Data readiness for AI: How to make your data AI-Ready – Key insights for IT leaders

As AI becomes more widely used in various industries, data quality is everything and many organisations are interested in tapping into its potential for automation and innovation. But the first question you should ask yourself is: is your business data ready for AI? 

For most companies, the answer is, unfortunately, no. Studies show that 85% of AI projects fail, largely due to poor data preparation. Without well-prepared data, even the most advanced AI models can fail to deliver accurate and reliable results. This is where data preparation for AI becomes essential. To avoid this and make your AI investment worthwhile, it’s essential to understand how to prepare data effectively for AI.

In this article, we’ll dive into what data preparation for AI entails, why it’s critical for organisations and how a solid approach can make the difference between success and failure in your AI projects. From data collection to automation, you’ll discover the key steps, common challenges and the most effective tools to optimise this crucial process. Let’s get started!

What is data preparation for AI?

Data preparation is the process of gathering, cleaning, transforming and organising raw data to make it suitable for analysis, reporting, or training AI and machine learning models. It involves identifying and correcting errors, filling in missing values, removing duplicates and standardising formats to ensure consistency and accuracy. 

Data preparation often includes combining data from multiple sources, enriching it with additional context and structuring it in a way that aligns with analytical or computational requirements. This step is critical because the quality and readiness of data directly impact the reliability and performance of AI models and decision-making processes. Without thorough data preparation, even the most advanced analytical tools or AI systems may produce flawed insights, leading to poor decisions and costly errors.

Image 1: Data preparation for achieving data readiness and AI adoption

Why is data preparation for AI  essential?

Data preparation forms the foundation for accurate and efficient AI, making it critical for organisational success. Here we have highlighted a few reasons as per why it matters:

1. Improves accurate outcomes: The adage “garbage in, garbage out” is especially relevant in AI. Data preparation ensures AI models are trained on high-quality, relevant data, which improves the accuracy of insights and predictions.
2. Improves interpretability: Well-prepared data allows for greater trust in AI model outcomes. This transparency helps users understand AI decisions, which is crucial in high-stakes sectors like healthcare and finance.
3. Reduces costs: Clean data minimises the need for repeated analysis and corrections, which in turn reduces costs by saving compute resources.
4. Accelerates adoption: Well-structured data preparation accelerates the implementation and adoption of reliable AI tools, enabling faster integration into workflows.
5. Enables Cross-functional collaboration: Properly prepared data is more accessible across teams, fostering effective collaboration for AI projects.
6. Minimises technical debt: Good data preparation reduces the risk of accumulating poor-quality data, preventing future issues and cleanup costs.
7. Ensures compliance and ethics: Filtering sensitive information and addressing biases helps ensure compliance with regulations and ethical standards.
8. Strengthens data security: The data preparation process includes opportunities to improve data security, protecting sensitive information from breaches.
9. Supports Customization: Preparing data well allows for the fine-tuning of AI models to meet specific business needs, improving relevancy and performance.

Step-by-step framework for making your data AI-Ready

The success of any AI model depends on the quality of the data it is trained on. Preparing data for AI is a systematic and iterative process that ensures accuracy, reliability and meaningful results. By following a structured approach, you can transform raw, unstructured data into a clean, optimised foundation that enables AI models to deliver robust, high-performance results.

In this guide, we walk you through the essential steps of data preparation, providing you with a clear path to ensure your data is ready for AI development. Let's get started:

1 Data collection

The first step is gathering data relevant to your problem or project. AI models are only as good as the data they are trained on, so it is critical to ensure that the data is diverse, representative and high quality.

Key actions:

• Identify data sources: Collect data from internal sources (databases, spreadsheets, IoT devices) or external sources (APIs, web scraping, open datasets like Kaggle or UCI).
• Merge data: If data comes from multiple sources, combine it into a unified dataset for consistency.
• Ensure diversity: Include a variety of examples to avoid bias. This ensures the AI model generalizes well across different conditions.

Example: For a customer churn prediction model, data sources may include CRM systems, customer feedback and transaction histories.

2 Data cleaning

Raw data often contains errors, missing values, duplicates or inconsistencies. Data cleaning resolves these issues to produce a high-quality dataset.

Key actions:

• Handle missing values: Use techniques such as:
• • AI-based imputation.
  • Imputing values using statistical methods (mean, median, mode).
• Fix inconsistencies: Standardize formats for dates, text and numerical values. Correct typos and mismatches.
• Deduplicate data: Remove repeated rows or entries to prevent bias.

3 Data transformation

Once the data is cleaned, it must be transformed into a format that AI models can easily understand and process. This step includes scaling and encoding categorical variables so that AI models can recognise patterns effectively.

Key actions:

• Encoding categorical variables.
• Normalizing and scaling numerical data.
• Combining or splitting fields.

4 Data reduction

Deduplication and balancing reduce redundancies and prevent bias. This process helps prepare data for faster processing and better model accuracy.

Key actions:

• Deduplication: Remove identical rows or highly correlated features.
• Balancing the data: Address imbalanced datasets where one class dominates. Use techniques such as:
• • Oversampling (e.g., SMOTE) to generate more data for minority classes.
  • Undersampling to reduce data from the majority class.
• Dimensionality reduction: Use methods like Principal Component Analysis (PCA) or feature selection to reduce the number of features while retaining essential information.

5 Data Validation

Validate the data to ensure it meets set standards for quality, integrity and reliability before it’s fed into AI models. Statistical analysis can help verify the data’s consistency and suitability.

Key actions:

• Statistical validation: Perform checks to ensure consistency, such as analyzing means, distributions, and correlations.
• Integrity checks: Verify logical relationships (e.g., ages cannot be negative, dates must follow a timeline).
• Split the data: Divide the dataset into:
• • Training set (70-80%): Used to train the model.
  • Validation set (10-15%): Used to tune the model.
  • Test set (10-15%): Used to evaluate final performance.

6 Testing and Iterating

Once the dataset is prepared, test it with a baseline AI model to ensure it meets expectations. Use the model's performance metrics to iterate and improve the data preparation steps if necessary.

Key actions:

• Train a simple model on the prepared data.
• Evaluate performance metrics (e.g., accuracy, precision, recall, or RMSE for regression).
• Identify weaknesses in the data (e.g., insufficient features, poor scaling) and refine accordingly.

Example: If a model performs poorly due to imbalanced data, revisit the balancing techniques in the Data Reduction step.

Automating data preparation for AI

Automating data preparation can significantly improve the process by saving time and improving data quality. Here are a few strategies for automating data preparation:

Simplify data workflows with ETL Automation tools

ETL (Extract, Transform, Load) tools take the heavy lifting out of data preparation by automating key steps:

• Extraction: Data is pulled from multiple sources like databases, APIs, or files without manual intervention.
• Transformation: Predefined rules clean and restructure data to ensure consistency and compatibility with AI models.
• Loading: Prepared datasets are automatically sent to storage or pipelines for further processing.

By eliminating repetitive tasks, ETL tools save significant time and standardise data preparation across projects. Popular tools like Microsoft Azure Data Factory, AWS Glue, Talend, or cloud-native services can scale to meet growing AI demands.

Implement data quality with automated rules

Automating data quality checks helps identify and resolve issues before they compromise AI outputs. Key strategies include:

• Setting up validation rules to catch missing, incorrect or duplicate data.
• Monitoring for anomalies like extreme outliers or mismatched formats.
• Automating alerts to notify teams when thresholds for data quality are breached.

With automated integrity checks, teams can ensure their datasets are always accurate, reliable, and ready for AI model training.

Use AI to improve data cleaning

AI and machine learning algorithms can further automate data cleaning by identifying and correcting errors intelligently. These tools can:

• Detect patterns and suggest corrections for missing or incomplete data.
• Identify and remove outliers or inconsistencies that could distort model results.
• Adapt to changing data characteristics over time, making the cleaning process more robust.

By applying machine learning techniques to data cleaning, organisations can improve the accuracy and quality of large, complex datasets with minimal human intervention.

Automate consistency with data pipelines

Automated data pipelines ensure a continuous and repeatable flow of data from ingestion to preparation and storage. Benefits include:

• Consistency: Data is prepared the same way every time, reducing errors and variability.
• Reproducibility: Pipeline automation makes it easier to replicate results and maintain version control.
• Scalability: Pipelines can handle increasing volumes of data without manual adjustments.

Tools like Apache Spark, Azure Data Factory and Google Cloud Dataflow make it easier to automate and scale data pipelines for AI projects.

Common challenges in data preparation and solutions

Data preparation is a foundational step in building reliable AI systems, yet it often poses significant challenges for data teams. The growing scale, complexity and demands of AI projects have intensified the need for efficient and effective data management. Below, we discuss some of the most common challenges and actionable solutions.

1- Challenge: Data volume and variety: Managing vast and diverse datasets can be resource-intensive. 

Solution: Scalable storage solutions and cloud-based processing platforms can help handle large data volumes effectively.

2- Challenge: Data quality issues: Poor data quality (including incomplete, inconsistent, or inaccurate data) can undermine AI model accuracy and reliability. 

Solution: 

• Automate data cleaning and validation to identify and fix errors quickly.
• Remove duplicates and use data profiling tools to monitor data quality.

3- Challenge: Identifying and Mitigating Bias in Datasets: Biased datasets can lead to unfair AI predictions, impacting outcomes and trust in AI systems.

Solution: Regularly assess and adjust datasets to minimise bias, promoting fair and accurate outcomes.

4- Challenge: Privacy and security concerns: Safeguarding sensitive data during preparation is essential. 

Solution: 

• Apply data anonymisation and use role-based access control to restrict access.
• Use encryption for data at rest and in transit to safeguard information.

5- Challenge: Tool selection and integration: With many data preparation tools available, choosing the right ones can be challenging.

Solution: Evaluate tools based on compatibility with data sources, ease of integration and scalability.

6- Challenge: Keeping up with evolving requirements: AI models and data needs change over time. 

Solution: 

• Automate flexible data preparation pipelines that adapt to new requirements.
• Regularly review processes and incorporate the latest tools and techniques.

Claria uses Boomi's advanced, AI-driven platform to optimise and improve data preparation for AI applications. With Boomi’s scalable, cloud-native platform, Claria can handle complex data workflows from diverse sources, ensuring data is prepared for effective AI-driven insights. Boomi’s automated data quality management cleans, transforms and standardises data, minimising quality issues that often hinder AI success.

Why choose Boomi for data AI-Ready ?

Boomi offers an AI-driven platform that simplifies data preparation, enabling organisations to make the most of their AI investments. Boomi is considered an excellent choice for data preparation in AI for several compelling reasons, let’s have a closer look at how Boomi addresses common challenges:

Scalable architecture for diverse data

Boomi’s cloud-native platform handles large datasets from various sources, including on-premises systems, SaaS applications and cloud platforms. Its scalability supports robust data preparation workflows without compromising performance, ensuring datasets are ready for AI models.

Automated data quality management

Boomi guarantees high-quality data by automating essential tasks:

• Data cleansing: Eliminating duplicates, fixing inconsistencies, and handling missing values.
• Transformation: Converting raw data into AI-ready formats such as JSON, CSV, or tables.
• Validation: Ensuring accuracy and consistency to reduce the risk of data quality issues.

This automation saves time and minimises human error, ensuring reliable datasets for machine learning models.

Bias mitigation

Boomi supports the use of diverse datasets to reduce AI model bias, helping deliver fairer and more representative results. Its workflows enable:

• The combination of multiple data sources for improved diversity.
• Manual reviews and rigorous testing to promote fairness in AI outputs.

This approach ensures AI models are more equitable and better aligned with real-world scenarios.

Privacy and security

Boomi prioritises data privacy and security. Its AI processes operate on anonymised metadata rather than customer data, safeguarding sensitive information and adhering to data protection regulations. This ensures businesses can use AI capabilities without compromising security or compliance.

Perfect integration

Boomi’s low-code platform simplifies the integration and preparation of data by:

• Offering pre-built connectors to simplify data ingestion from disparate systems.
• Automating end-to-end data workflows, reducing complexity and speeding up AI adoption.

This ease of use accelerates the delivery of high-quality data to AI environments, making the process faster and more accessible.

Cutting-edge AI capabilities

Boomi incorporates generative AI features to improve data preparation and stay ahead of technological demands:

• Automatic generation of APIs and data models.
• Rapid adaptability to evolving AI needs, enabling organisations to innovate and scale effortlessly.

This allows businesses to remain competitive in an ever-changing AI landscape.

Boomi AI Agents: driving operational efficiency

Boomi’s AI agents improve automation, simplifying data preparation and integration:

• Boomi GPT: Uses natural language for task automation and orchestration.
• Boomi DesignGen: Automatically generates integration designs, mapping data and reusing connections to reduce costs.
• Boomi Pathfinder: Suggests optimisation steps for integration workflows.
• Boomi Scribe: Automates documentation, saving time for developers.
• Boomi DataDetective: Classifies sensitive information and monitors data for compliance.
• Boomi Answers: Provides rapid answers from a vast community knowledge base, speeding up problem-solving.

With Boomi, organisations can prepare data more efficiently, ensuring their AI models deliver accurate, fair and impactful outcomes.

Conclusion

Data preparation is critical for successful AI implementation, impacting everything from accuracy and cost efficiency to compliance and security. Ensuring that your data is properly collected, cleaned and transformed will enable your AI models to produce reliable and valuable insights, maximising the return on your AI investments.

With the right tools, such as Boomi’s robust data preparation platform, companies can simplify their processes and overcome common data challenges. This readiness will pave the way for AI-driven innovation, helping organisations to stay competitive!

Does your company want to start the AI journey? Do not hesitate to contact us, one of our experts will be delighted to help you out! 

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Amanda Garrido Vallès

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