When we’re performing data preprocessing on your knowledge base, data cleaning (or data cleansing, or data scrubbing) is an important step. We don’t want to feed bad info into an artificial intelligence system. Data cleaning ensures that the AI is getting the best information so it can deliver the best outputs.
In the context of enterprise AI implementation, data preprocessing involves getting all of an organization’s knowledge base ready for AI ingestion.
Data cleaning is a crucial component of data preprocessing. It lays the foundation for efficient AI querying, accurate results, and overall better performance. Think of it like priming the walls before painting: skip it, and your paint will look terrible.
There are many different use cases for enterprise AI implementation, and most of them center around the efficiencies gained from having an internal AI expert that can be queried. Whether this AI takes the form of a RAG system, a fine-tuned LLM, or any other instantiation of generative AI, the starting point is the same: relevant documents and media need to be optimized for maximum intelligibility to the AI.
Data cleaning is how we prepare your knowledge base to be parsed by the AI system.
At a very basic level, data cleansing is the manipulation or editing of documents and media to resolve inaccuracies, omissions, mistakes, and duplicate content. It also involves the pruning down of content to that which is essential for the AI to understand.
Here are the “3 C’s” of data cleaning:
Here are our basic steps to effective data cleaning as a part of document preparation:
These steps form a systematic approach to cleaning data and preparing it for AI ingestion. Data cleaning is an iterative process, and we may need to revisit these steps multiple times to achieve high-quality, usable data.
Each use case and knowledge base is different, so we take an open-ended, flexible approach to data preprocessing and cleaning. Here are some general guidelines and best practices we follow in the data cleansing process:
By following these best practices, we help your organization create a clean, consistent knowledge base that is maximally intelligible to your AI instance.
Here are ten examples of how data cleaning prepares the way for effective AI implementation.
If you need help with data cleaning or any other aspect of data preprocessing and document prep, schedule a free consultation to learn how we can solve your problems. Or, check out our service offerings for the full scope of our offerings.
Data cleansing and data cleaning have slightly different focuses in the context of machine learning and neural networks.
For purposes of document preparation, we use the terms interchangeably.
After data cleaning, we may need to perform other methodologies to your knowledge base. These include, but are not limited to, the following:
Once the entire data preprocessing task is completed, your knowledge base is ready for AI ingestion.
Here are some top tools that can aid in the data cleansing process:
Data cleaning can be challenging, due to large volumes, varied formats, inconsistent entries, missing values, duplicate values, extreme values, errors, integration from multiple sources, and other factors.
Depending on the needs of a specific use case, some aspects of data cleaning can be done with tools (automated), but much of it often requires manual implementation. Even when data cleaning is automated, human oversight and review is imperative.
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