In all the excitement around AI, it is crucial to remember that data and AI are intrinsically linked. AI consumes data to operate effectively, and data requires AI to unlock its full potential. Without a solid connection to enterprise data, even advanced tools, such as ChatGPT, Bard or Copilot, will fall short of transforming your business in a meaningful way.
This symbiotic relationship between AI and data is not new. What has changed is the speed and scale of AI adoption along with its unprecedented ability to interpret and generate unstructured data, such as plain text, audio, images and videos.
This highlights a dual challenge for organizations: mitigating the risks associated with the rapid and widespread adoption of AI, while simultaneously implementing robust strategies to manage both structured and unstructured data effectively.
Lower barriers to entry demand stronger governance
AI now speaks our language, and extremely powerful algorithms are widely accessible to anyone. This has significantly lowered the barriers to experimenting with AI, conducting trials and proving concepts. However, what has not changed is the complexity of scaling these tests into production-grade systems, which still requires substantial effort and investment. Transitioning from a proof of concept to a minimum viable product requires a focus on privacy, security, fairness, regulatory compliance and, very crucially, the accuracy of the data that is used — all supported by strong governance.
Without proper governance, you risk employees leaking sensitive information by prompting a public AI model, or confidential data being shared with an unauthorized employee using your internal large language model. This can happen due to poor segregation of underlying unstructured documents. Without adequate governance, you risk being unable to distinguish between accurate sources of information and flawed data that could undermine your AI model.
AI holds tremendous potential for all companies, but as advanced models become a commodity, the battleground is shifting from algorithmic supremacy to data excellence. Data excellence will be the key factor in determining which organizations win the AI race in the long run.
Bad data quality propagates
The impact assessment of data quality on AI performance and safety can be technically complex, but it boils down to a simple principle: “garbage in, garbage out.”
If the data used to train your LLMs (or other models) is filled with errors, includes sensitive or confidential information, or is factually incorrect, the output generated will inherit those flaws.
Besides, a new challenge has emerged with the ability of AI to process and generate unstructured data. The output of one AI model can now feed into another AI model, creating a chain reaction where bad data quality propagates and amplifies at every iteration, producing misleading and potentially harmful results.
Fixing data quality in three steps
So, data quality has never been hotter. But how do you fix it? Fundamentally, there are three core areas to focus on:
1. High-value data first
Businesses are flooded with data of all kinds. Trying to improve the quality of it all at once is an overwhelming and often futile task. Instead, the key is to identify where improving data quality will bring the most immediate value. The businesses that succeed with AI are those that focus on specific, high-impact use cases that drive ROI. When addressing data-quality remediation, start with high-value data required to support these use cases.
Of course, identifying high-value data implies having an overview of the available data. Countless opportunities are missed simply because business stakeholders are unaware of the data they have access to.
The data governance foundations you will build to support high-value use cases can then scale across the organization to support more and more sources and applications.
2. Scaling step-by-step
This value-driven approach also means you build incrementally as you grow. With each step, review your data, identify the issues and fix them before moving forward. This not only ensures that the data meets your newly defined quality standards but also helps you stay compliant with regulatory requirements.
Different regions are introducing regulations at varying speeds, creating a legal and governance challenge for businesses trying to navigate what is necessary for their operations and what is not. By establishing a governance framework as you move from one use case to the next, you can more easily identify where compliance is necessary and where it may not apply.
3. Learn, fix, learn again
There will be missteps; the generative AI explosion has not yet brought a corresponding boom in experienced talent. Everyone is learning. How you incorporate these lessons into your approach will define your long-term success and determine whether you can keep pace with the ongoing AI advancements.
A crucial part of the learning process is continuously assessing the quality of AI inputs and outputs. Incorrect outputs reported by users may signal a need for data quality remediation. Repeated instances of unauthorized data being submitted to the AI might indicate poor data classification. Use these constant feedback loops to refine and improve your system as you go.
Transform one step at a time
All in all, the task of successfully embedding AI into your organization cannot be underestimated. While these tools hold immense potential, leveraging them effectively requires a mature governance approach. All businesses have access to vast amounts of data, but the truly successful ones will focus on identifying the most valuable use cases, improving data quality and building the necessary frameworks and guardrails, one step at a time.
AI is changing the world. In addition to data quality, you must consider what it means for your network and security and how you will use LLMs. Take a look at our guide on all those areas to learn more.
Jérémy is a former theoretical physicist who traded black holes and string theory for the equally fascinating field of AI, spending the past ten years supporting organizations across diverse industries to solve complex business challenges with cutting-edge technology. With a passion for innovation, he helps businesses achieve their full AI potential with strategic roadmaps, tailored solutions, targeted coaching, awareness sessions and much more.