What Is AI-Ready Data & Do You Already Have It?

Key Takeaways

• Structured data often exists in multiple locations, so consolidating it ensures easy access and processing for AI applications.
• Most organizational data is unstructured, often requiring special tools like Unstructured AI to transform it into an AI-ready format.
• Maintaining and processing data is especially crucial for large enterprises that handle vast amounts of information needed for AI applications.
• Properly managing and governing data ensures compliance, security, and ethical use, which are critical for AI success and avoiding regulatory issues.
• AI success relies on data readiness. Data must be properly prepared to meet AI standards.

Different Data Types

When businesses first start considering implementing AI, they often wonder what type of data they need. But that is a wrong question to ask. 

You need to start by asking yourself what type of data you already have and where it is stored. Based on those answers, you will better understand how to get your data AI-ready.

Let's review different data types before we help you determine your data readiness.

Structured Data

Structured data refers to organized information stored in relational databases, which use tables, rows, and columns to enforce a clear schema. Common examples include:

• order records 
• inventory lists
• financial data
• customer information

Structured data is typically easy to query and analyze, making it a strong foundation for many AI applications.

However, one challenge is consolidation. Structured data is often siloed across various databases or locations, making it necessary to migrate and replicate the data to ensure availability and speed for AI processes.

Unstructured Data

Unstructured data comprises most of an organization's information, including emails, PDFs, media files, web forms, etc. 

This type of data doesn't fit neatly into databases, making it harder to process and query. Around 80% of all enterprise data is unstructured, posing significant challenges for AI readiness.

To make unstructured data AI-ready, it must be standardized and consolidated into a searchable format. 

One way to do so is to use tools like Unstructured AI which convert difficult-to-use document formats into structured, AI-ready data. It converts formats such as PDFs, PowerPoints, Excel files, CSVs, HTML, DOC/DOCx, turning text, charts, and numerical tables into structured outputs.

Unstructured AI by Multimodal is a sophisticated Extract, Transform, Load (ETL) layer designed to process complex, unstructured document formats for RAG architectures or downstream Generative AI applications.

One key feature is its ability to "chunk" text—organizing it into smaller, semantically relevant pieces that can be converted into embeddings. Embeddings are numerical representations of text that AI models can interpret and use in downstream tasks, such as generating answers in Conversational AI systems or retrieving context from vector databases. 

Unstructured AI also processes tables and charts, pulling them into database-friendly formats and adding semantic tags to images for easier AI retrieval.

Another feature is hierarchical text retention. For example, it can extract nested text from a PDF, ignoring sidebars or footers, while pulling out chart information and annotating it with semantic details for downstream use.

All the information is clearly structured, formatted, and pulled out in JSON format, tables and charts, and rich metadata. 

Unstructured AI also works to make our other AI Agents, such as Document AI, Conversational AI, and Database AI, more powerful and easy to use.

Semi-Structured Data

Semi-structured data falls between structured and unstructured data. It doesn't fit neatly into a traditional database format but still contains markers or tags that give it some organizational structure. Examples include JSON, XML files, and certain types of NoSQL databases. 

While not as rigid as relational data, semi-structured data is more flexible and easier to process than fully unstructured data. 

What Is AI-Ready Data?

AI-ready data consists of well-structured, accurate, and properly governed datasets, making them suitable for training and deploying AI models. In essence, AI-ready data allows machine learning algorithms to interpret, analyze, and use information without requiring extensive preparation or cleaning. 

AI-ready data is well-governed, secure, unbiased, accurate, and high-quality.

At its core, AI-ready data should meet three key criteria:

1. Interpretable and usable: Data must be structured to allow data scientists, AI vendors, or large language models (LLMs) to interpret it and generate insights easily. This includes ensuring consistency in how data is labeled, categorized, and formatted.
2. Simple to query and optimize for feature engineering: Data should require minimal preprocessing, making it easier for data professionals to extract features, generate insights, and train AI models. Time-consuming tasks such as data cleaning or dealing with fragmented data sources should be minimized.
3. Accurate and reliable: High-quality data leads to accurate predictions. Poor quality data, on the other hand, introduces bias and errors that can hinder AI success. Ensuring data is free from inaccuracies and noise is critical for reliable AI outcomes.

Why Is Having AI-Ready Data Important?

Data readiness for AI ensures companies can leverage AI to its fullest potential, providing a competitive advantage through more informed decisions. The success of any AI initiative depends heavily on the data quality used to train the models. 

Quality data leads to quality results. Accurate models generate reliable insights, minimize bias, and achieve meaningful outcomes in AI-driven processes.  

There are a few more reasons why having AI-ready data is important:

• Minimizing risk and ensuring compliance: Properly ethically governed, AI-ready data helps organizations comply with legal and ethical standards. 

In industries like banking, healthcare, or insurance, where data security and privacy are paramount, ensuring data readiness is key to staying compliant with regulations and protecting sensitive information.

• Fostering innovation and competitive advantage: AI success allows businesses to automate processes, enhance decision-making, and offer better products and services. 

Companies with AI-ready data can stay ahead of competitors by innovating faster, making better predictions, and optimizing processes.

On the flip side, using data that isn't ready for AI can spell disaster. Inaccurate, incomplete, or poorly prepared data creates bias-enriched models, inaccurate outputs, or hallucinations, especially in applications like retrieval augmented generation (RAG). Poor data quality in RAG can lead to flawed, unreliable results that derail entire projects. 

This is why we always advise businesses to prioritize data readiness. Ensuring robust data prevents costly pitfalls and guarantees your AI systems deliver accurate and trustworthy insights across all applications.

You should also check out our other articles on training data, such as How to Train Generative AI Using Your Company’s Data and How to Train an LLM With Custom Data.

The Challenges of Getting Your Data AI-Ready

Achieving AI data readiness is no easy task, and companies often face multiple challenges when preparing their data for AI adoption. Traditional data cleaning and preprocessing is error-prone and time-consuming, with an estimate of 80% of the time spent on data cleaning while developing GenAI applications.

Other challenges include:

• Data silos: Many organizations store data in silos across different departments or systems. This creates disjointed datasets that are difficult to access, integrate, or use for AI purposes. 
• Data quality & accuracy: Low-quality or inaccurate data can negatively impact AI models. The same applies to your data timeliness because outdated data loses value.
• Unstructured data: A significant portion of data in organizations is unstructured, such as emails, PDFs, and images. 
• Scaling performance: As data capacity grows, performance doesn’t always scale accordingly, creating bottlenecks in AI processes.
• Data governance & compliance: Meeting industry regulations and governing data ethically presents a significant challenge for many organizations. 
• Storage complexity: Copying data through multiple tiers complicates management and reduces efficiency.
• Resource demands: AI models require significant storage, power, and space, creating challenges in maintaining efficiency.
• Data literacy: Many organizations struggle with data comprehension, meaning that key decision-makers may not fully understand how to assess or prepare data for AI readiness. This lack of knowledge can slow down AI initiatives or lead to misguided efforts.

Is Your Data FAIR?

Ensuring your data is FAIR—Findable, Accessible, Interoperable, and Reusable—also plays a critical role in AI readiness. These four foundational principles help organizations manage their data more efficiently, making it easier for AI models to utilize the information effectively.

1. Findability: Data should be easy to locate, with clear metadata and unique identifiers to help users and AI systems find it quickly.
2. Accessibility: Once found, data must be available for use, ideally through well-defined protocols that allow seamless access while maintaining security.
3. Interoperability: Data should work across different systems, formats, and technologies, ensuring compatibility with other datasets and AI tools.
4. Reusability: Data should be well-documented and governed adequately so it can be reused in various contexts, supporting future AI projects without the need for extensive reprocessing.

Many current FAIR data practices offer a strong foundation but organizations can further improve them by incorporating Responsible AI principles such as fairness, transparency, accountability, and ethical use. 

Step-By-Step: Achieve Data Readiness for AI

Getting your data AI-ready requires careful planning and execution. Let’s summarize everything you need to do to have your data properly prepared:

Step 1: Assess Your Current Data State

The first step in preparing your data for AI is evaluating your current data sources, formats, and structures. Identify where data is stored, its quality, and any potential gaps. This assessment helps pinpoint what data is missing, is of poor quality, or is inaccessible.

Step 2: Break Down Data Silos

Data silos limit the availability of valuable information that could benefit AI models. Begin integrating data across different departments or systems to create a unified dataset. Make sure that this data is accessible to data scientists and AI professionals.

Step 3: Cleanse and Prepare Data

After consolidating your data, clean it up by removing duplicates, filling in missing values, and ensuring accuracy. Unreliable data can lead to incorrect predictions, so data cleansing is a critical step in data readiness for AI.

Step 4: Convert Unstructured Data to Structured Formats

We mentioned earlier that a large volume of business data remains unstructured, and without structure, it’s difficult for AI models to use this data effectively. 

Utilize tools like Unstructured AI because it is secure (SOC 2 Type 2 compliant), easy to use, supports 20+ file types, has an API structure, and doesn’t need custom coding while leveraging state-of-the-art AI models.

Step 5: Implement Data Governance Frameworks

Establish a strong data governance framework to ensure compliance with regulatory standards and ethical data management. Data governance includes defining data ownership, tracking data lineage, and maintaining secure, bias-free, and accurate datasets.

Step 6: Ensure Data Literacy Across the Organization

Encourage data understanding among business leaders and teams. When decision-makers understand the importance of high-quality data and the process of preparing data for AI, they can make more informed decisions that drive AI success.

Is Your Data Ready for AI? A Checklist

Consider the following checklist to determine whether your organization’s data is ready for AI. If you can answer "yes" to most of these questions, your data is likely well on its way to being AI-ready.

We cannot overstate the importance of having your data AI-ready, as it is the foundation of successful AI initiatives. Without it, businesses risk making poor decisions based on inaccurate models. 

With AI-ready data, scaling becomes smoother as normalized data simplifies integration. It also reduces costs by minimizing the need for complex tooling and streamlines compliance processes, making it easier to meet regulatory requirements.

Organizations can transform their datasets into valuable assets that power their AI success by addressing the challenges we listed and following the checklist.

Get Your Data AI-Ready with Unstructured AI

Ready to transform your data and explore AI use cases? Schedule a free 30-minute call with our team! We’ll show you how Unstructured AI can convert complex document formats and help get your data AI-ready for seamless scaling and integration.

During the call, we’ll discuss your specific needs, demonstrate how Unstructured AI can streamline your AI readiness, and guide you through tailored AI solutions that fit your business.

FAQs

What is the purpose of getting data AI-ready?

The purpose of AI-ready data is to ensure that machine learning models can easily use it to generate accurate insights and predictions. It minimizes the need for data cleansing or restructuring during AI deployment.

What are the three types of data in AI?

The three types of data in AI are structured data, which is organized in a clear format like databases; unstructured data, which includes documents, images, and videos that lack a predefined structure; and semi-structured data, which contains some organizational tags or markers, like JSON or XML files.

How much data is enough for AI?

The amount of data needed depends on the complexity of the AI model and the use case. Typically, more data improves model accuracy, but it must be of high quality and properly structured to be effective.

What is AI data readiness?

AI data readiness refers to preparing data for use in AI models, ensuring it is accurate, accessible, and properly structured for training and deployment.

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