How to Prepare Data for AI

Key Takeaways

• Preparing data for AI is essential to ensure accurate insights and reliable performance, as raw, unstructured data can lead to errors and inefficiencies.
• The goals of data preparation are to ensure quality, quantity, and completeness for effective AI training.
• Data preparation includes collecting, cleaning, transforming, and labeling data while consolidating it from multiple locations for better accessibility.
• Automated tools like Unstructured AI simplify the data preparation process and reduce human error.
• Ongoing data preparation is crucial, as models evolve and new data becomes available, requiring regular updates to maintain model performance.

Why Is Preparing Data for AI Important?

AI thrives on high-quality data—the better prepared the data, the better the results. Raw data often contains errors, inconsistencies, and gaps that could severely impact the AI model's performance. 

Unstructured data is especially problematic, as AI models have a harder time processing it than structured data.

Proper data preparation for Generative AI reduces these issues, ensuring accurate insights and better model performance. While the specific percentage may vary, most surveys agree that data engineers spend between 40 and 80% of their time on data preparation activities.

Poorly prepared data can lead to faulty results or wasted resources. For example, missing values, data inconsistencies, and irrelevant data points all reduce the effectiveness of machine learning algorithms.

That is why this preliminary step is crucial for your AI implementation.

The Goal of Preparing Data for AI

When preparing data for AI, the primary goals are:

1. Quality: Data must be accurate, complete, and relevant.
2. Quantity: AI requires large datasets to build reliable and scalable models.
3. Completeness: Missing data points must be handled to avoid inaccurate outcomes.

An additional goal can be converting raw, unorganized (or unstructured) data into a clean, structured format that AI models can actually use.

With these goals in mind, let's explore the practical steps.

How to Prepare Data for AI: Step-by-Step Instructions

Before you start training GenAI using your company’s data, you need to prepare it. Following this step-by-step process should provide clear guidance and ease successful data preparation.

Step #1: Collect and Consolidate Raw Data

Before transforming your data, the primary step is data collection from all relevant internal and external sources. This can include:

• structured data from databases, 
• unstructured data from emails, or 
• sensitive data stored in data warehouses. 

Often, data is scattered across different locations—offices, data centers, factories, or creative hubs. When your data is siloed across multiple sites like that, it prevents AI from reaching its full potential. For AI services to operate effectively, data must be centralized. 

To maximize AI effectiveness, consolidate all data into a single location. 

Pro Tip: Collecting data from diverse sources avoids fragmentation and ensures you capture all relevant variables.

To collect data, you may first need to extract it from the documents it’s buried in. 

Take PDFs as an example. 

PDFs are often filled with text, images, and even tables. That’s why our first step is typically to extract all that information by using tools that grab the plain text; but also pull out images, figures, and tables.

For example, if we’re working with an invoice in a PDF format, we may extract:

• The invoice number, date, and total (text)
• A company logo (image)
• An itemized breakdown (table).

Step #2: Clean the Data

The next step should be data cleaning. It involves:

• identifying and fixing errors,
• removing duplicates, 
• handling missing values, 
• ensuring data consistency. 

This is arguably the most time-consuming process. If you fail to clean data correctly, your model’s performance will suffer.

Step #3: Transform Raw Data Into AI-Ready Formats

After cleaning, analyze the data through exploratory data analysis (EDA). This helps reveal its characteristics, distributions, and patterns, providing valuable insights for the next stages of preparation. 

Once the data has been analyzed, you can convert it into formats suitable for AI models. Transforming data may include changing data formats, converting categorical data into numerical values, or standardizing data units.

Transforming raw data can be dreary, particularly with unstructured data. 

Again, the goal is to convert your data into format that models can directly work with. So, if we’re dealing with unstructured data, we also convert it into a structured, hierarchical format.

Unstructured AI automates the process of transforming unstructured data into AI-ready formats, reducing the time and effort spent on manual preparation.

Imagine the data organized into categories or fields, like:

• Header Info: Invoice number, date, client name.
• Tables: Item names, prices, quantities, and totals.
• Images: Company logos or signatures.

Organizing data like this allows us to store it in a structured format, such as CSV files or even directly in a database, where each piece of data has its own column or field.

For unstructured text (like legal documents), we use natural language processing (NLP) techniques to break the text down into meaningful components. 

Depending on the data, we may employ techniques like tokenizing sentences, identifying keywords, and even classifying paragraphs by their purpose — e.g., terms and conditions, client details, etc.).

Step #4: Label the Data

If necessary, you may also need to label the data. 

This step involves assigning tags or labels to data points, allowing supervised machine-learning models (MLMs) to understand patterns and relationships within the data. If the data is not labeled correctly, the model may not learn properly, leading to inaccurate predictions.

The first step of data labeling is clearly defining what each label represents. You can create and standardize meaningful labels that align with your company’s needs using domain-specific knowledge.  

For example, image datasets might need objects tagged, while text data could require data classification and categorization by topic. 

Step #5: Address Dimensionality and Data Reduction

Large datasets often contain many irrelevant variables, leading to dimensionality issues and model overfitting. 

Dimensionality reduction techniques help remove unnecessary features and improve model performance.

In insurance, dimensionality reduction can help narrow down the most influential factors in a dataset that includes variables like policyholder age, claim history, geographic location, and credit score. 

And so, factors that don’t significantly impact claim outcomes can be removed. This allows your model to focus on the most relevant variables like claim history or credit score, thus improving overall performance.

Step #6: Ensure Data Integration and Consistency

Once you’ve cleaned and reduced your dataset, ensure that all data sources fit together without inconsistencies. Poor integration leads to fragmented data and can result in inaccurate models.

Step #7: Validate Data Before Training

Before training an AI model on data, ensure that the input data is free from errors and in the correct format for machine learning (ML) algorithms. Data validation helps to catch any lingering issues that may have been missed during cleaning or transformation.

Common Myths About Data Preparation for AI

Myth #1: Sharing Data with AI Hosts Always Compromises Security

Before starting data preparation, one of the most important decisions is where to deploy your AI infrastructure. A misconception about AI deployment is that sharing data with a third-party AI host will inevitably lead to security vulnerabilities. While there can be risks, they depend on how and where the AI is hosted.

For companies in highly regulated industries, like finance or healthcare, we advise deploying AI solutions on your own on-prem infrastructure or self-managed VPC to safeguard against vulnerabilities and ensure your data never leaves your control. 

Our founder, Ankur Patel, advises that this approach is the most secure, particularly when managing sensitive data.

Myth #2: You Need a Perfect Dataset

Broadly speaking, no dataset is perfect, and aiming for perfection often wastes time. What matters is having data that’s good enough to produce reliable results. The goal is improvement, not perfection.

Myth #3: Bigger Datasets Are Always Better

Bigger isn't always better. While quantity is important, larger datasets with poor quality will harm your models. Focus on high-quality, relevant, and accurate data rather than sheer volume.

Working with noisy or incomplete data can lead to inaccurate results, so it’s more important to focus on clean, relevant, and structured data. 

"If there’s a lot of data that’s hard to work with, maybe it’s noisy and incomplete, then it’s better not to use this data. Let’s work with the remaining data, which is much cleaner." — Ankur Patel

Instead of prioritizing quantity, focus on gathering high-quality data that your models can learn from effectively.

Myth #4: Manual Data Preparation is Better Than Automated Tools

Many believe that manually preparing data is the only way to ensure accuracy. This simply isn’t true. Automated tools, like Unstructured AI, can clean, format, and transform data faster while reducing the risk of human error. It allows data scientists to focus on model development, rather than tedious data prep tasks.

Myth #5: Data Preparation is a One-and-Done Task

Some believe that data preparation is a one-time task that you can complete at the start and then forget about. However, this process requires continuous attention and refinement. 

Data preparation is an ongoing effort, not a one-time job.

As machine learning models evolve or as new data becomes available, you will often need to revisit your data preparation steps to ensure consistency, quality, and relevance.

AI models are dynamic and thrive on timely, high-quality data. Regularly updating and refining the data is key to maintaining model performance and accuracy over time.

Frequent Data Preparation Challenges to Expect

Like with any task, challenges can arise, and we advise our clients to prepare themselves ahead of time. Keep these points in mind before you start planning your AI strategy.

• Poor data quality: Incomplete or erroneous data requires cleaning, which can be time-consuming.
• Data consistency: Inconsistent formats and labeling across data points can hinder model training.
• Missing data: To ensure accurate insights and completeness, you must address missing values.

Common Dataset Characteristics, Issues, and Preparation Techniques

Ready To Streamline Your Data Preparation Process?

Data preparation for artificial intelligence is a complex but crucial step in building effective machine learning models. 

Whether you're dealing with unstructured data or massive datasets, proper preparation ensures that your AI systems deliver valuable insights and reliable results. Automated solutions like Unstructured AI simplify this process, making it faster and more accurate. 

Schedule a free 30-minute call with our team to discuss your specific needs. We’ll demonstrate how Unstructured AI can convert complex document formats, and guide you toward tailored AI solutions that fit your particular business.

‍