AI Data Collection: Key Concepts & Best Practices

AI systems require large amounts of structured data to function. Most AI systems undergo a training or fine-tuning phase, during which the basic AI algorithm is adapted to the task. Generative AI applications typically do not require training for most use cases, but instead need data for context when making decisions. Real-time post-deployment data may also be necessary to make predictions or generate output. 

This article explores the fundamental concepts underlying AI data collection and concludes with six recommendations for enhancing your AI data collection processes.

Summary of key AI data collection concepts

Understanding AI data collection

AI data collection is the process of gathering data to build AI use cases within an organization. The use cases can involve projects based on statistical machine learning, deep learning, or even LLMs. While use cases involving statistical machine learning and deep learning require data for both training and evaluation, those based on LLMs predominantly only need data for inference. 

At a high level, AI data collection involves collecting large volumes of data from various sources, including websites, APIs, sensors, social media, and user interactions on the organization’s website. The collected data can be structured or unstructured and of varying quality. Effective AI data collection requires that data quality management and governance processes be integrated with the data collection process. 

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Data sources

How the data is sourced impacts the design of the AI pipeline. Below is an overview of the key types of sources typically involved in AI data collection. 

Web-scraping

Web-scraped data is typically unstructured and may still contain HTML elements that were accidentally left in. Increasingly, websites are blocking automated scrapers through services like Cloudflare.

API queries

Many data providers offer a REST API that enables users to make requests to an HTTPS endpoint and receive substantial amounts of data, often in JSON format. Such API calls may be rate-limited depending on the agreement with the provider. 

Monolithic files

Some data providers may make a large ZIP file available for download. This can be extracted into a local subdirectory for further usage. 

Database queries

Many companies have their internal data stored in databases. These are queried through SQL or some visual equivalent. LLMs enable natural language querying of such databases. The resulting content is highly structured.

User-generated content

Users utilizing the AI system are also generating valuable data. Such data is available in real-time and directly relevant to the system’s performance and application. User privacy is a concern in this context.

Sensor data

A vast amount of sensor data is generated constantly on a global scale. Factories, processing plants, domestic and business properties, and any organizations using sensors are continually creating large amounts of non-textual and non-media data. 

Data synthesis

While data from real sources is the best reflection of actual problems, it is often expensive to collect and comes with privacy risks. Synthesizing data required for training and evaluation can reduce these problems. It is also a valuable resource when evaluating LLMs for specific use cases. Data synthesis can be done using rule-based methods or LLMs. 

Given the vast number of different sources and data types, developing custom data ingestion systems can be a time-consuming and risky process. Funelling a variety of big data into a single AI system and ensuring it is compatible and in the correct format requires careful preparation and automation. 

Integrating, synchronizing, standardizing, and preprocessing diverse and multiple streams is a significant part of developing an AI. For example, an AI that receives line-by-line data from live user interactions, a historical relational database from a cloud provider, an internal NoSQL database that is live-updating, and combines this with audio data from a real-time stream. 

Nexla provides several pre-built connectors that enable the automatic integration of data from multiple sources, such as various AWS databases. Specifically, over 500 different data sources can be integrated through the Nexla connector system. Thus, multiple incompatible varieties and large quantities can be brought together with minimal development time, providing rapid data delivery and processing speeds.

Structured and unstructured data

Data required for training and inferring from AI can take several forms. On a high level, they can be structured or unstructured. Unstructured data can take various forms, including text, audio, images, and even binary formats. Its size can vary drastically across each form. Structured data, on the other hand, comes from relational or NoSQL databases. While traditional machine learning models primarily relied on structured data, the advent of LLMs has brought about drastic changes. Large language models and vision language models can interpret unstructured data and make informed decisions based on it. 

To make unstructured data AI-ready, it is necessary to add comprehensive metadata and lineage information. Generative AI architectural patterns, such as multi-modal RAGs and agentic systems, can leverage metadata information to make more informed decisions.

For example, consider a global bank trying to automate compliance monitoring through generative AI. The solution utilizes unstructured data, including emails, PDF documents, meeting transcripts, audio files, and scanned documents. For LLMs to make sense of these documents, they require metadata information, including asset authors, timestamps, document type, sensitivity classification, language, format, and encoding. 

Modern data integration platforms provide support for active metadata. Such systems can auto-generate metadata information from data assets by utilizing API descriptions, data distribution, and any lineage information available through transformations within the platform ecosystem. 

The metadata view of data assets within Nexla, an enterprise-grade data integration platform, is given below. Nexla represents data assets as Nexsets—virtual data products with a built-in metadata intelligence layer. 

Data quality

Data quality is critical while building reliable generative AI applications. High-quality data ensures that LLMs have the proper context and metadata necessary for producing unbiased and meaningful outputs. The key data quality aspects from the perspective of generative AI applications are as follows. 

Consistency

The data needs to be internally consistent. For example, if a sensor is replaced halfway through data collection with one that has a different response, then the first part of the data may be inconsistent with the second half. Any changes in data collection regimes should be deliberate, part of the generalization process, rather than imposed upon it by external circumstances.

Completeness

The data must have complete information for LLMs to make decisions. For example, if you are dealing with documents, missing pages or metadata for specific pages can result in erroneous output from LLMs. Likewise, in the case of multimodal applications, it is essential to ensure that all data modalities are present for each sample. 

Accuracy

Inaccurate data as context affects the LLM output. Data must be factually accurate, without grammatical or spelling mistakes. It must be correctly tagged with labels and relevant data type information. 

Diversity and fairness

LLMs are trained on large amounts of data available all over the internet. They suffer from all the biases and diversity issues found in human-generated content on the internet. Hence, organizations need to use diverse and bias-free data while using LLMs for inference. This can help bypass the inherent biases present in LLMs due to the original data on which it was trained. 

Versioning

In the same way that an engineer may use version tracking to try out and collaborate on different versions of an AI model,  AI data also needs to be versioned and tracked. This helps improve explainability, solve bugs, and roll back to previous states in case something goes wrong. 

Raw data often suffers from issues related to consistency, completeness, accuracy, and versioning. Inspecting data quality issues, waiting for data stewards to respond, and fixing them are tedious processes and can create bottlenecks if done after the data integration step. This can lead to delays in analytics and decision-making at the destination. 

The solution is to use a data platform that combines data quality processes with data integration processes. A data integration platform that can learn about your data, smartly apply validation rules, and enrich the data can unblock this bottleneck. Data integration platforms like Nexla come with built-in functions for smart output validation, eliminating the need for coding. Given below is an interface for Nexla’s Nexset output validation rules. 

 Smart output validation in Nexla

Real-time vs. historical data 

The choice between real-time and historical data for generative AI applications depends on the use cases. While real-time data serves as the primary input for generative AI models, historical data provides the context. Hence, most generative AI applications employ a hybrid data pattern, utilizing both real-time and historical data to inform their decisions. 

For example, consider a customer service agent model. It utilizes real-time input from the user, along with historical data such as previous purchases and the organization’s customer service manuals.

A common challenge when using real-time data for generative AI applications is striking a balance between latency and privacy. Since most organizations use cloud-based LLMs for building generative AI applications, IT security teams impose strict policies about sending PII data to externally hosted LLMs. 

Hence, gen AI applications often intercept real-time data, identify PII through locally hosted LLMs or rule-based systems, and then send the data to externally hosted LLMs. This creates latency issues, as a single LLM’s call requires multiple hops between internal validation systems before a response is created. 

Data integration platforms that can mask PII in real-time from event streams can help mitigate this problem.

Hybrid generative AI application that uses real-time and historical data

AI data governance and privacy

Data collection is a key source of risk when building AI applications. Global and regional laws governing the ethical and lawful use of private data pose significant financial and reputational risks for organizations. 

Compliance

Adhering to regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is vital for an organization handling sensitive AI data. Organizations should be aware of the countries in which they operate and the laws they must implement within their systems.

Transparency & explainability

Accountability can only be maintained if the AI’s decisions are made understandable to human observers. This requires avoiding “black box” systems and a deep effort to understand—and therefore be able to regulate—an AI’s input/output behavior. A common way to ensure explainability in LLM output is to add prompt sections that trigger the LLM to explain how it arrived at the output. Data collected for AI applications should include metadata that aids LLMs in generating sufficient information to ensure transparency. 

Data privacy

Data contains advantageous and privileged information about people, processes, and places; therefore, the ethics of data are a vital and sometimes life-saving consideration. Failing to consider ethics can not only lead to legal issues, but also severe damage to the company’s brand and reputation when training or utilizing the AI in question. 

Copyright considerations

Multiple court cases are currently active, and some successful actions have been taken against companies that utilize copyrighted text or images for AI training. AI data collection must consider copyright, intellectual property, and the consent of stakeholders. 

Adding and verifying compliance and lineage information while creating new data products is a tedious task that requires manual approvals. A data integration platform with built-in governance can help streamline data operations. It is even more helpful if the integration platform has built-in support for third-party governance tools, such as Alatio and Collibra. 

Recommendations

Data is both a strategic asset and a risk vector in the generative AI era. Collecting data responsibly is critical while building effective, legally compliant generative AI applications. The following section provides important best practices for AI data collection. 

Prioritize collecting data based on use cases

AI data is closely tied to specific use cases. Before collecting data, one must clearly define the specification of the application and the end users. This helps in identifying domain-relevant data sources. For example, a generative AI agent operating in the legal domain requires legal documents, agreements, contracts, and other relevant information as context.

Establish thorough data quality rules

The quality of data is as essential as the volume of the data collected. In fact, a lower number of high-quality samples often gives better results than a larger number of low-quality samples in the case of LLM responses. This is because the collected data is often used in inference rather than training in the case of generative AI applications. 

Hence, it is essential to define protocols for ensuring and evaluating data quality, consistency, completeness, and accuracy from the beginning. Integration platforms like Nexla can automatically monitor data as it flows in real-time, apply smart validations, and integrate metadata intelligence on the fly. You can read more about maintaining data quality for real-time flows here. 

Ensure data diversity

As in the case of traditional AI applications, diversity of data is also critical in the generative AI era. While traditional AI applications primarily use the collected data for training, generative AI applications rarely do so. Instead, the collected data is used as context information that is passed to the LLM for responses. Since LLMs can be biased or opinionated based on the data they are trained on, it is even more important for the context data to be fair and well-represented. Overrepresentation of topics can lead to incorrect responses.

Another aspect of data diversity is the diversity of modality. For use cases involving multimodal LLMs, it is crucial to collect parallel data. Parallel data contains data from multiple modalities, such as text, audio, and images, that belong to the same semantic context or situation. For example, an agent who helps mechanics troubleshoot machinery failures should have access to operating manuals as well as videos of the specific machinery being fixed. 

Ensure data lineage and metadata integrity

Capturing data lineage and adequate metadata enhances transparency and auditability. Data Lineage helps debug LLM output and is also crucial for regulatory compliance. At the bare minimum, organizations must track the sources, acquisition method, transformations that occurred, annotation history, and governance attributes such as consent status, PII flags, and usage rights. 

Build privacy-aware and ethical data pipelines

An important question that one must ask while collecting AI data is ‘Will the subject of this data element reasonably expect this use of their data?’ Current global and regional data privacy guidelines, such as the CCPA and DPDP Act, emphasize informed, granular consent. 

Hence, it is essential to collaborate with all providers and stakeholders to collect data in an ethical manner and restrict data access to only those with a clear business need. Personally identifiable information must be anonymized, and users must be given an option to revoke their consent and request deletion. 

Implement data lifecycle management protocols

Implementing robust data versioning protocols is a crucial aspect of AI data collections.  LLM output is heavily dependent on contextual information, and unversioned changes to this data can lead to an unexplainable system that cannot be rolled back to stable states. Organizations must establish metrics to track data quality and data drift, enabling them to identify when data needs to be updated. 

Data integration platforms that provide no-code solutions for data versioning and setting up rules to monitor data properties reduce the manual effort required in data lifecycle management. While automated monitoring rules provide some assistance, human review and approval remain critical. Hence, it is best to plan and budget for a human-in-the-loop workflow for any generative AI applications. 

Conclusion

AI efficiency and accuracy have been driven primarily by data availability as much as by algorithm research and development. Collecting data from multiple sources with varying structures and freshness, while ensuring quality and adhering to strict governance policies, requires a Herculean effort. A data integration platform with comprehensive connector support, automated data quality validations, and built-in governance features can reduce this effort. Nexla is an enterprise-grade data integration platform built around the concept of data products. You can check out its features here.