For most organizations, the effective use of AI is essential for future viability and, in turn, requires large amounts of accurate and accessible data. Across industries, 78% of executives rank scaling AI and machine learning (ML) use cases to create business value as their top priority over the next three years.
Yet, organizations need help in scaling AI and moving applications from pilot to production. The main reason is that it is difficult and time-consuming to consolidate, process, label, clean, and protect the information at scale to train AI models. Organizational data is diverse, massive in size, and exists in multiple formats (paper, images, audio, video, emails, and other types of unstructured data, as well as structured data) sprawled across locations and silos. CIOs must solve these challenges to achieve organizational AI readiness and unlock innovation.
Tangible AI benefits
Solving these challenges is essential to realize the benefits AI produces in real world use cases. The results from three organizations make it easy to see AI’s transformative value:
- A European government agency responsible for distributing pensions uses AI to improve time-to-payout from two-plus years to weeks.
- An aircraft engine provider uses AI to manage thousands of technical documents required for engine certification, reducing administration time from 3-6 months to a few weeks.
- A media agency leverages generative AI to extract copyright information and ownership details across 20 million images and thousands of films and TV episodes, shrinking decision-making from months to hours.
The examples above demonstrate how expanding AI applications and unstructured data help create transformational outcomes. By all accounts, the influx of unstructured data used by organizations in AI models has fueled new opportunities and challenges in preparing data—and organizations—for AI.
Every AI journey begins with the right data foundation—arguably the most challenging step. Among CIOs surveyed, 72% say that problems with data are the most likely factor jeopardizing the achievement of their AI goals. Let’s look at four principals needed to get your information AI-ready.
Making your data AI-ready
Inventory and catalog data
Organizations store massive amounts of physical and digital data that may or may not be useful for AI. One estimate reveals that 64% of organizations manage at least one petabyte of data, and 41% of organizations surpass that with at least 500 petabytes of data. But this data is in disparate systems, silos, and various formats, hindering organizations from realizing its full potential. Many organizations do not have their data cataloged and lack understanding of which data is relevant to their AI strategy and ready for use.
Conduct a comprehensive inventory of your organization’s data across systems, sources, and formats—including structured, unstructured, digital and physical. With a well organized inventory, you can make the right decisions about what information should be retained, defensibly destroyed, or digitized. Accomplishing this requires an integrated set of tools and capabilities which Iron Mountain’s InSightⓇ Digital Experience Platform (DXP) provides in a single unified solution. InSight DXP provides capabilities to assess and address data quality with intelligent document processing (IDP), implementing governance and security with integrated features to address retention and privacy obligations, and robust content management with end-to-end audit tracking to ensure data is handled responsibly.
Assess and address data quality
Once your data is centralized and cataloged, assessing and addressing data quality standards is crucial. That’s because AI model output is only as accurate as the data inputs. To confirm data accuracy, IDP workflows can extract information to undergo validation and verification. IDP systems compare the extracted data against predefined rules, databases, or reference documents to validate its correctness. Any discrepancies or errors are flagged for manual review and resolution. This process maintains good data hygiene and is crucial for long-term AI success and data resilience.
Implement governance and security
Addressing data quality can provide guardrails to then focus on governance and security strategies that ensure data is used appropriately, protected against breaches and compliant with regulations such as the General Data Protection Regulation (GDPR). Implementing a data retention schedule defines an organization’s legal, operational, and compliance requirements. It helps guide employees on how long to keep records, regardless of format, for legal and operational purposes and when it’s time to review or dispose of them. Industry and legislative regulations are constantly changing, with many enacting stricter rules on how businesses manage their information. Organized, accessible data makes compliance easier to achieve and supports ongoing regulatory, legal, and privacy obligations alongside data security access controls.
Source data responsibly
Sourcing data responsibly means acquiring and using data in a way that is ethical, legal, and respectful of privacy and intellectual property rights. This involves complying with relevant laws, obtaining informed consent, ensuring data privacy and security, maintaining data accuracy, and being transparent about data sources and usage. It also requires respecting intellectual property, considering ethical implications to avoid harm and bias, and being accountable for addressing any issues that may arise. By adhering to these principles, organizations can ensure responsible and ethical data practices.
You are one step closer to AI-readiness
Data is an organizational asset. By making your information accessible and usable, you are preparing to unleash the full potential of your data for use in AI models. While the journey to AI at scale is not simple, it is worthwhile and provides a critical competency. Iron Mountain is committed to helping organizations in their data-readiness and AI journeys in pursuit of a stronger, better future for all.
Learn more about InSight DXP and how it supports making your data AI-ready.
