Principles of AI-ready data: Put yourself to the test

You cannot trust the source of the data found on the internet. Without knowing the source of the data, you can’t know whether it’s true. If you base your AI system on false data, this can lead to inaccuracies and false results.

Narrow and siloed data sets can lead to bias within AI systems. By only focusing on a small number of datasets, these can include prejudicial assumptions, meaning the AI system is more likely to make unfair decisions.

Diverse data means you draw from a wide range of data sources that span different patterns, perspectives, variations, and scenarios relevant to the problem domain. This is crucial for stopping bias in AI systems and ensuring that AI applications are less likely to make unfair decisions.

While it's true that ML and Gen AI applications thrive on diverse data, the freshness of that data is also crucial. Just as a weather forecast based on yesterday's conditions isn't conducive for a trip you plan to take today, AI models trained on outdated information can produce inaccurate or irrelevant results.

Fresh data allows AI models to stay current with trends, adapt to changing circumstances, and deliver the best possible outcomes. Therefore, one of the key principles of AI-ready data is timeliness.

Timeliness does matter, so using old data will lead to inaccurate and incorrect results. Think about it like a weather forecast based on yesterday's conditions. That won’t be conducive for a trip you plan to take today, AI models trained on outdated information can produce inaccurate or irrelevant results.

The success of any ML or Gen AI initiative hinges on one key ingredient: correct data. This is because AI models act like sophisticated sponges that soak up information to learn and perform tasks. Inaccurate data, however, is like a sponge mopping up dirty water, leading to biased outputs, nonsensical creations, and, ultimately, a malfunctioning AI system.

AI models cannot detect what is and isn’t true. We’ve all heard the saying “garbage in, garbage out” and that’s exactly what will happen if you feed or train your AI system on inaccurate data. You’ll get inaccurate results!

While its important enough to have enough data to improve learning and performance, inaccurate data can lead to incorrect and unreliable outcomes. Many people take what their AI systems tell them at face value, but you cannot trust the results of AI that is built on inaccurate and incorrect data.

AI systems can do powerful things with sensitive data like Personally Identifiable Information (PII), financial records, or proprietary business information, but this power comes with a responsibility. Unsecured data in AI applications is like leaving the vault door wide open. Malicious actors could steal sensitive information, manipulate training data to bias outcomes, or even disrupt entire Gen AI systems.

Security of your data is critical and should always be prioritized, this is why security is one of the six principles of AI-ready data. If you do not prioritize security above all else, it’s like leaving the vault door wide open. Protecting your data is key to improving the overall trust in your AI system and safeguarding its reputational value.

Unsecured data in AI applications is like leaving the vault door wide open. Malicious actors could steal sensitive information, manipulate training data to bias outcomes, or even disrupt entire Gen AI systems. Securing data is paramount to protecting privacy, maintaining model integrity, and ensuring the responsible development of powerful AI applications.

AI-ready data needs to be discoverable and readily accessible within the system. Imagine a library with all the books locked away – the knowledge is there but unusable. By creating a business glossary and indexing metadata you enhance human understanding of the data and make the information easily searchable via a data catalog.

Governance is a key component to ensure data security, and in cases with highly sensitive information it needs to be in place. But if you unnecessarily restrict access you prevent other business users in the organization from discovering the data and using it to help make informed decisions.

AI systems are capable of using structured and unstructured data sources. Whatever the source, results must be easy to discover and access within the system for all relevant users.

That’s not true. Data transformation is the unsung hero of consumable data for ML. The effort invested in cleaning, organizing, and making data consumable by ML models reaps significant rewards. Prepared data empowers models to learn effectively, leading to accurate predictions, reliable outputs, and, ultimately, the success of the entire ML project.

AI’s potential rests on the ability to readily consume data. Unlike humans, who can decipher handwritten notes or navigate messy spreadsheets, these technologies require information represented in specific formats. Making data easily consumable helps unlock the potential of these AI systems, allowing them to ingest information smoothly and translate it into intelligent actions for creative outputs. That's why making data readily consumable is the final principle of AI-ready data.

Wrong! AI’s potential rests on the ability to readily consume data. Unlike humans, who can decipher handwritten notes or navigate messy spreadsheets, these technologies require information represented in specific formats so cleaning and organizing data is a crucial first step. Making data easily consumable helps unlock the potential of these AI systems, allowing them to ingest information smoothly and translate it into intelligent actions.