Data preprocessing services
Knowledge formatting
Knowledge consistency
If you have inconsistencies in your knowledge base, that’s a problem. We’ll make sure all info is accurate and consistent.
Knowledge enrichment
What is data preprocessing?
Data preprocessing is the preparation of data for machine learning, data analysis, or artificial intelligence systems to ingest.
For Talbot West clients, data preprocessing often takes the following form: an organization needs its knowledge base to be queried by an AI system, which usually functions as an internal expert on the workings of the company.
Said knowledge base consists of documents, media, and other forms of data. This data needs to be “fixed up” to be optimally intelligible to the AI system. Without preprocessing, the AI's outputs will be of significantly lower quality, reducing the ROI to the organization.
Let’s talk about data preparation
Data preprocessing techniques
Data cleansing
Data transformation
We’ll convert your data into the right formats. This may include normalization, standardization, and discretization.
Data integration
If you have multiple datasets, we’ll need to combine them in one coherent dataset that the AI system can reference.
Data reduction
We’ll aggregate, consolidate, and streamline your dataset so the AI system has less noise to parse through.
Feature engineering
Feature selection and feature extraction make your dataset more intelligible to AI systems so you get better responses.
Data augmentation
If your dataset is lacking key context, we need to enrich it. This is especially applicable when your dataset is small.
See our article titled “What is data preprocessing” to learn more.
Why do I need data preprocessing?
You’re implementing some sort of AI system to streamline operations. That AI system will be trained on your knowledge repository—that mass of documents, videos, and other media that has accrued and that represents the combined understanding of “how things work” at your organization. But your knowledge base probably isn’t ready.
Most organizations didn’t build their knowledge repositories with an eye to future AI implementation. In other words, from a data ingestion perspective, your repository is probably a mess. Inconsistent formatting, missing context, lack of clarity, and internal inconsistencies plague most knowledge bases.
You’ve heard of “garbage in, garbage out” (GIGO), right? Nowhere is that phrase more applicable than here. Data preprocessing is one of the most critical steps in AI implementation. The lack of proper preparation can doom your AI instance to mediocrity, while the correct prep will give you crisp, relevant outputs.
Why choose Talbot West for document preparation?
Document preparation is tedious and must be done to meticulous standards. If you’re like most businesses, you don’t have time to edit and update a ton of documents and media.
- Consistency and standardization: we get all your documents into a uniform format.
- Efficiency: organized documentation speeds up information retrieval when your AI system is up and running.
- Accuracy: organization improves the accuracy of retrieved results.
- Time savings: your team can do what you do best, leaving the chore of document prep to the experts.
More info about data preprocessing
Data preprocessing offers the following benefits for any organization seeking to instantiate an artificial intelligence agent or system:
- Preprocessing helps in contextualizing the data, allowing the AI to understand the specific nuances and jargon used within your organization. This leads to more accurate and contextually relevant responses.
- By integrating data from various sources within your organization, preprocessing ensures that the AI has a comprehensive understanding of the knowledge base. This holistic view improves the AI's ability to provide well-rounded answers.
- Identifying and correcting errors in the data prevents the AI from learning and perpetuating these errors, thereby enhancing the overall accuracy of the AI's responses.
- A clean and well-organized knowledge base allows the AI to adapt more easily to new information or changes within your organization. This adaptability ensures that the AI remains up-to-date and relevant.
- With a well-prepared knowledge base, the AI can provide more precise and helpful responses to user queries, leading to improved user interaction and satisfaction.
Here are some scenarios in which data preprocessing and document preparation will pave the way for successful AI implementation.
- Healthcare provider: a hospital needs to create a queryable corpus of records and medical research for an AI diagnostic assistant. This will allow doctors to quickly access relevant patient histories and research findings for improved diagnosis and treatment plans.
- Legal firm: a law firm aims to standardize legal documents for an AI-powered legal research tool. This will enable lawyers to efficiently search for case precedents, legal statutes, and client records, and will enhance case preparation and strategy.
- University: an academic institution wants to organize its publications and research papers for an AI-driven research assistant. Researchers and students will be able to easily access a vast repository of academic work, fostering collaboration and innovation.
- Government agency: a government body plans to streamline regulatory and compliance documents for an AI compliance monitoring system. This system will help agencies monitor regulatory compliance and swiftly respond to policy changes.
- Financial institution: a bank needs to prepare financial reports and transaction records for an AI-based fraud detection system. This system will enhance the bank’s ability to detect fraudulent activities and ensure financial security.
- Tech company: a technology firm seeks to standardize internal technical documentation for an AI-driven knowledge management system. Engineers and developers will be able to find technical specifications and development guidelines, speeding up project timelines.
- Consulting firm: a consulting company wants to organize client reports and project documentation for an AI project management tool. This tool will allow consultants to better manage projects, track progress, and deliver insights to clients efficiently.
- Manufacturer: a manufacturing business aims to standardize product manuals and technical specifications for an AI-based maintenance and troubleshooting system. Maintenance teams will be able to quickly find solutions to technical problems, reducing downtime and improving productivity.
- Nonprofit: a nonprofit organization is prepping for an AI grant management system. The system will streamline the grant application process and improve the tracking of grant funding and outcomes. Retail company: a retail business needs to standardize customer feedback and sales reports for an AI-driven customer insights platform. This platform will empower the company to better understand customer preferences and market trends, leading to more effective marketing strategies and product offerings.
- Pharmaceutical company: a pharmaceutical firm wants to organize clinical trial data and research findings for an AI drug development assistant. The assistant will accelerate drug discovery and development processes through efficient data access and analysis.
- Insurance: an insurance company aims to standardize claims records and policy documents for an AI claims processing system. The system will drive faster and more accurate claims processing, which will improve customer satisfaction and profit margins.
In each of these scenarios, the company or organization is implementing some sort of internal body of knowledge that will be queried by an LLM or other predictive model. In each situation, proper document preparation is critical for the system to work properly.
Data cleaning, also known as data cleansing or data scrubbing, is a crucial step in AI development and implementation. It involves identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets. You need your dataset to be accurate, complete, and reliable for analysis or machine learning models. Here are the some of the components of data cleaning:
Handle missing values
- Your knowledge repository may have gaps where certain information is missing. This could be due to incomplete documentation, data entry errors, or changes in “the way things are done around here” over time. Here’s how we address these:
- Imputation: fill in missing information where possible. For example, if some documents lack author names, infer them based on similar documents or on metadata.
- Removal: if certain documents are incomplete, you may want to remove these documents to preserve the quality of search results.
Remove noise and outliers
- Your repository might contain irrelevant or erroneous data, such as outdated documents or incorrect entries. Standard approaches to this include the following:
Outlier detection: identify and isolate documents that are significantly different from the rest. For example, documents with incorrect dates or anomalous metadata entries. - Noise reduction: filter out irrelevant data that could interfere with search accuracy, such as extraneous notes, or drafts that are not useful for querying.
Correct errors
We’ll want to identify and correct any errors in your documentation.
- Validation: check for typographical errors, inconsistent data entries, and logical inconsistencies, and correct any instances. For example, ensure dates are formatted consistently and employee names are spelled correctly.
- Standardization: standardize the format of data entries, such as dates, names, numerical values, and titles, to ensure uniformity. This helps the AI system process queries more effectively.
Remove duplicate records
Duplicate documents can clutter the repository and confuse the AI system during querying. During deduplication, we identify and remove duplicate entries to streamline the repository.
Resolve inconsistent data
- Consistency checks: verify that data is logically consistent. For example, check that project completion dates are after start dates, and all related documents are linked correctly.
- Correction: adjust values to ensure consistency, such as unifying different naming conventions for the same entity (e.g., using a single format for department names).
Relevance assessment
Identify and remove documents that do not add value to the knowledge repository, such as outdated policy documents or irrelevant meeting notes.
Data transformation involves changing the format, structure, or values of your data to make it more comprehensible and consistent for an AI system. The goal is to enhance the data's usefulness without changing its meaning.
- Ensure consistency and standardization across diverse data sources
- Improve the AI's ability to understand and interpret the information
- Enhance search and retrieval capabilities
- Facilitate more accurate and relevant responses to queries
- Enable the AI to make connections and inferences across the knowledge base
Here are some of the steps we may take to transform your data.
Data normalization
- Scaling numerical features to a standard range (e.g., 0 to 1), sometimes called min-max scaling
- Helps prevent features with larger scales from dominating the analysis
- Standardizing text format across documents (e.g., lowercase, removing special characters)
- Ensuring consistent terminology and acronym usage across the knowledge base
Document structuring and standardization
- Transforming data to have zero mean and unit variance
- Useful for algorithms sensitive to the scale of input features
- Converting unstructured documents into a structured format
- Organizing information into consistent sections or fields
Metadata enrichment and encoding categorical variables
- Adding tags, categories, or labels to documents
- Creating indexes for faster retrieval and more accurate matching
- One-hot encoding: creating binary columns for each category
- Label encoding: assigning numerical labels to categories
- Ordinal encoding: assigning ordered numerical labels to ordinal categories
Discretization
- Converting continuous variables into discrete categories
- Can simplify data and reduce the impact of small fluctuations
Entity recognition and linking
- Identifying and tagging important entities (people, places, products)
- Linking related entities across different documents
Content summarization
- Generating concise summaries of lengthy documents
- Creating abstracts or key points for quick reference
Language translation
- Translating documents to a common language if the organization is multilingual
- Ensuring consistency in translated terms
Date and time standardization
- Converting all timestamps to a single format and time zone
- Handling historical data with different date formats
Numerical data formatting:
- Standardizing units of measurement
- Ensuring consistent decimal and thousands separators
Semantic annotation
- Adding semantic markup to enhance understanding of content
- Identifying relationships between different pieces of information
Format conversion
Converting different file formats (PDFs, Word docs, spreadsheets) into a unified, AI-readable format
Noise reduction
- Removing irrelevant information or boilerplate text
- Filtering out outdated or redundant data
Versioning
- Implementing a system to track different versions of documents
- Ensuring the AI accesses the most up-to-date information
Cross-referencing
- Creating links between related pieces of information across the knowledge base
- Establishing a network of interconnected data points
Privacy and security transformations
- Anonymizing sensitive information
- Implementing access controls and data masking where necessary
Data imputation is a key step in data preprocessing. It involves replacing missing or incomplete data with substituted values. This is essential for maintaining the integrity and usability of a dataset, particularly when preparing an internal knowledge base for querying by an AI system.
Missing data can arise from many sources, such as human error, incomplete data entry, missed data collection, or integration of disparate data sources. Whatever the cause, data imputation can “smooth out” the gaps to make your dataset more intelligible to AI systems.
Data preprocessing is all about getting your internal knowledge repository into a suitable format so that an LLM or other AI system can understand it and return the best responses to your queries. While data preprocessing is a complex science, it can generally be broken down into the following steps. Of course, depending on the state of your internal documentation, some preprocessing steps may be omitted or additional preprocessing tasks added.
- Identify sources: gather documents, databases, emails, reports, and any other relevant information from departments within the organization.
- Consolidate data: merge data from different sources into a unified repository to ensure comprehensive coverage of the organization's knowledge base.
- Remove duplicates: identify and eliminate duplicate documents to avoid redundancy.
- Resolve missing values: address gaps in the data by either filling in missing values with appropriate estimates or removing incomplete records if necessary.
- Correct errors: fix typographical errors, inconsistent data entries, and logical inconsistencies. standardize formats (e.g., dates, names) across all documents.
- Normalize data: ensure data is in a consistent format, such as standardizing date formats and units of measurement.
- Encode categorical data: convert categorical information into a format that the AI system can easily process, such as one-hot encoding for textual labels. Ensure that categorical features are clearly demarcated.
- Feature selection and feature extraction: identify and extract key features that are relevant for querying, such as metadata, keywords, and summaries.
- Merge related data: combine related documents and datasets to provide a holistic view. ensure all links and references between documents are intact and correctly mapped.
- Ensure consistency: align data formats and resolve discrepancies between different datasets to maintain consistency.
- Dimensionality reduction: simplify the dataset by reducing the number of variables, using techniques like principal component analysis (PCA) if applicable.
- Filter irrelevant data: remove data that is not pertinent to the AI’s querying tasks, focusing on high-value information that will enhance the AI's responses.
- Binning: group continuous data into discrete intervals or bins to simplify the analysis.
- Aggregation: summarize data points to reduce volume while retaining essential information, such as aggregating logs or transaction data.
- Tagging: add relevant tags or labels to documents to facilitate easy retrieval and context understanding by the AI.
- Metadata enrichment: enhance documents with additional metadata, such as author, date of creation, and relevant keywords.
- Consistency checks: perform checks to ensure that data entries are logically consistent (e.g., ensuring project start dates precede end dates).
- Review and validation: have subject matter experts review the processed data to verify its accuracy and relevance.
- Anonymization: anonymize sensitive information to protect privacy and comply with data protection regulations.
- Access controls: implement access controls to ensure that only authorized personnel can modify or access the knowledge repository.
- Organize data: structure the repository in a logical manner, such as categorizing documents by topic, department, or project.
- Backup and recovery: implement regular backup procedures and establish a recovery plan to protect against data loss.
- Indexing: create indices for faster querying and retrieval of documents by the AI system.
- Performance tuning: optimize the repository for performance, ensuring quick access times and efficient processing.
- Regular updates: continuously update the repository with new information and remove outdated data to keep it relevant.
Retrieval augmented generation (RAG) combines retrieval-based and generation-based approaches to produce more accurate and informative text outputs. Think of it like an LLM, such as ChatGPT, that is trained to query a specific dataset when given a prompt.
A RAG system includes two primary components:
- A vector database: the knowledge repository against which the LLM will query.
- An LLM—which could be any of the commercially available LLMs, such as Claude by Anthropic, ChatGPT by OpenAI, or Mistral; or it could be a custom LLM—connected to the vector database and able to query it.
It’s a bit more complex than that, but that’s the basic architecture. A RAG system is a good model for many organizations that wish to instantiate a custom internal AI expert.
For a RAG to be most effective, the knowledge encoded in the vector database should be cleaned, transformed, and enriched. Relevant features should be highlighted, and redundant features consolidated.
BaseN encoding is a family of binary-to-text encoding schemes. Essentially, they convert binary data, such as that contained in images or video, into a text-based format that is queryable by an AI system.
For example, Base64 encoding could be used to convert images and multimedia into a vector representation, which could then be stored in a vector database and be queried by a RAG or other system.
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