Is Your Data AI-Ready?
The path to AI-driven innovation is paved with data readiness. These three indicators provide a quick data readiness assessment.
- By John Peluso
- July 31, 2024
The promise of artificial intelligence (AI) to transform business operations has generated great enthusiasm from organizations across industries. Yet, despite the widespread adoption of AI—with 74% of organizations now leveraging it in some capacity—a staggering 95% have encountered roadblocks during implementation.
Further, although 80% of organizations believe their data is ready for AI, over half have grappled with internal data quality and categorization issues. This disparity between perception and reality signals a critical need for a more rigorous data readiness assessment before diving into AI projects.
How can your organization determine if your data is truly AI-ready? Here are three key indicators for a quick data readiness assessment.
Is Your Data Centralized?
One of the foundational requirements for successful AI implementation is the availability of high-quality, centralized data. However, the reality for many organizations paints a more fragmented picture. According to a G2 survey, 70% of organizations adopt hybrid cloud storage practices. Among those, nearly half prefer dealing with multiple cloud solutions to meet their needs.
This decentralized data landscape poses a significant challenge for AI systems, which require seamless access to all relevant information to generate accurate insights. When data is scattered across multiple repositories, AI models are restricted to only the data they can directly access, limiting their ability to uncover comprehensive, data-driven discoveries.
When implementing Microsoft Copilot, for example, it is a best practice to consolidate data into Microsoft 365 as the centralized, cloud-based repository. This enhances accessibility for Copilot and improves data management, security, and control. By breaking down data silos and unifying your information, Copilot is empowered to tap into the full breadth of your organization's knowledge, unlocking the true potential of this technology.
For some AI-driven projects, however, where centralizing data is not possible, ensuring your data is AI-ready means being able to harvest data from multiple sources to prepare it for AI. This may involve consolidating messy, incomplete, or inconsistent data through a workflow of collection, cleaning, transformation, splitting, and formatting to produce high-quality data that is ready to feed into an AI algorithm for training.
Do You Have Context for Your Data?
Without proper data contextualization, AI systems may make incorrect assumptions or draw erroneous conclusions, undermining the reliability and value of the insights they generate. To avoid such pitfalls, focus on categorizing and classifying your data with the necessary metadata, such as timestamps, location information, document classification, and other relevant contextual details. This will enable your AI to properly understand the context of the data and generate meaningful, actionable insights.
Additionally, integrating complementary data can significantly enhance the information’s value, depth, and usefulness for your AI systems to analyze. For example, appending customer demographic data (such as age, gender, location, and income level) to your sales records can unlock new dimensions for your AI to explore. Instead of just looking at raw sales numbers, your AI could start uncovering deeper, more nuanced patterns and trends.
Is Your Data Relevant and Timely?
The third key indicator of AI readiness is the relevance and timeliness of your data. The report referenced earlier revealed that 50% of organizational data is over five years old, suggesting a significant proportion of redundant, obsolete, or trivial (ROT) content within many organizations' data ecosystems. This isn’t helped by the fact that only 23% of organizations have systems to help them get real-time access to their ERP data for making smart decisions.
Although older data may be necessary for compliance or historical purposes, it may not be relevant or useful for your AI initiatives. Outdated information can burden your storage systems and compromise the validity of the AI-generated insights. Imagine an AI system analyzing a decade-old market report to inform critical business decisions—the insights would likely be outdated and misleading.
That’s why establishing and implementing robust retention and archiving policies as part of your information life cycle management is critical. In fact, organizations with mature information management strategies are 1.5 times more likely to realize the benefits of AI than those with less-mature approaches.
By purging ROT content and maintaining only the most current and meaningful information, you'll streamline your data ecosystem and empower your AI to deliver the most accurate and impactful insights.
A Final Word
Ultimately, the path to AI-driven innovation is paved with data readiness. By taking a proactive and holistic approach to information management, organizations can ensure their data is AI-ready and future-proofed to adapt to the ever-evolving data landscape. It's time to move beyond the perception of data readiness and embrace the reality of data maturity—the key to unlocking the true transformative power of artificial intelligence.
About the Author
John Peluso is AvePoint’s chief technology officer. In this role, he aligns the company’s technology and product road maps to grow AvePoint’s market share and accelerate the ideation, development, and launch of innovative software products tailored to anticipate customer needs. Prior to this role, John held multiple leadership roles over his 13-year tenure at AvePoint, including chief product officer, SVP of product strategy, director of education, and chief technology officer, public sector.