Insight
The ability of AI to automate processes or deliver personalized customer experiences is transforming businesses across all industries. As a result, the pressure to launch AI initiatives continous to be at record highs. Yet one critical fact is still widely underestimated: AI is only as effective as the data it is built on.
In practice, data quality is often treated as a secondary concern, perceived as complex, time‑consuming, and difficult to sustain. Many organizations therefore move forward with AI projects before their data is truly ready. The outcome is predictable: delayed initiatives, unreliable results, wasted budgets, and growing frustration among stakeholders. Only by addressing data quality in a structured and sustainable way can organizations create a reliable foundation for AI.
As AI has become increasingly popular as this ‘all-powerful’ or ‘magic’ tool, expectations have become unrealistically high. This often leads to disappointment when AI projects, which begin with the idea of quick, transformative outcomes, end up delayed, failed, or delivering underwhelming performance. The hype has unfortunately led to numerous projects being launched without careful consideration, preparation, or understanding of the requirements and limitations. A massive factor in the failure or under-delivery of AI projects are data issues. ‘Big Data’ is a buzzword in the AI space, but what does big data give you when it is not ‘Good Data’?
This can be best explained at the example of the AI outcomes during the pandemic. Despite having the best data scientists, significant computing power, and large amounts of data, no AI model made a meaningful difference. For instance, many hospitals attempted to use AI to predict the severity of COVID-19 cases and manage resources like ventilators and ICU beds. However, the data from different hospitals was highly inconsistent – variations in patient records, testing protocols, and reporting standards made it nearly impossible to train accurate and reliable models. This inconsistency led to AI systems that produced unreliable predictions, ultimately failing to provide the needed support.
In an everyday business context, even if you understand the concept of ‘Garbage in, Garbage Out’ when it comes to AI, the next critical challenge is assessing the quality of your data. Is it truly 'garbage,' or is it 'good'? Identifying this requires effective data validation. Once identified, data cleaning processes and standardization efforts are necessary to ensure that the data is consistent and reliable before it can be used to train AI models effectively.
A common misconception is that data quality is solely an ‘IT issue’ rather than a business issue. In reality, data quality goes far beyond technical or formatting problems and requires a holistic approach involving both IT and business/domain experts. AI projects often fail when there is a disconnect between these two groups, as considering only one side of the data is not sufficient. It is crucial for you to ensure that your data is of high quality to achieve the desired outcomes from your AI projects. High-quality data is the foundation for any successful AI initiative, but how exactly does data impact AI?
To answer how data effects AI performance, let us dive deeper into how data affects AI performance to better understand how you can get your data ready for the next AI project. Starting with the basics: What is AI, and how does it work? Here is a quick breakdown of the main buzzwords that have been circulating:
Starting with Machine Learning (ML), which is by far the biggest subcategory of AI, it generally uses algorithms to learn from data. Deep Learning (DL) goes one level deeper and is a specific ML method that uses large brain-like models to learn patterns in data. The most popular DL method is Generative AI (GenAI), which learns patterns in data and based on these patterns, generates text, images, or videos by predicting, for example, the most likely next pixel or word. With each progression from ML to DL to GenAI, model sizes tend to increase, and their workings become more complex and less transparent, making high-quality data increasingly important for achieving reliable outcomes.
Now, let us have a brief overview of how AI projects operate. In a very simplified way, you select the data relevant to your needs, choose and configure an AI model, train the model on a large part of the selected data, and then test the model's performance with the unseen parts of the data called test data.
With this brief AI recap, let us look at some specific examples of how poor data can negatively affect an AI model:
We have now explored some specific ways AI is affected by data. Understanding and gaining an accurate overview of your data, along with identifying potential issues, is the vital first step. This understanding and transparency form the basis for evaluating where the biggest pain points are and how to efficiently address them. Based on key learnings from various industries and our experience with many clients, here are three key recommendations you should consider when getting your data AI-ready, which are often underestimated:
Data quality is not just an ‘IT job’; it requires input from domain experts who will have varying levels of data understanding. Consider the examples from the previous section: only an HR expert can accurately identify incorrect data in an HR context, only a logistics expert can recognize outliers in shipping data, and only a compliance expert can determine which data is truly relevant in fraud detection. Therefore, data must be understandable and accessible not only to ‘data experts’ but to everyone involved. Using tools like the Designer AI can enable domain experts without any technical background to directly analyse and detect issues in their data themselves using GenAI.
It is important to highlight that your goal should not be to achieve 100% perfect data but to find a realistic balance between cost and value. In a value-oriented approach, you need to evaluate the most critical data issues for your company and the effort required to solve them. Our decade of client experience has shown that many are initially overwhelmed by the seemingly massive data-cleansing task, thinking that perfect data is necessary. In reality, identifying and focusing on the areas currently most critical for your company's focus is sufficient to achieve effective outcomes. Impact first, coverage later. Start where data quality directly affects AI performance, business decisions, or operational efficiency, and expand systematically from there. For more advice on how to best approach such a project schedule a free call with one of our experts.
Continuous AI‑ and future‑ready data cannot be achieved through isolated clean‑up projects. As data volumes grow and business requirements evolve, data quality must become a permanent capability embedded into daily operations and strategic initiatives.
Data Quality Navigator builds on existing expert content and proven methodologies, enabling organizations to move quickly from initial, impact‑driven improvements short-term to sustainable data quality at low ongoing effort. Continuous monitoring and automated remediation workflows ensure that data quality is maintained over time, not just at project start. This allows organizations to scale from focused, high‑impact use cases to broader coverage in a controlled way, while remaining flexible enough to integrate new AI models, regulatory requirements, and business initiatives whenever needed.