According to McKinsey, 70% of AI projects fail.

The main reason isn’t bad algorithms or weak models.

It’s data quality.

Teams build sophisticated pipelines and fine-tune parameters, but none of that matters if the underlying data is messy, incomplete, or poorly structured.

AI data preparation is the foundation that everything else depends on, and most teams underestimate how much effort it requires. Data scientists spend 50-80% of their project time on preparation alone.

This guide covers what AI data preparation involves, why it matters, and how to do it step by step.

• 70% of AI projects fail due to poor data quality, not bad algorithms
• Most teams underestimate data prep effort and treat it as an afterthought - this is why they fail
• Data scientists spend 50-80% of their time on preparation alone
• The process follows 5 key steps: collect, clean, transform, validate, and optimize
• Different AI applications (RAG, fine-tuning, inference) need different preparation approaches
• Tools like Firecrawl simplify web data collection by converting messy HTML into clean, AI-ready formats

AI data preparation covers the full cycle of getting raw information ready for machine learning systems: pulling data from databases, APIs, web pages, and documents; removing duplicates and fixing inconsistencies; converting formats so models can parse them; and structuring everything into datasets that algorithms can learn from.

The typical pipeline follows this pattern:

• Collect data from your sources
• Clean by removing noise, handling missing values, and standardizing formats
• Transform into structures your model expects
• Validate quality and optimize for your specific workload

Different AI applications need different preparation approaches.

Training a model from scratch requires labeled datasets where each example has a correct answer attached. You also need balanced class distributions so the model doesn’t overfit to common cases.

RAG systems need text broken into chunks, typically 256 to 512 tokens each, with embeddings stored in vector databases for similarity search. Fine-tuning works with instruction-response pairs formatted specifically for the target model, often as JSONL files with prompt and completion fields.

Inference pipelines focus on real-time cleaning and caching, since production systems need to process new data quickly without batch preprocessing.

Here’s how it compares to traditional data preparation.

The main difference: AI data prep handles unstructured data and requires extra steps like annotation, augmentation, and train/test splits that traditional Business Intelligence (BI) workflows don’t need.

Understanding what data preparation involves is one thing. Understanding why it determines whether your AI project succeeds or fails is another. Most teams focus on model architecture and hyperparameter tuning while treating data prep as a preliminary chore. That approach explains why so many projects never make it past the proof-of-concept stage.

Research by organizations such as Gartner, Deloitte, and McKinsey consistently finds that in 70% or more of unsuccessful AI projects, the root cause is data-related issues rather than problems with the algorithms.

The rest stall somewhere between promising demo and deployed system. Data quality is the most common culprit.

Andrej Karpathy, former Director of AI at Tesla, put it this way in A Recipe for Training Neural Networks:

The first step to training a neural net is to not touch any neural net code at all and instead begin by thoroughly inspecting your data. This advice gets ignored constantly.

Teams jump straight to modeling, discover their data has problems, then spend weeks backtracking. The projects that succeed tend to front-load data work rather than treating it as an afterthought.

Poor data quality costs the US economy an estimated $3.1 trillion annually, according to IBM and Harvard Business Review. That number includes wasted compute cycles, incorrect business decisions, and failed AI initiatives.

In a 2023 survey by Arion Research, 39% of businesses cited data quality as their primary challenge when preparing data for AI. It ranked above privacy concerns (21%), bias (10%), and lack of available data (10%).

The pattern is consistent: organizations know data is the problem, yet still underinvest in preparation.

The gap between classroom ML and production ML surprises many developers.

One HackerNews commenter described it bluntly:

Universities and online challenges provide clean labeled data, and score on model performance. The real world will provide you… ‘real data’ and score you by impact.

Another practitioner estimated that actual modeling work accounts for only up to 20% of a typical data team’s time. The rest goes to wrangling, cleaning, and preparing data.

These aren’t edge cases. They reflect what most teams encounter once they move past tutorials and start building real systems.

Before covering the step-by-step process, here are the specific technical obstacles you should expect.

Beyond the time investment, teams face specific technical obstacles during data preparation.

• Data quality issues: Source data often arrives incomplete, inconsistent, or outdated. Missing values, duplicate records, and conflicting formats require detection and handling before training can begin.

• Unstructured data handling: Web pages, PDFs, and documents contain noise that models can’t process: navigation menus, ads, scripts, and inconsistent HTML structures. Extracting clean content from these sources requires specialized tooling.

• Privacy and security: Training data frequently contains personally identifiable information that must be detected and removed. Compliance requirements like GDPR and CCPA add constraints on what data you can collect and retain.

• Bias in datasets: Skewed class distributions cause models to favor majority outcomes. Historical bias in labels perpetuates discrimination. Detecting and correcting these issues requires deliberate auditing.

• Scale and volume: Large datasets strain memory and processing capacity. Efficient batching, streaming, and distributed processing become necessary as data grows.

• Lack of standardization: Data arrives in CSV, JSON, XML, HTML, and proprietary formats. No universal schema exists for AI training data, so teams build custom parsers for each source.

With the challenges in mind, here’s how to actually prepare data for AI applications.

The process follows five stages: collection, cleaning, transformation, validation, and optimization.

Each stage builds on the previous one, and skipping steps usually creates problems downstream. This section walks through each stage with practical guidance you can apply immediately.

Data for AI comes from multiple sources, and each requires different extraction approaches.

Internal data lives in databases, data warehouses, and existing datasets your organization already maintains. SQL exports, API calls to internal services, and direct database connections get this data into your pipeline. The main challenge is access permissions and ensuring you’re working with production-quality snapshots rather than stale copies.

External APIs provide data from third-party services. Weather data, financial feeds, social media content, and industry databases all expose APIs for programmatic access. You’ll need to handle authentication, rate limiting, pagination, and error recovery. Most APIs return JSON, which simplifies parsing but may require flattening nested structures.

Documents like PDFs, Word files, and spreadsheets contain valuable unstructured content. PDF extraction is notoriously inconsistent since the format prioritizes visual layout over data structure. Scanned documents need OCR before text extraction. Libraries like PyMuPDF, pdfplumber, and python-docx handle common formats, but expect edge cases that require manual cleanup.

Web data requires either scraping or crawling. Scraping extracts data from a single page. Crawling follows links across an entire site, collecting data from multiple pages automatically. Most AI projects need crawling since training data rarely lives on one page. Tools range from Beautiful Soup for simple parsing to Playwright for JavaScript-heavy sites.

For AI workflows, Firecrawl handles rendering and format conversion in one step. If you’re new to this, the web scraping beginner’s guide covers the fundamentals.

Whatever your source, document data provenance and respect access restrictions. For web data specifically, check robots.txt and terms of service before large-scale collection.

Raw data is messy. Cleaning involves three main tasks: removing duplicates, handling missing values, and standardizing formats.

Duplicates skew model training by overweighting repeated examples. Simple hash-based deduplication catches exact matches. Fuzzy matching catches near-duplicates with minor text variations.

Missing values require more thought. Your options include:

• Imputation: Fill gaps with mean, median, or predicted values
• Indicators: Create a separate feature flagging missing entries
• Removal: Drop rows or columns with excessive missing data (typically >50%)

One critical rule: fit any imputation logic on training data only, then apply it to validation and test sets. Fitting on the full dataset causes data leakage and inflates performance metrics artificially.

Format standardization covers dates, currencies, text encodings, and other inconsistencies that accumulate when data comes from multiple sources.

AI systems need data in specific structures. The right format depends on your use case, but three options cover most scenarios: JSON for structured records, plain text for simple sequences, and Markdown for content with hierarchy.

Markdown deserves special attention for LLM applications. Unlike raw HTML, Markdown preserves semantic structure (headers, lists, emphasis) without the noise of tags, scripts, and styling.

This matters for two reasons:

• First, LLMs understand Markdown natively since it appears throughout their training data.
• Second, removing HTML clutter cuts token counts by roughly 50%, reducing API costs and fitting more useful context into each request.

For tabular data, transformation includes encoding categorical variables. One-hot encoding works for low-cardinality features. Label encoding suits tree-based models. High-cardinality features benefit from target encoding or learned embeddings.

Scraping the internet meant broken scripts, bad data, and wasted time. With Firecrawl Extract, get any data in any format effortlessly – in a single API call.

Before training, validate that your data meets quality standards. This catches problems that cleaning missed.

Quality checks include:

• Completeness: Are required fields populated?
• Consistency: Do values fall within expected ranges?
• Accuracy: Do spot-checks against source data reveal errors?

Bias detection matters for models that affect people. Check class distributions for imbalance. Look for demographic skew in labeled data. Imbalanced classes cause models to favor majority outcomes, which compounds existing biases in production.

Schema validation ensures structural consistency, especially for data from multiple sources. Define expected fields, types, and constraints, then reject or flag records that violate them.

Different AI applications need different optimizations.

For RAG systems, chunking strategy matters most.

Split documents into segments of 256 to 512 tokens, small enough for precise retrieval but large enough to preserve context. Add 50 to 100 tokens of overlap between chunks so information at boundaries isn’t lost. Attach metadata (source URL, document title, section headers) to enable citation and filtering in responses. Store chunks as vector embeddings in a database like Pinecone, Chroma, or Weaviate for similarity search at query time. The quality of your chunks directly affects retrieval accuracy, so test different sizes with your actual queries.

For a deeper comparison of approaches, see chunking strategies for RAG and vector database options.

For fine-tuning, format data as JSONL with clear prompt and completion fields. Split into training (80%), validation (10%), and test (10%) sets. Ensure the test set represents the distribution you expect in production, not just a random sample.

For inference pipelines, optimize for speed. Cache cleaned versions of frequently accessed data. Implement real-time PII detection and redaction if processing user inputs. Design around the latency requirements of your application.

These five steps cover the core pipeline. The next section covers tools that simplify web data collection, which is often the messiest part of data preparation.

The steps above work regardless of your tools. But for web data specifically, Firecrawl simplifies the messiest parts of the pipeline.

Firecrawl handles the work that typically eats up engineering time before model training can begin. You don’t need to build custom scrapers for each site. You don’t need to wire up headless browsers to render JavaScript. You don’t need to write parsing logic to strip out HTML artifacts.

Point it at a URL, and it returns clean data.

The platform handles rendering, extraction, and cleanup in one step. Sites built with React, Vue, or other JavaScript frameworks work out of the box. So do pages with anti-bot protections, since Firecrawl manages proxy rotation and browser fingerprinting automatically.

Raw HTML is full of noise that wastes tokens and confuses models: navigation menus, ads, cookie banners, tracking scripts. Firecrawl strips all of that out.

Instead, you get clean markdown that preserves the content structure (headers, lists, paragraphs) without the clutter. You can also request JSON or structured data if your pipeline needs specific fields. The output is ready for chunking, embedding, or direct prompting with minimal additional processing.

For teams building RAG systems, fine-tuning datasets, or AI agents that browse the web, this removes a layer of complexity from the data preparation workflow. For a practical example, see how to turn any documentation site into an AI agent using Firecrawl and LangGraph.

Get started at docs.firecrawl.dev.

The 70% failure rate for AI projects isn’t a mystery. Most teams don’t fail because of model architecture or hyperparameter choices. They fail because they underestimate data preparation.

The process follows a clear path: collect data from your sources, clean out noise and inconsistencies, transform it into formats your models can consume, validate quality before training, and optimize for your specific workload.

Each step builds on the previous one. Skipping any of them creates problems that surface later in the pipeline.

Data cleaning is one step within data preparation. Cleaning focuses on fixing errors, removing duplicates, and handling missing values. Data preparation covers the full pipeline: collecting data, cleaning it, transforming formats, labeling examples, and splitting into training and test sets.

It depends on your task and model type. Simple classification tasks might need a few thousand labeled examples. LLM fine-tuning typically requires hundreds to thousands of instruction-response pairs. In most cases, data quality and diversity matter more than raw volume. A smaller, well-curated dataset often outperforms a larger, noisy one.

Yes. Synthetic data can supplement or replace real data, especially for rare edge cases or privacy-sensitive domains. Research from MIT has shown that synthetic data can match or exceed real data performance in certain situations. The key is validating that synthetic examples reflect the distribution you expect in production.

For fine-tuning, JSONL files with prompt and completion fields work best. Conversational models use a messages array with role assignments (system, user, assistant). For RAG systems, Markdown preserves document structure while staying readable for both humans and models.

Start by auditing your class distributions and demographic representation. Use stratified sampling to ensure balanced splits. For imbalanced classes, consider augmentation techniques or oversampling underrepresented categories. Regular audits throughout development catch bias before it reaches production.

Raw HTML wastes tokens on content that adds no value: navigation menus, ads, tracking scripts, cookie banners. This noise confuses models and inflates costs. Clean Markdown preserves the semantic structure (headers, lists, paragraphs) without the clutter, reducing token usage by roughly 60%.

Web scraping is a common data collection method. According to Arion Research, 32% of businesses use web scraping to gather data for AI applications. The main challenges are handling JavaScript-rendered pages, bypassing anti-bot protections, and converting messy HTML into clean formats. Tools like Firecrawl handle these automatically.