Data Preparation for AI

Is your business data ready for AI? Unfortunately, for most companies, the answer is no. Research shows that 85% of all AI initiatives will fail due to inadequate data preparation. Without good inputs, your artificial intelligence investments won’t generate a positive return. Plus, with many organizations spending millions of dollars on AI, failure often means staggering financial losses. To prevent this and achieve successful AI integration and implementation, it’s essential to understand how to optimally prepare your data for AI. Read on to get the facts you need to ensure your AI investments create real value.

What Is Data Preparation for AI?

Data preparation means collecting, cleaning, labeling, and transforming data to make it ready for AI model training and analysis. This is a crucial step to ensure the accuracy and effectiveness of AI systems. This process is achieved through data pipelines that pull data from multiple sources, prepare it for analysis, and then send it to the machine learning algorithms.

Why Is Data Preparation for AI Important?

Data preparation for AI is crucial as it forms the foundation for accurate, interpretable, and efficient AI systems that drive organizational success. Even if you are relying on models that have already been trained externally, it’s critical to ensure that the data you feed it for analysis is clean and accurate to get useful outcomes. Bad data leads to poor AI decision-making, creating a disaster for your organization.

Here’s a closer look at why data preparation matters:

• Improves Accurate Outcomes: The “garbage in, garbage out” cliché certainly applies to AIs. Even the most sophisticated model can’t do a good job if it’s trained on bad data. Data prep ensures AI tools are only using the highest-quality, relevant organizational data.
• Enhances interpretability: Knowing your AI is using well-prepared data enables greater understanding, trust, and explanation of AI model decisions.
• Reduces costs: Good data decreases compute resource consumption by reducing the need for repeat prompts due to inaccurate answers.
• Leads to faster adoption: Data preparation accelerates the process of reliable AI tools being adopted and integrated into employee workflows.
• Enables Cross-functional Collaboration: Properly prepared data can be more easily shared and understood across different teams, fostering more effective collaboration in AI projects.
• Minimizes Technical Debt: Good preparation practices help prevent the accumulation of poor-quality data or inconsistent formats, reducing future cleanup efforts.
• Ensures Compliance and Ethics: Proper data preparation helps maintain regulatory compliance and ethical standards by filtering sensitive information and reducing biases in the dataset.
• Strengthens Data Security: The preparation process provides an opportunity to implement data security measures, protecting sensitive information from unauthorized access or breaches.
• Supports Customization: Well-prepared data allows for easier fine-tuning of pre-trained models to specific organizational needs and contexts.

A Step-by-Step Guide on How to Prepare Data for AI

Whether you’re a data scientist, AI engineer, or executive looking to leverage AI for your company, a structural approach to preparing their data inputs is crucial for developing accurate and reliable AI models.

Here’s our step-by-step guide to preparing data for AI:

1. Data Collection: AI preparation always begins with gathering data from relevant sources. Ensuring data diversity and representativeness is crucial for building reliable AI models.
2. Data Cleaning: Cleaning data prevents skewed analysis and costly consequences. This process involves resolving missing values, removing outliers, and addressing inconsistencies so that the dataset is accurate and reliable.
3. Data Transformation: Data that has been cleaned must then be normalized in a process known as data transformation. At this point, information from various sources is standardized to ensure that all numerical values are on the same scale. Categorical variables must also be encoded to ensure the machine learning model’s ability to recognize patterns and make accurate predictions.
4. Data Reduction: Deduplication is essential for eliminating redundant records, which can distort analysis and slow processing. Balancing datasets is another crucial data preparation process and is key for preventing biases in the model’s classifications.
5. Data Validation: Finally, before the data is sent to the machine learning algorithm, it must be verified that it is valid and meets the specified standards. Statistical analysis is used to validate the integrity and consistency of the data, ensuring that it is capable of supporting reliable and trustworthy outcomes.

How to Automate Data Preparation for AI

One of the key ways organizations are speeding up the data preparation process is by using automation. Automated data preparation saves time and money and leads to more accurate model development.

Here are the key strategies to use to automate data preparation:

1. Use ETL Tools: ETL tools can automatically extract, transform, and load data to prepare it for AI models. When using these solutions, it’s important to carefully set up the transformation rules to be consistent with your model’s requirements.
2. Implement Data Quality Rules: It’s also essential to define data quality standards. Implement automated checks for data integrity and consistency, and create alerts for data quality issues to quickly address bad data before it impacts your AI model.
3. Use Machine Learning for Data Preparation: Use machine learning algorithms to automatically detect anomalies within your datasets, such as outliers or unexpected patterns. This proactive approach helps in cleaning and refining data, making it more suitable for AI training and reducing the likelihood of introducing biases or inaccuracies into your models.
4. Establish Data Pipelines: A crucial part of automating data preparation is establishing data pipelines. When properly implemented, data pipelines can automate the entire data preparation workflow, from ingestion to transformation and validation. With these workflows, implementing version control allows you to track changes in both the data and preparation processes, ensuring reproducibility and the ability to roll back to previous versions if necessary.

Challenges in Data Preparation for AI

Data preparation is a critical step in the AI development process, serving as the foundation upon which successful models are built. However, it brings complications that organizations must navigate to ensure optimal AI performance.

Here are six key challenges and practical solutions to address them effectively:

1. Data volume and variety: Handling large and diverse datasets can be overwhelming and resource-intensive. The solution is to use scalable storage solutions and cloud-based processing platforms to manage the vast amounts of data.
2. Data quality issues: Inconsistent, partial, or inaccurate data can degrade model performance. To prevent this, rigorous data cleaning and validation processes must be implemented.
3. Bias identification and mitigation: Bias in data can lead to unfair or skewed AI model outputs. This is why companies must regularly analyze and adjust datasets to remove or balance biased inputs, ensuring fairness and accuracy in AI models.
4. Privacy and security concerns: Protecting sensitive information during data preparation is critical but problematic. Apply encryption, anonymization and secure access controls to safeguard data throughout the preparation process.
5. Tool selection and integration: Choosing and integrating the right data preparation tools can be complex. There are so many options available. To cut through the noise, make sure to evaluate tools based on compatibility with your unique data sources and AI needs. Opt for those that offer seamless integration and scalability.
6. Keeping up with evolving AI requirements: Evolving AI models demand continual adaptation of data preparation techniques. It’s important to stay up to date with the latest AI trends and techniques and regularly review and adjust your data preparation processes to meet new requirements.

How Boomi Enables Robust Data Preparation for AI

Boomi is an AI trailblazer, enabling strong data preparation for effective AI implementations that create real value. Our platform streamlines data integration, ensures data quality, and provides enhanced data security. Here’s how you can overcome the issues highlighted above with the power of Boomi:

• Handle Large and Diverse Datasets: Boomi’s scalable, cloud-native architecture is designed to efficiently manage large volumes of data from a wide range of sources. With an extensive library of connectors, Boomi processes diverse datasets from various applications, databases, and systems, enabling comprehensive data preparation for AI.
• Ensure Data Quality: Automate the entire data cleaning, transformation, and preparation to ensure high-quality input for AI models with the power of Boomi. Its advanced data management tools prioritize accuracy and consistency, addressing data quality issues before they impact AI performance.
• Mitigate Bias: Boomi actively works to reduce bias in AI models by enabling the use of diverse datasets that reflect a broad spectrum of user demographics and scenarios. The platform also supports manual model reviews and rigorous testing to identify and correct any biases, promoting fairness in AI outcomes.
• Provide Privacy and Security: Boomi AI uses only anonymized metadata, not actual customer data, to build AI models while keeping customer data private. Our security measures protect data and AI models through encryption and secure access.
• Allow Seamless Integration: Boomi offers a unified platform with a low-code, accessible interface. Discover simple data integration and preparation, and incorporate AI into your workflows with ease.
• Stays on the Cutting Edge: Boomi AI represents an evolution to an AI-first approach, with generative AI capabilities to automatically build integrations, APIs, data models, and more from natural language commands. Unlock faster adaptation to new AI use cases and requirements with Boomi.

Introducing Boomi AI Agents

Boomi is committed to supporting organizations in the age of AI. That’s why we’re now offering several AI agents to users on our enterprise plan. These AI agents enhance integration and automation by autonomously performing tasks and making decisions, improving operational efficiency. This is what Boomi’s agents can do for you:

1. Boomi GPT: Boomi GPT enables developers to use natural language to orchestrate Boomi’s agents and complete automation or integration tasks.
2. Boomi DesignGen: With Boomi DesignGen, you can automatically generate integration designs with simple commands. Boomi DesignGen also provides data mapping based on patterns and allows reuse of existing connections to reduce costs.
3. Boomi Pathfinder: Never lose your way with Boomi Pathfinder. Pathfinder is a bot that provides suggestions based on the next steps to optimize your integrations. Get access to patented recommendations directly within the Boomi Process Canvas.
4. Boomi Scribe: Once you’ve built (or generated) your integration processes, get out of having to write up all the documentation manually and use Boomi Scribe. Scribe generates documentation automatically, improving productivity and freeing up time for developers.
5. Boomi DataDetective: Your data is under attack from all directions. Protect it and prevent compliance violations with Boomi DataDetective. The DataDetective bot classifies personally identifiable information (PII) and tracks data movement for compliance and provides a filterable dashboard for monitoring.
6. Boomi Answers: Get relevant answers and insights about the Boomi platform with Boomi Answers. The Answers bot instantly extracts the insights you need from a community of over 250K members, saving time on problem-solving.

Boomi Is the Ideal Solution for Data Preparation in AI

AI is incredibly powerful, but companies can’t take full advantage of it without robust data preparation processes. Whether you’re building and training your own model, or feeding your data into a pre-trained system, the AI output is only going to be as reliable as your input.

Automation can accelerate and enhance the quality of data cleaning, transformation, and integration.

Boomi DataHub is the comprehensive solution that you need to streamline your AI data preparation.

Among its many features, Boomi DataHub allows you to:

• Continuously validate, enrich, and steward data for AI confidence.
• Quickly find correct master data for AI with DataHub’s fast search engine.
• Use Boomi Integration and DataHub for real-time, accurate data flow.
• Employ golden records to drive trusted AI data.
• Establish a centralized, consistent data source to reduce bias.
• Support AI data governance with a centralized approval process.
• Use Boomi DataHub authentication to verify user identities and ensure proper access.

Learn more about how to get your data AI-ready with Boomi DataHub.