessible, high-quality data. Organizations can architect the right data foundation today to adopt and scale AI capabilities tomorrow.

Here are the four steps of the journey to get data AI-ready.

1. Conduct a comprehensive inventory of data
   

   Given the size and sprawl of organizational data, taking inventory of it may sound daunting. However, there is no way around this step. Data in all formats—structured, unstructured, and semi-structured—and across systems, sources, and silos, must be inventoried. With an organized inventory, data can then be cataloged into one of three classifications: retain, destroy, or digitize. For data that is retained and digitized, metadata can be added.

   To help organizations achieve a comprehensive, cataloged inventory, the integrated set of tools and capabilities in Iron Mountain InSight Digital Experience Platform (DXP) provides a single unified solution. The platform provides intelligent document processing (IDP), deploys governance and security, including retention and privacy, and offers audit-tracking data management capabilities.

2. Validate and verify your data’s accuracy
   

   After data is centralized and cataloged, its accuracy must be validated and verified. AI model output is only as accurate as the data that goes into the model, making data quality essential. InSight IDP can streamline this process by comparing data against sources, such as rules, reference documents, and databases. Any discrepancies between the two are highlighted for manual review and resolution, helping to advance data hygiene, an imperative for AI success.

3. Deploy data governance and security
   

   Once data quality issues are addressed, organizations can implement governance and security tactics. These strategies help protect data from breaches and ensure regulatory compliance with the General Data Protection Regulation (GDPR) and others.

   A significant element of compliance is data retention, which can be automated by InSight DXP to ensure that legal, operational, and compliance requirements are met. As industry, geographic, and legislative regulations continue to evolve, an organized, accessible data foundation streamlines both ongoing compliance and the implementation of data security access controls.

4. Acquire data properly
   

   Acquiring and using data responsibly requires alignment with ethical, legal, privacy, and intellectual property rights. In addition, proper data acquisition extends beyond transparency about data sources to include informed consent, data accuracy, and legal compliance. This is an increasingly important principle since data used in AI models needs to eliminate bias and mitigate potential harm.

The payoff is real

While data readiness challenges are steep, the payoff is high. For one Iron Mountain customer that operates in over 70 countries, a complex divestiture mandated the management of hundreds of diverse, country-specific regulatory requirements for data retention. To navigate these complexities and achieve audit readiness, the organization pursued the deployment of an archive and retention process.

The audit-readiness project leveraged Iron Mountain Information Governance Advisory Services and InSight^® to know what data it had and what format it was in. Using InSight, their data was classified, tagged with metadata, and organized for management, accessibility, and usability across the organization.

As a result, the organization built the right data foundation and attained both audit-ready and AI-ready data while simultaneously delivering 70X more efficiency to the business. Looking ahead, the organization is now prepared for a stronger, smarter, more scalable future.

Getting data AI-ready

To realize the full potential of AI, organizations must establish the right data foundation. Properly preparing data for AI use can give organizations a competitive advantage via timely AI deployments and catalyze AI use cases at scale. Using the four steps outlined above as a guide, future-forward organizations can unlock AI’s potential. Most importantly, with the increasingly intense pace of change and AI adoption, the time to get data AI-ready is right now.

Learn more about InSight DXP and how it supports making your data AI-ready.