The importance of data collection and integration for enterprise AI

The advent of generative AI has led several prominent companies to limit its use due to mishandling of sensitive internal data. According to CNN, some companies have instituted internal bans on generative AI tools to better understand the technology, and many have also blocked internal ChatGPT use.

Companies often still accept the risk of using internal data when exploring large language models (LLMs). Because these contextual data allow LLM to change from general-purpose knowledge to domain-specific knowledge. In a generative AI or traditional AI development cycle, data collection serves as the entry point. Here you can collect, preprocess, mask and transform raw data tailored to your company’s needs into a format suitable for LLM or other models. There is currently no standardized process to overcome data collection challenges, but the accuracy of the model depends on them.

4 pitfalls of poorly collected data

1. Generates incorrect information: If LLMs are trained on contaminated data (data that contains errors or inaccuracies), incorrect answers may be generated, leading to poor decision-making and potential cascading problems.
2. Increased Volatility: Variance is a measure of consistency. Insufficient data can cause answers to vary over time or introduce misleading outliers, which can especially affect small data sets. A large variance in a model can mean that the model works well with the training data but is inadequate for real-world industrial use cases.
3. Limited data scope and unrepresentative answers: If your data sources are limited or contain identical or incorrect duplicates, any results may be skewed by statistical errors such as sampling bias. This can cause the model to exclude entire areas, departments, demographics, industries, or sources from the conversation.
4. Challenges of correcting biased data: If the data is biased to begin with, “the only way to retroactively remove some of that data is to retrain the algorithm from scratch.” When an LLM model is vectorized, it is difficult to discard answers derived from unrepresentative or contaminated data. These models tend to reinforce understanding based on previously assimilated answers.

Data collection must be done right the first time. Handling this incorrectly can lead to numerous new problems. The training data base for an AI model is similar to piloting an airplane. If the takeoff angle is off by 1 degree, you may land on a completely new continent than expected.

Your entire generative AI pipeline depends on the data pipeline that supports it, so taking the right precautions is essential.

Four key components to ensure reliable data collection

1. Data Quality and Governance: Data quality means ensuring the security of data sources, maintaining holistic data, and providing clear metadata. This may also involve working with new data through methods such as web scraping or uploading. Data governance is an ongoing process in the data lifecycle that helps ensure compliance with laws and company best practices.
2. Data Integration: These tools allow businesses to combine disparate data sources in one secure location. A widely used method is Extract, Load, Transform (ELT). In an ELT system, data sets are selected from siled warehouses, transformed, and then loaded into a source or target data pool. ELT tools, such as IBM® DataStage®, facilitate fast and secure transformation through parallel processing engines. By 2023, the average enterprise will receive hundreds of different data streams, making efficient and accurate data transformation critical to developing existing and new AI models.
3. Data cleaning and preprocessing: This includes formatting data to meet specific LLM training requirements, adaptation tools, or data types. Text data can be chunked or tokenized, and imaging data can be stored as embeddings. You can perform comprehensive transformations using data integration tools. Additionally, you may need to manually manipulate the raw data, such as deleting duplicate data or changing data types.
4. Data storage: After data has been organized and processed, the problem of data storage arises. Since most data is hosted in the cloud or on-premises, businesses must decide where to store their data. It is important to be careful when using an external LLM to process sensitive information such as personal data, internal documents or customer data. However, LLM plays an important role in fine-tuning or implementing retrieval augmented generation (RAG) based approaches. To mitigate risk, it is important to run as many data integration processes as possible on internal servers. One potential solution is .

Start collecting data with IBM

IBM DataStage combines a variety of tools to simplify data integration, making it easy to import, organize, transform and store the data you need for AI training models in your hybrid cloud environment. Data practitioners of all skill levels can use the tool using a no-code GUI or by accessing an API with guided custom code.

The new DataStage as a Service Anywhere remote runtime option provides flexibility in executing data transformations. You can use parallel engines anywhere., It gives you unprecedented control over your location. DataStage as a Service Anywhere appears as a lightweight container, allowing you to run any data transformation function in any environment. This avoids many of the risks caused by incorrect data collection when performing data integration, cleansing, and pre-processing within a virtual private cloud. DataStage allows you to address all your data needs for your generative AI initiatives while maintaining complete control over security, data quality, and efficiency.

There are virtually no limits to what can be achieved with generative AI, but there are limits to the data the models use, and that data may make a big difference.

Schedule a meeting to learn more. Try DataStage with a data integration trial.