Data for AI: What It Is, Why It Matters, and How It’s Used

How Data Became Central to Artificial Intelligence

"In god we trust. All others must bring data".

-W. Edwards Deming

The formal birth of artificial intelligence as an academic field is often traced to the 1956 Dartmouth Summer Research Project, where the term “artificial intelligence” was first coined. Early AI systems in the 1960s and 1970s, such as ELIZA and Shakey the Robot, relied on symbolic logic and handcrafted rules rather than data-driven learning. While promising, these systems struggled to scale, leading to periods of reduced funding known as “AI winters.”

In the traditional context, AI’s modern resurgence began in the late 1990s and accelerated dramatically in the 2010s. Advances in computing power, cloud infrastructure, and the availability of massive datasets enabled machine learning and deep learning approaches to outperform earlier rule-based systems. Defining and equally terrifying moments, such as IBM’s Deep Blue defeating a world chess champion, demonstrated AI’s growing capabilities. Yet, it was the combination of large-scale data and neural networks that truly unlocked today’s AI boom. In modern AI, data is no longer supplementary; it is foundational.

What Is Data for AI?

Data for AI refers to the information used to train, test, and evaluate machine learning models. This data can take many forms, broadly categorized into structured and unstructured data. Structured data is highly organized and typically stored in databases or spreadsheets, such as transaction records or sensor logs. Unstructured data, which includes text, images, video, and audio, is more complex and makes up the majority of real-world information.

Even after having gone through the arduous process of gathering all the necessary data, there is still not the time for rest. For AI systems to learn effectively, raw data needs to be transformed through annotation and enrichment. Data annotation involves labeling data with human-provided context, such as identifying objects in images, tagging sentiment in text, or marking anomalies in time-series data. These labels allow models to connect patterns with meaning, which makes annotation a critical step in supervised learning workflows.

Why Data Matters for AI

Not only is data input into AI systems, it literally determines their limits. AI models learn patterns from historical examples, meaning their performance is tightly tied to the quality, diversity, and relevance of the data they are trained on. More data does not automatically mean better results; poorly curated or biased datasets can degrade performance and lead to unreliable outcomes.

Research strongly supports this reality. A 2019 study examining the application of AI in radiology found that while many algorithms achieved impressive diagnostic accuracy on specific datasets, their performance often dropped significantly when applied to unrelated or external datasets—sometimes performing worse than human clinicians. This gap highlights a critical limitation of AI systems: models frequently fail to generalize beyond the conditions represented in their training data. As the authors note, AI is only as good as the data it learns from, making dataset relevance and representativeness essential in real-world deployments (Deyer & Doshi, 2019).

This challenge is compounded by the presence of bias in data. If training data reflects historical inequities or narrow populations, AI systems may reproduce or amplify those biases. In high-stakes domains such as healthcare, finance, and hiring, these effects can have serious ethical and legal consequences. Ensuring fairness, coverage, and transparency in datasets is therefore a core requirement for responsible AI development.

How Data Is Used in AI Systems

In machine learning, data is used to train algorithms to recognize patterns and make predictions. Models learn relationships between inputs and outputs by analyzing large volumes of labeled examples. For instance, recommendation systems rely on historical user behavior data to predict future preferences. Many of us have had the eerie moment when we got recommended exactly what we didn't even know we wanted.

Deep learning systems extend this approach by using multi-layer neural networks to learn complex representations from data. These models power applications such as image recognition, speech processing, and large language models, but they require vast amounts of high-quality, annotated data to perform reliably.

Natural language processing is another domain where data plays a defining role. Text from documents, customer interactions, and online content is used to train models that can summarize information, analyze sentiment, or engage in conversational interfaces. Again, the diversity and structure of language data directly affect how well these systems perform across different contexts and populations.

Across industries, data-driven AI is transforming operations. Healthcare organizations use patient records and medical imaging data to support diagnosis and treatment planning. Financial institutions rely on transaction data to detect fraud and manage risk. Retailers analyze customer behavior data to personalize experiences and optimize supply chains. In every case, data quality and relevance determine whether AI systems create value or introduce risk.

Preparing Data for AI: From Collection to Sensemaking

As we have mentioned, preparing data for AI is a complex, iterative process. It begins with data collection from sources such as sensors, platforms, simulations, or human input. Raw data is rarely ready for use and must be cleaned to address errors, inconsistencies, and missing values.

On the next step, annotation serves as the connecting bridge between raw data and training-ready datasets. This process is not merely a mechanical task. It is part of a broader sensemaking process. Drawing on cognitive research, sensemaking involves organizing individual data instances into meaningful structures that support hypothesis formation and validation. In AI development, practitioners analyze model inputs and outputs to understand behavior, identify failure cases, and refine datasets accordingly.

Unlike traditional data analysis, AI sensemaking is continuous. Models are frequently updated, exposed to new data, and deployed in changing environments. Each update can alter system behavior, requiring reevaluation of assumptions and additional data collection. Techniques such as data augmentation, synthetic data generation, and active data collection—where users or experts surface model failures—are increasingly used to support this process. Still, gathering diverse and representative data remains expensive and challenging, reinforcing the need for structured data strategies and expert oversight.

Data Access, Competition, and Innovation

Beyond model performance, access to data has become a key driver of competitive advantage in AI. Research on AI-driven innovation suggests that organizations with early or privileged access to large datasets may gain long-lasting advantages, not through formal intellectual property alone, but through superior learning from data. When AI systems exhibit increasing returns to scale, larger and more diverse datasets enable faster improvement and reinforce market leadership (Cockburn, Henderson, & Stern, 2018).

Data also functions as an upstream input that shapes downstream markets. Whether traded directly or monetized through data-based services, access to data influences decision-making across sectors. The structure of data markets—competitive or monopolistic—can affect innovation, competition, and outcomes in real-world goods and services (Martens, 2018). As a result, data governance and access regimes are increasingly central to AI policy discussions.

Ethics, Culture, and Global Perspectives on AI Data

Attitudes toward AI and data vary across cultures, shaped by differing philosophical views of humanity and technology. In some Eastern traditions, humans are viewed as part of a broader natural continuum, reducing the perceived divide between people and machines. In contrast, Western philosophical traditions often emphasize human uniqueness, contributing to anxiety around automation and dehumanization.

These cultural differences influence how societies approach data collection, AI regulation, and ethical boundaries. As AI systems are deployed globally, understanding these perspectives becomes essential for building responsible technologies. Coordinated international dialogue and collaboration are necessary to establish ethical guidelines that respect cultural diversity while ensuring accountability and fairness in AI systems (Zhou, 2018).

The Future of Data in AI

As AI continues to evolve, data practices will become more automated, adaptive, and privacy-aware. Advances in tooling are enabling partial automation of data collection, labeling, and evaluation, reducing manual effort while increasing scale. Synthetic data is emerging as a complementary approach when real-world data is scarce or sensitive, particularly in domains such as autonomous systems and healthcare.

At the same time, privacy-preserving techniques such as federated learning are gaining traction, allowing models to be trained across decentralized datasets without exposing raw data. These approaches reflect a broader shift toward responsible data use, where performance, privacy, and ethics must be balanced.

Conclusion

Data is the backbone of artificial intelligence. Without high-quality, relevant, and well-governed data, even the most advanced AI models fail to deliver reliable results. Research consistently shows that data relevance, diversity, and access shape AI performance, innovation, and fairness far more than algorithms alone.

As AI systems become more deeply integrated into real-world decision-making, organizations must treat data as a strategic asset, not just technical input. Doing so requires expertise in data collection, annotation, evaluation, and ongoing sensemaking.

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References

• Deyer, T., & Doshi, A. (2019). Application of artificial intelligence to radiology. Annals of Translational Medicine, 7(11), 230.
• Cockburn, I., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation. NBER Working Paper No. 24449.
• Martens, B. (2018). The importance of data access regimes for artificial intelligence and machine learning.
• Pirolli, P., & Card, S. (2005). The sensemaking process and leverage points for analyst technology. Proceedings of the International Conference on Intelligence Analysis.
• Zhou, A. (2018). The Intersection of Ethics and AI. ACM.
• Google Cloud. What is artificial intelligence and how is it used?
• European Parliament. What is artificial intelligence and how is it used?

Frequently Asked Questions (FAQ)

What is big data and why is it important for AI?

Big data refers to extremely large and complex datasets that are generated at high volume, velocity, and variety, which are often beyond the capacity of traditional data-processing tools. Big data is important for AI because modern machine learning models, especially deep learning systems, rely on large amounts of diverse data to identify patterns, learn representations, and improve accuracy. Access to big data allows AI systems to generalize better across real-world scenarios, provided the data is well-curated and representative.

What is the 30% rule in AI?

The “30% rule” in AI is an industry heuristic suggesting that a significant portion—often around 30% or more—of an AI project’s total effort, time, or cost is spent on data preparation tasks such as collection, cleaning, annotation, and validation. While not a formal scientific rule, it reflects a widely observed reality in AI development: preparing high-quality data is one of the most resource-intensive and critical parts of building reliable AI systems.

What kind of data is used by AI?

AI systems use a wide range of data types depending on the application. This includes structured data such as tables and databases, unstructured data such as text, images, audio, and video, and semi-structured data like logs or sensor outputs. In many cases, AI models also rely on labeled or annotated data, where human experts provide context or ground truth. Increasingly, synthetic and augmented data are used to supplement real-world datasets when data is scarce or sensitive.

What is data annotation, and why is it important for AI?

Data annotation is the process of labeling or tagging data so that AI models can recognize and learn from it. For example, images might be tagged with descriptions, or text might be labeled with sentiment. Proper annotation helps AI models make accurate predictions and decisions.