AI Ready Data: Enterprise AI Readiness and Data Preparedness

Having AI-ready data is critical for building AI applications that are not only functional but also scalable and reliable. Traditionally, machine learning (ML) has focused on classifying, predicting, or analyzing data based on predefined features. Today, however, the AI space has evolved with the rise of generative AI. Organizations looking to create AI-based applications will find the real value in using their proprietary data with these models. To achieve this, they must ensure that the data is AI-ready.

AI-ready data is prepared and structured to allow machine learning algorithms or generative AI models to extract insights effectively. This includes handling various data types: structured, unstructured, and semi-structured, each requiring different preparation methods. Being AI-ready also includes ensuring that the architecture, metadata, and storage solutions are aligned to support generative use cases. Achieving this state requires a combination of data quality, governance, and architecture.

This article covers the critical steps toward having AI-ready data and guides organizations in bringing in AI tools.

Summary of best practices for AI-ready data

This table provides the best practices to improve your organization’s AI data readiness.

Prepare and structure data to make it AI-ready

AI-ready data must be well-structured, clean, and aligned with the specific needs of the AI applications you intend to build. This process includes dealing with raw data, managing various data types, ensuring high data quality, and selecting the right storage systems for your needs.

Data types

The image below shows the different types of raw data: structured, unstructured, and semi-structured.

Structured data

This type of data has a clearly defined data model. Data can be organized in a fixed format, like rows and columns (e.g., relational databases).

You must ensure this data is clean, consistent, and well-labeled for your AI algorithms. For example, one data preparation activity for structured data is binary-encoding categorical columns, as most traditional machine learning models work with numerical features. Take a categorical feature such as “cost_of_living,” which might have two values: “high” and “low.” This feature can be binary encoded as “1” for “high” and “0” for “low” to transform it into a format that machine learning models can process effectively.

Semi-structured data

This data has a loosely defined data model. It doesn’t fit perfectly into the rows and columns of structured data, but it still contains some organizational properties. Examples include JSON, XML files, log files, and reports that contain structured elements like forms and tables.

A major challenge with semi-structured data is deciding which pieces of data need to be extracted for the AI-based application. For instance, a JSON file might contain nested and complex data fields, requiring a hybrid data pipeline to capture both structured and unstructured elements effectively.

Unstructured data

This data type lacks a clearly defined data model and is difficult to search. It includes reports, images, emails, social media posts, audio files, and videos. Also, raw unstructured data might not have associated metadata, making it harder to organize and track without further preparation.

For example, to build a chatbot that minimizes hallucination, you can connect it to a repository of PDFs as a knowledge base. This involves first projecting the textual data into vectors and storing them in a vector database. Then, when querying the chatbot, you retrieve the most semantically related vectors based on the query and use these results to guide the LLM in generating an accurate response.

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Data storage

For unstructured data, data lakes are the most appropriate option. They allow you to store vast amounts of raw data in its native format without worrying about predefined schemas. Tools like AWS S3 are commonly used for these purposes.

Databases such as SQL or NoSQL are better suited for structured data.

Hybrid systems work well for semi-structured data, where combining a data lake with a database system is a good approach. An example would be using ElasticSearch or MongoDB to store semi-structured data alongside a data lake solution like Delta Lake or Apache Hudi.

Remember that after you select a storage solution, you must ensure that it is scalable to handle the growth of your AI workloads over time.

Metadata

You can create metadata to tag datasets with information about their source, relevance, and usability in specific AI models. This helps improve data discoverability, tracking, and governance. It also ensures traceability, allowing you to trace back model outcomes to the specific datasets used, which is critical for debugging or improving models over time.

Metadata is essential for LLM-based applications interacting with structured data to enhance the effectiveness of LLM prompt engines. For example, text-to-SQL applications must map a user’s prompt into an SQL query. Interpreting the user’s prompt without ambiguity can be challenging without metadata describing the tables and columns. The models will better understand and work with tables and columns when metadata has more descriptive information. This metadata is usually captured within a semantic layer, but a fixed semantic layer is not enough for LLM-based applications. The latest LLM-based applications (like WisdomAI) extend the semantic layer into a context layer. The context layer is learned by mining and analyzing enterprise-specific content so that the LLM-based application can interpret jargon specific to your organization.

Align the AI architecture

It is critical to align your AI architecture with the type of data you are working with while also considering the specific approach you want to use: generative AI or traditional ML. In traditional ML, labeled datasets were used to train ML models, classify new information, or predict future outcomes. Generative AI extends these capabilities to create summaries, find complex correlations, or generate new content. The diagram below shows two data pipelines that use generative AI. Pre-trained LLMs are used in both pipelines, and the pipeline is adapted to the different data types.  

For generative AI applications, such as chatbots or content generation tools, retrieval-augmented generation (RAG) is an effective architecture, particularly when working with LLMs that need external data sources for better responses and especially so when dealing with unstructured data. In RAG-style architecture, models retrieve relevant information from a knowledge base or external source to improve the generation process. This approach is highly effective for tasks like question-answering systems and chatbots. When implementing RAG, key decisions include how to chunk large documents (determining size and overlap), choosing the right embedding model and size, and selecting the appropriate LLM for the task.

Meanwhile, if you are working with structured data in traditional ML applications, you should prioritize architectures that can efficiently handle features and transformations because structured data often requires extensive feature engineering to maximize model performance. For instance, ML models can handle structured datasets, making them ideal for tasks like financial forecasting, recommendation systems, and customer segmentation. Generative AI can interact with structured data by turning natural language queries into SQL. For more information, see the text-to-SQL article.

For semi-structured data, hybrid architectures that integrate structured and unstructured data processing methods offer a viable option. A hybrid architecture can combine the traditional keyword-based search for structured elements, like filtering records based on specific fields, with a semantic search for unstructured parts, such as texts from customer feedback. You can also use embedding models to convert all texts into vectors or use multi-modal neural networks that simultaneously handle text and structured data. This flexibility allows models to parse, transform, and process semi-structured data while maintaining the high performance needed for fraud detection, log analysis, and complex document processing.

For organizations that don’t want to build an AI-based application from scratch, enterprise-grade solutions can remove the burden by providing AI-driven insights from your data.

AI readiness checklist

Implementing or developing AI applications within your organization shares similarities with non-AI applications. However, there are AI-specific considerations that need to be understood.

System security

The AI system should comply with traditional platform security and privacy requirements. For example, a generative AI application vendor should be able to demonstrate SOC 2 compliance, and compliance with data privacy regulations like GDPR, CCPA, and the EU AI Act. The cloud computing shared responsibility model sets out what responsibilities transfer to the cloud provider and what responsibility is always retained by the customer. Develop a similar responsibility matrix for your AI systems and your AI vendors.

Data security

Not all data should be available to every individual or system within an organization. The AI-based application must honor existing role-based and resourced-based access controls.

For example, a user with a finance role should be able to access revenue data via the AI-based application, but a user from the human resources team should not. In a text-to-SQL system, a user with a finance role can execute SQL queries against tables containing financial data.

Resource-based access controls will limit the AI-based application to certain resources. For example, the AI application should honor row-level access controls to structured data and only access unstructured to which a user is entitled.

Data usage and protection

Understanding how the AI-based application will use your organization’s data is imperative. If you use an AI vendor, will your data be used to train a model? If so, what protection is in place to stop another customer from benefitting from your data?

How is your data stored within the system, and how is it protected from third-party access?

Evaluation, monitoring, and auditing

AI-based applications present new challenges to engineering teams because LLMs can produce unexpected outputs.

An LLM evaluation framework measures the performance of AI-based applications. An LLM evaluation dataset consists of test cases used to measure the application's performance. Different scoring methods can be used to evaluate whether the application passes or fails, depending on the test case. For example, a text-to-SQL system will need test cases that measure whether the generated SQL queries have valid SQL syntax and whether the queries yield correct results. Whenever changes are made to the AI-based application, an evaluation framework provides a standardized way to test how the changes have impacted performance.

Auditing is a safeguard against reliability risks associated with AI-based applications. It is important to implement guardrails to prevent the misuse of AI-based applications that have access to company data. These could include monitoring access, logging user activity, and setting strict limits on how AI systems interact with sensitive data. Within the AI application itself, guardrails control the acceptable inputs and outputs, and there are a variety of different guardrails that you need to consider. An auditing framework helps identify and fix security vulnerabilities.

Observability and monitoring are both critical for maintaining the performance of AI-based applications. Feedback mechanisms within your AI-based application can help uncover performance issues and unexpected outputs. An AI-based application needs to provide traceability of the inputs and outputs. For example, WisdomAI captures the user’s question, the generated SQL query, and the accessed data.

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Last thoughts on AI Ready

Ultimately, achieving AI-ready data comes down to ensuring high data quality, aligning the right architecture with your data types, and implementing strict governance to maintain security and compliance. AI readiness is a continuous process that requires ongoing efforts and adaptation.

Building an end-to-end business intelligence solution is resource-intensive, especially at the enterprise level—platforms like WisdomAI help streamline the implementation of AI-powered business intelligence. WisdomAI uses customized LLMs to translate user prompts into SQL queries, integrates metadata to resolve ambiguities, and generates tailored visualizations.