AI-Ready Data: How to Extract and Clean Raw Data for a GenAI/LLM, Step by Step

Data that’s ready for AI—meaning it’s properly cleaned, structured, labeled, and ready to train and deploy AI models—is crucial to capturing value in an AI project.

Four parts to AI-ready data

Getting data AI-ready has four key components:

1. people
2. processes
3. technology
4. data

People in the data preparation process

It is one thing to be an organization that prioritizes a mature data culture where information is abundant, easy to access, and aligned with AI modeling needs. It is quite another to integrate that data so it flows seamlessly across systems. The human element in AI data readiness is a workforce with the necessary skills and knowledge to navigate AI implementation.

Processes

Organizations should take time to create and document an efficient process to maintain a secure, compliant, and usable data environment. The data must be ethically governed, unbiased, and accurate to serve as a solid foundation for AI models. Processes that govern how data is prepared involve meticulous raw data cleaning and structuring to ensure AI models deliver reliable outcomes.

Technology

Data intelligence platforms and catalogs make it easy for data scientists to locate and use the most relevant data for training AI models. These tools bridge the gap between raw information and AI-ready data so organizations maximize the value of their AI initiatives.

The importance of high-quality data

High-quality data directly impacts the accuracy and reliability of AI-generated insights and outputs. Data validation makes sure the data used is accurate and reliable. Organizations prioritizing data quality are better positioned to harness the full potential of AI technologies.

The relationship between data quality and AI performance is clear: good training data enhances model reliability, while poor-quality or inaccurate data leads to suboptimal results. This correlation underscores why data leaders consistently emphasize that trusted data is a prerequisite for trusted AI.

However, many organizations face significant challenges in maintaining data quality. According to recent surveys, 29% of organizations report issues with data that diminish the value they derive from AI initiatives. Furthermore, an IDC survey revealed that 24% lack trust in their data. Successful data preparation ensures consistency and relevance, leading to better AI outcomes.

How to prepare data for AI

Data preparation transforms raw, unstructured data into a format that AI algorithms effectively process and analyze. This step accounts for up to 80% of the total workload in AI initiatives.

Data prep workflow:

Preprocess

Data preprocessing and quality assurance clean the data to remove errors, inconsistencies, and duplicates.

Transform

Data transformation adapts the data structure to meet the specific requirements of AI models.

Prepare

Data preparation ensures the model generalizes well to new data and makes accurate predictions.

Select features

Feature selection identifies the most relevant variables for the AI task.

Reduce

Data reduction techniques manage large datasets efficiently.

AI-ready data step-by-step

When we prepare data well, algorithms identify patterns more accurately, leading to more trustworthy AI systems. Conversely, poorly prepared data introduces biases, errors, and inefficiencies that compromise the effectiveness of AI solutions.

Data collection and cleaning

Preparing data for AI begins with data collection.

This is where new data integrates with existing databases to enrich data. The sources range from point-of-sale systems and customer feedback forms to online reviews and social media mentions.

Organizations often use automated interfaces (APIs) to streamline this process. During this cleaning process, engineers transform raw data into data useful for AI and machine learning applications.

If the raw data is unreliable, it will lead to accuracy losses. But data cleaning prevents inaccuracy by handling missing values, outliers, and inconsistencies to protect data quality and prevent biased analysis.

To identify missing values:

• input average values for missing ratings
• use forward-fill or backward-fill techniques for time-series data
• replace missing numeric values with the mean or median of the column
• delete rows with missing critical data when necessary

Outliers also greatly impact the performance of a machine learning model. One common approach is using z-score normalization to identify outliers (typically those with a z-score above 3 or below -3). Once identified, outliers are removed or capped at a certain value to prevent them from skewing the analysis.

Finally, addressing inconsistencies involves standardizing naming conventions across the dataset and setting up automated checks to flag inconsistencies. This ensures that the data is uniform and coherent.

Transforming data for AI

After collecting and cleaning raw data, it’s time to normalize and transform it. This process converts cleaned data into a format that machine learning algorithms effectively interpret and learn from. After transformation, the true potential of your data begins to unfold.

Feature scaling and encoding are the most common techniques for transforming data.

Feature scaling

Feature scaling and normalization bring all numerical features to a similar scale, allowing them to contribute equally to a model’s learning process. This is particularly important for algorithms sensitive to the magnitude of input features, such as neural networks or k-nearest neighbors.

Encoding

Encoding is crucial for handling categorical variables. Techniques like one-hot encoding, available in libraries such as scikit-learn, convert categorical data into a format that algorithms can process by creating binary columns for each category.

Other transformation techniques include aggregating data to meaningful units (e.g., converting hourly sales data to daily totals) or calculating derived features (e.g., first-year spending for customers based on their purchase history). More advanced techniques using neural networks and deep learning are necessary to extract meaningful features from unstructured data like emails or images.

Data reduction and splitting

Data reduction and splitting are techniques particularly useful for areas where quick, accurate decisions are required, such as marketing applications. These processes simplify datasets, making it easier for machine learning models to identify patterns and generate insights efficiently.

Reduction

Data reduction techniques decrease dataset complexity without sacrificing the integrity of the information they contain. This is valuable for high-dimensional data, where the number of features overwhelms machine learning algorithms. Dimensionality reduction methods, such as Principal Component Analysis (PCA), are commonly employed.

Chunking is another reduction technique used for large datasets or streaming data. It involves breaking down datasets into smaller, more manageable pieces or “chunks.” This approach processes data that might otherwise be too large to handle in memory simultaneously. Chunking is particularly useful when data needs to be processed in parallel or when working with time-series data in marketing analytics.

Splitting

Data splitting divides datasets appropriately for training and testing purposes so machine learning models can generalize. The most common approach is to divide the dataset into training and test sets, typically using a ratio of 70-30 or 80-20. The training set is used to teach the model, while the test set serves as unseen data to evaluate the model’s performance. A third validation set may be introduced for more complex models or when fine-tuning is necessary.

Overcoming common challenges

Common hurdles in data preparation:

• data inconsistencies
• missing or null values
• duplicate entries
• difficulties merging data from diverse sources

Inconsistencies

Data inconsistencies arise from different data entry practices or changes in data collection methods over time. To combat this, organizations implement regular data audits and establish standardized data entry guidelines that reduce the likelihood of errors in the analysis.

Missing or null values

Missing or null values, if not properly addressed, lead to biased or incomplete analyses. Strategies for handling missing data include interpolation techniques, mean substitution, or more advanced feature selection methods that work around missing values. The choice of method depends on the nature of the data and the specific requirements of the AI model.

Duplicates

Duplicate entries skew analysis results and waste computational resources. Automated deduplication processes identify and remove redundant data points.

Merging

Merging data from different sources presents compatibility issues due to varying formats, structures, or naming conventions. Advanced data integration tools with automated features make this process easier, reducing the manual effort required and minimizing the risk of errors.

Data preparation is not a one-time task but an ongoing process. As models evolve and new data becomes available, organizations must continually revisit and refine their data preparation steps.

Skills for data restructuring

Data engineers and data scientists understand both the technical and analytical aspects of data transformation to ensure that the data is in the optimal format for AI model training.

Key skills for data restructuring:

Proficiency in programming languages

Languages commonly used for data manipulation, cleaning, and transformation tasks include Python, R, and SQL. Python, in particular, offers a wide range of libraries like Pandas and NumPy for data restructuring.

Data structures and algorithms

A solid grasp of data structures (e.g., arrays, linked lists, trees) and algorithms is crucial for optimizing data storage and retrieval, which is vital for handling large datasets.

Data transformation tools

Tools like Apache Spark, Talend, and Alteryx offer advanced features to clean, transform, and integrate data.

Analytical skills

Data restructuring identifies patterns and relationships within the data. Data scientists make informed decisions about which features to retain, transform, or discard, ultimately improving the quality of the training data.

Knowledge of machine learning algorithms

Understanding how different machine learning algorithms work helps structure data to maximize the model’s performance. For example, knowing that certain algorithms are sensitive to feature scaling informs the decision to normalize or standardize the data.

Automating data preparation

Automated tools are workhorses for data prep. From cleaning and feature engineering to anomaly detection and feature selection, automation reduces the time and effort required for AI model training.

Automated feature engineering identifies and creates relevant features from raw data that human analysts might overlook. This process improves model performance by providing a richer set of input variables. Automated data cleaning tools efficiently detect and correct inconsistencies, handle missing values, and remove duplicates across large datasets.

Key areas where automation excels in data preparation:

• feature engineering and selection
• data cleaning and preprocessing
• data augmentation for limited datasets
• anomaly detection
• handling missing values and duplicates

These automated tools offer significant advantages, but choosing and configuring automated tools requires human expertise.

Data engineering and governance

Data engineers curate and process datasets, making them suitable for AI consumption. They also develop pipelines to handle the volume and complexity of data. They design systems for real-time data access and processing, optimizing storage and retrieval mechanisms using technologies like distributed systems and cloud computing. They also engage with data governance complexities, implementing robust security measures and clear policies aligned with regulations such as GDPR and CCPA. Additionally, they ensure data lineage is maintained to track data throughout its lifecycle.

Key responsibilities of data engineers in generative AI:

• create efficient, scalable data pipelines
• ensure datasets are structured, labeled, and representative
• implement data security and privacy measures
• develop regulatory compliance policies
• employ strategies like role-based access control
• continuously monitor and optimize system performance

Empowering AI-ready data: How DataStax streamlines data preparation

Proper data preparation influences model performance and AI accuracy for real-world applications. Follow the steps outlined here to overcome common challenges of data cleaning for GenAI and unlock the full potential of AI for your organization.

Use DataStax tools to streamline data preparation, reducing the manual effort it used to take to clean, transform, and integrate data.

The comprehensive suite of tools get your data AI ready so you confidently deploy AI models in the real-world. Here are the key features that give you back so much time and valuable resources, DataStax becomes a differentiator for success:

1. Unstructured integration for efficient data ingestion
2. Astra Vectorize for vector embedding generation, partnering with industry leaders like OpenAI
3. Robust data governance capabilities that maintain data quality and compliance throughout the preparation process

Data preparation will be one of the levers that determines future success in the AI-powered world. DataStax's platform gives you the competitive edge you’re looking for today.