As AI continues to transform nearly every industry, from finance and healthcare to retail and manufacturing, organizations are focused on gathering more and more data to train and refine their AI models.
But in this rush to gather as much data as possible, it’s crucial to focus not just on the quantity of data but also on the quality. As a recent Enterprise Strategy Group research report explained, “AI doesn’t know good data from bad. It just knows data, and whatever data is being used to train a model will ultimately become the model.” Data is the foundation on which your AI is built. The more accurate and structured the data, the more trustworthy your AI.
Getting good data isn’t easy, though. In fact, 31 percent of IT decision makers cited limited availability of high-quality data as the #1 obstacle to implementing AI within organizations, according to the research—placing it above data privacy, budget limitations, legal concerns, and other issues. As more organizations drive innovation and growth through AI, whether they are building their own models or integrating third-party ones, it’s essential to understand what factors make data more (or less) valuable.
The hallmarks of high-quality data
One place to look for best practices is in IT, an industry in which new, patented AI solutions are being used to dramatically reduce the number of help desk tickets and increase productivity. Given that a software bug or patch that may cause conflicts with existing apps on the system can cost a company millions of dollars, IT data experts use various proven strategies to improve the quality of the data that fuels their AI.
First, it’s essential to ensure that your data is well-structured and well-labeled. While this might seem like an unnecessary step for an AI that will simply ingest, process, and organize all the data, removing as much unnecessary data (aka “noise”) as possible at the front end of the process can dramatically affect your results. As one report noted, failing to structure and label your data appropriately “can lead to quality issues—not to mention duplicative or useless data that increases the cost of training without providing any real value—that affect the overall trustworthiness of the data.”
Of course, it’s important to focus on both breadth and depth. Domain-specific data is extremely valuable; going deep allows you to extract the highest value from AI. In one instance, an organization used data intelligence gathered from thousands of endpoint devices throughout the enterprise. When the IT team received two help desk tickets about slowdowns, they used AI to quickly discover that these two tickets actually represented 800 users who were also experiencing the same issue—but simply hadn’t reported it yet. Your organization is sitting on a goldmine of data, and your AI is mining for gold. Anomaly detection using AI can stave off major IT issues that could affect the business or employee productivity.
Gathering more data from as wide a range of sources as possible will typically lead to greater accuracy, as long as the data is good. When you think about the breadth of your data sources, consider how your historical organizational data can help identify year-over-year trends, provide context, and build trust among your users.
Finally, focus on your people and their collective expertise. Despite all the advantages of AI, it’s still just lines of code. It doesn’t have the organizational context, domain expertise, and insight that your employees have. It doesn’t sit in department meetings (even if you feed it transcripts, it’s not going to capture everyone’s mood, tone, or body language). It doesn’t have lunch with the CEO or marketing team. AI is a tool, and there must be a “human in the loop” to train the model for greater accuracy, relevance, context, and explainability of the model’s outputs.
Poor-quality data: exploring the hidden costs
If you can’t trust your data, you can’t trust your AI. As one report explained, “Without good data to feed into the AI, trust can never be achieved. Without trust, full adoption can’t happen. And without full adoption, the overall goals can’t be achieved.” People already mistrust AI due to well-documented biases, hallucinations, and ethical considerations. Training an AI on inaccurate or poor-quality data increases the possibility of untrustworthy results, which can quickly lead to a loss of faith among users.
But the issues with poor-quality data go far beyond trust. More than a third of business stakeholders involved with AI infrastructure purchases cited legal and regulatory issues of generated content as a barrier to AI implementation. While the compliance landscape for AI is still evolving, the AI tools are changing even faster. Imagine the risk of an AI that provides false results to your customers or exposes your company to new security risks. Eliminating poor-quality data sources may help limit your organization’s liability.
Finally, there’s the cost issue. Implementing generative AI requires significant computational resources, potentially requiring considerable budget allocations. So every minute you’re training your AI on poor-quality data, you’re wasting time and money. Well-structured, high-quality data means you’re feeding the AI 5-star-level data!
Whether you’re already using AI in your organization, or you’re still planning on different ways to implement it, one thing is clear: focusing on the quality of your data now will likely pay significant dividends far into the future. Because in a world driven by AI, high-quality data is the currency that will set you apart—and set you up for success.
