TL;DR
- AI isn’t a magic wand. It enhances process, but doesn’t solve everything overnight
- AI success depends on high-quality data, proper infrastructure, and continuous iteration.
It’s easy to see why so many believe AI can solve almost anything. It feels like magic. Type a question, and within seconds you get a fluent answer, a working piece of code, or even a lifelike image. There’s no user-run training, no setup, no waiting. You write a prompt and get instant results.
But this brings about the unrealistic expectation that AI will simply “work” like flipping a switch.
The reality is less magical. AI can only be as good as the information it learns from. Behind every impressive model or clever chatbot lies data. The quality of that data determines whether AI dazzles or disappoints.
Expectation vs. reality in AI
AI adoption has surged across industries, with marketing, sales, and product development teams leading the way. But more and more organizations are discovering that even the smartest system can’t overcome poor, messy, or biased inputs.
When AI investments are built on poor quality data, it leads to unreliable AI outputs, financial waste, and increased risk. 90% of data professionals agree that company leaders are not paying adequate attention to bad or inaccurate data.
The importance of data quality for AI
AI doesn’t “understand” truth; it predicts patterns. If the AI uses poor-quality data i.e., incomplete, inconsistent, or biased data, it will give you biased, wrong, and misleading answers. It’s the old adage of “garbage in → garbage out”.
Check out the video explainer on Garbage in --> Garbage out.
Since the arrival of ChatGPT, we train our own models less and rely on pretrained models like ChatGPT to do the job. Yet, if we feed it poor data, it will return poor answers.
For example, here at KNIME we built an “Ask Me Anything” AI agent to handle any type of internal question about support tickets, the KNIME community, employees, and more. Instead of us having to manually search through documents, databases, and internal tools, the agent can answer questions directly by retrieving the relevant information and generating useful responses.
However, if the data the agent accesses was fragmented, messy, or obsolete, the agent could only produce unsatisfactory, misleading, or even wrong answers.
Poor data comes in many forms, but it all leads to the same outcome: unreliable (and costly) results. It can be:
- Incomplete — missing key context or filled with irrelevant information
- Outdated — relying on obsolete or old data that no longer reflects reality
- Noisy — cluttered with duplicates, inconsistencies, or outright errors that distort patterns,
- Biased — shaped by stereotypes or skewed sources that lead to unfair or misleading conclusions
Even the most advanced large language model (LLM) has a hard time making sense of poor data. Quality data, on the other hand, allows AI to perform accurately and effectively. consistently.
Unpacking three Vs for data quality
To understand what makes data truly reliable for AI, it helps to look at three Vs for data quality — veracity, validity, and volume. There are more characteristics for data quality, which you can check out here, but these three are particularly relevant. They determine whether your data can be trusted to produce accurate, meaningful results.
| The Vs | What it is | Why it’s important | Example |
| Veracity | This refers to the trustworthiness and accuracy of the data. | Inaccurate, missing, or duplicated data can lead to flawed decisions | If you have negative social media posts about your company’s smart fridges that are all from an area experiencing a power outage, the negativity is not due to the fridges but the weather. In this case the data is inaccurate and misrepresents the reality. |
| Validity | The data is relevant and fit for the purpose | Invalid data can cause systems to fail or produce incorrect results | If you want to analyze the internal temperature of your company’s smart fridges you’d be interested in temperature readings, number of times the door opened, etc. But if your dataset also contains metrics like purchase price, this is less relevant and might distort your analysis. |
| Volume | The amount or scale of data that is used. | You need a large enough sample of data for the AI to learn from and predict patterns. | Imagine you want to train an AI model to automatically restock those smart fridges when supplies run low. If the model’s trained on data from just a handful of households it might learn the wrong habits, e.g. over-ordering milk because a few users drink large quantities. |
A cautionary tale: When poor data quality makes AI-driven insights unreliable
“AI is all about data,” said AI thought-leader, Kathleen Walch, at European Health Conference HIMSS25, where she discussed AI in healthcare.
Epic’s AI Sepsis Prediction Model was developed to help predict sepsis, but in practice the model often failed. In effect the AI solution couldn’t reliably predict sepsis because the underlying patient data lacked completeness and consistency across the hospital network. There were two main data quality problems:
- The AI was trained on patient data that didn’t reflect real-time conditions.
- Inconsistencies in the data across hospitals meant that prediction accuracy varied widely.
- False alarms were common due to missing or incomplete patient data.
No matter how advanced the model, if the data foundation is flawed, performance suffers.
Data quality is where the real magic happens
AI might feel magical, but its performance depends on something far less mysterious: data quality. The more we rely on AI to make decisions, the more critical it becomes to invest in the fundamentals of collecting, cleaning, and maintaining reliable data. The real magic happens when the data is clean, consistent, and representative.