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Every business wants to use artificial intelligence for its operations, but most forget to consider the most important ingredient that makes the technology effective—data. Unless it’s only for basic, out-of-the-box functions, your AI tool requires highly relevant, business-specific data.
And, this data must be AI-ready.
Of course, we first need to understand what ‘data ready for AI’ really means.
What Is AI-Ready Data?
AI-ready data, as the name suggests, is data that can be used by an AI model. Think of it in terms of languages. No matter how grammatically correct a piece of text is, if it’s in a language you don’t understand, it won’t mean anything to you.
A computer—which is essentially what the model is—cannot understand human language. It doesn’t matter if you and I can understand what the information means; it has to be in a format the machine can understand.
The characteristics of AI-ready data are:
Clean
When you clean your data, you remove any mistakes that might have crept in. You also look for duplication and irrelevant information that could skew your AI model. This helps it focus on information that matters instead of trying to work through noise or inaccuracies.
Consistent
For a person, minor inconsistencies in the format don’t matter, but for AI applications, they can significantly affect the data collection process. For example, we know “Doe, John” and “John Doe” are both the same name. However, an AI model requires a standardized format to process and interpret data accurately.
Relevant
Too much noise—or excessive information—can make processing inefficient for anyone, even an AI model. By training the model with only relevant data, you reduce complexity and help it learn better and faster.
Labeled
With unstructured data, labeling the relevant data points helps your model learn patterns and make better predictions. For example, understanding “spam” from “not spam” in emails, or “dog” or “cat” in images can provide more semantic understanding.
Accessible
Whether your data is stored in databases, cloud storage, or connected with APIs, it should be easy to retrieve and process. It should also be in a format that’s easy to read—JSON, CSV, or SQL—so it doesn’t require extensive extraction or transformation efforts.
Compliant
All data must comply with security and privacy regulations, and data for AI is no exception. The most important part of compliance is to get user consent for their data to be used for AI training. Once you have that, any sensitive data must be anonymized, with all personal information encrypted.
Prepared
Consistent and clean data can be used for any purpose, including reporting and analytics. For it to be AI-ready, it must be processed and prepared for use. This includes scaling numerical features, encoding categorical variables, normalizing datasets, etc.
Unfortunately, getting the data to a state where it can easily be used by your model takes a lot of time—according to a popular joke among data scientists, it takes 80% of the total project time. In reality, it’s more likely to be 50%–70%, but even that’s still quite high.
So, why go through all this effort? Let’s find out why AI-readiness is so important.
Why Data Readiness Is Important for AI Adoption
The data you feed your AI model directly impacts its performance and accuracy, which in turn affects its reliability. If the data isn’t AI-ready, your AI systems will struggle to learn effectively, which might result in inaccurate predictions, biased outputs, and inefficient processes, , undermining the effectiveness of your AI algorithms.
When you use AI-ready data to prepare your model, you enjoy:
Model Accuracy
When your model has been trained on high-quality data, it becomes better at identifying meaningful patterns and making accurate predictions.
Efficiency
Without unnecessary noise and not having to parse information from inconsistent formats, your model uses less computational resources and requires less processing time.
Scalability
If your data is already AI-ready, it integrates more smoothly with any future AI projects and system updates.
Compliance and Trust
If you process and use data according to privacy and security standards, you are less likely to face legal troubles. Additionally, it builds trust with your consumers.
Cost-Effectiveness
It is always cheaper to start well than to try and fix problems later. If you begin your project with data that is ready for AI processing, you won’t face model rework or costly reprocessing later.
So, now, the question is…
How Do You Make Your Data AI-Ready?
Build a Data-Driven Culture
We’ve already established that a successful AI model needs high-quality data. However, for data to be effective, it needs to be valued and used for decision-making across the organization. Instead of relying on intuition, departments should be looking at insights gained from gathered data and making decisions based on them.
When you have a culture that sees data as the source of truth, you’ll find it easier to effectively implement AI solutions at scale and drive business outcomes.
Assess and Define AI Use Cases
As mentioned earlier, AI-ready data must be relevant. That means you must first define what you want your AI systems for and then understand the data requirements for these use cases, especially in relation to machine learning.
For example, predictive analytics requires different data types from generative AI. Once you know the data volume, diversity, and types required for your purposes, you can collect and collate accordingly.
Leverage a Data Management Platform
A data management platform can help you centralize, organize, and govern your data. It gives you more visibility into where and how data is stored and how it’s being used. This allows you to make it easily accessible and retrievable.
Modern cloud-based data warehouses or lakes also allow you to avoid duplication and multiple stores of the same data. You can also leverage lakehouses to store different types of data—structured and unstructured.
Clean and Organize Data
After you’ve collected your data, you can start the process of checking it for errors and removing duplication. It’s also important to check for any inconsistencies that might affect your results.
The cleaned data must be organized, which means standardizing the formats across the board. You may also need to apply labels, which again must be consistent. To make the data easy to retrieve, it should be classified logically.
Your data must also be free of bias as this can lead to a skewed AI model. For example, if historical data shows hiring trends leaning towards a certain group, an AI model trained on this data might perpetuate this trend.
Enrich Your Data
Data enrichment is the practice of adding additional information and attributes to give more context. This makes the data more comprehensive and valuable. This might mean supplementing the data with internal or external sources, such as that provided by third-vendors or your internal processes and resources.
Manage Data Security and Governance
To keep sensitive information safe from unauthorized access—both from outside and inside your organization—you need strategies like role-based access control (RBAC), encryption, and data anonymization. A data management platform would offer tools to keep your data secure using these methods, and also offer features like automated data pipelines and real-time processing to improve efficiency.
You can also use it to monitor and observe the quality of your data. This can help you identify any anomalies in real time. You can also use it to track data lineages so you can see where a piece of information originated and how it transformed. Active and ongoing monitoring will also alert you to stale or incomplete data.
Infuse Data With Semantic Meaning
While cleaning and enriching data can make it more usable, giving it additional context can make it more meaningful. This can include defining data sets, explaining relationships between data points with metadata, and providing a structure that helps the AI understand dependencies during tasks.
For example, a “customer” might be someone who has made a purchase from you, but “active customer” only applies to someone who bought a product in the last 30 days. Linking “customer ID” to “purchase history” can help the AI make better predictions and suggestions. Defining a “completed order” to include “payment received” and “shipment processed” helps it understand your business rules.
You may also want to define semantic relationships for complex AI uses, such as hierarchical structures and temporal dependencies. These relationships often depend on metadata, such as timestamps and origin identifiers that can be valuable in identifying trends and tracing anomalies back to their source.
Develop a Talent Strategy
It’s not enough to just have the data; you also need professionals who know how to manage and handle it. You need to hire and retain data scientists, engineers, analysts, risk managers, and product managers.
Your existing teams should also be trained in managing and preparing data for AI-readiness, following best practices fore effective data governance. They should also be taught the use of data tools and managing data quality. This will ensure that any data your organization collects for use by AI is already cleaned and ready to be transformed.
Integrating Your AI-Ready Data
Now that you have your data prepared, you need a way to integrate it with your AI model. This is where RAW can help. Use it to build, share, and host APIs that seamlessly link your data to your model in real time.
The platform’s easy, low-code development means you can build APIs faster, even if you don’t have much technical expertise. What’s more, it now offers CRUD support for dynamic data management.
There’s no better way to connect your AI-ready data to your AI tool than APIs. They allow real-time data gathering and integration, giving you a model that’s faster and responsive. In fact, we’ve got 10 reasons why APIs are the best large language model (LLM) tools that you might want to check out.
