Those in the know have long been aware of the potential of AI technologies. However, powerful as the data you feed it. Is your data AI-ready?
If not, you’re not alone. While 55% of companies have adopted AI, many still struggle with messy, unorganized data that slows down AI projects. Whether you’re building predictive models or enhancing customer experiences, preparing your data is step one.
In this blog, we’ll break down the four critical steps to making your data AI-ready, from cataloging and curating to ensuring compliance and improving data quality.
Related Post : Data Governance Tools: Capabilities To Look For
What is AI Readiness?
AI readiness is a broad concept that touches on every aspect of your organization, from company culture to infrastructure and resources. However, at its core, it boils down to one simple question: Is your company prepared to leverage AI technologies effectively?
When it comes to data, AI readiness means ensuring that your data is organized, clean, and easy for data scientists to access and use in AI modeling. Many organizations face a key challenge here—they don’t have data scientists on staff full-time. Instead, they rely on external hires or dedicated teams to tackle AI projects.
This creates two potential issues:
- Costly delays: The longer it takes data scientists to interpret and organize your data, the higher the project cost.
- Competitive risk: The more time spent cleaning and organizing data, the more likely it is that competitors will outpace your AI efforts.
In short, the faster and more efficiently you can prepare your data for AI, the greater your chances of staying ahead in the AI race. Being AI-ready is about minimizing these delays and costs, so your business can fully harness the power of AI.
1. Creating a Data Catalog
Imagine a kitchen with all your ingredients spread across different cabinets—some in the pantry, others in the fridge. Cooking a meal becomes a headache. Similarly, when your data is scattered across different systems, it’s hard for data scientists to work efficiently.
Why it matters:
Most companies have data spread across various repositories (data warehouses, departments, etc.), making it difficult to find and use.
How to fix it:
Build a centralized data catalog. Tools like OvalEdge's data catalog can crawl through your data and create a single place where all your data is accessible and organized.
A data catalog not only locates your data; it also adds context. It is like labeling ingredients in a pantry, it ensures data scientists understand what they’re working with.
Related Post: How to Build a Data Catalog
2. Classify and Curate Your Data
Once your data is cataloged, the next step is to curate it. Curation means organizing your data in a way that makes it easy to find and understand.
Why it matters:
Without context, data is like ingredients without labels—hard to use! Curation helps ensure data is correctly organized for AI projects.
Key Benefits:
- Data becomes easier to find and prioritize.
- Data teams and business teams gain access to important contextual information, like who owns the data and what it’s used for.
- AI models can be built faster, reducing the time to market.
Manual curation can be time-consuming, especially with large datasets. Fortunately, AI-driven tools like OvalEdge can speed up the process by automatically classifying data. But don’t forget to involve business teams—technical curation alone won’t provide the business context that’s crucial for AI
3. Ensure Data Compliance
In today’s world, ignoring data privacy regulations can be disastrous. AI models often handle sensitive information like personal customer data, which makes compliance a critical step.
Here’s why compliance is important:
- Avoid costly fines: Laws like GDPR and CCPA require companies to protect personal data. Failing to comply can lead to massive fines.
- Ensure global scalability: AI models used across different regions must comply with local regulations. A model built for the U.S. might not meet the strict data privacy rules in Europe, for example.
How to stay compliant:
- Curation tools: Flag sensitive data like Personally Identifiable Information (PII) during the curation process.
- Keep records: Ensure data scientists know which datasets can be used and under what conditions.
Real-world example:
Clearview AI was fined €20 million for violating GDPR by collecting facial images without consent, demonstrating the costly impact of ignoring regional data laws. Proper data governance could have prevented this breach, ensuring compliance and avoiding penalties.
Related Whitepaper: How to Ensure Data Privacy Compliance with OvalEdge
4. Data Quality Improvement
While organizing and cataloging your data is essential, improving data quality is the long-term goal for AI success.
Why it matters:
AI models perform best when trained on high-quality data. However, data scientists can still work with less-than-perfect data in the early stages, provided it’s organized and accessible.
Quick wins:
- Catalog and curate first: Ensure your data is accessible and well-organized upfront.
- Teach data scientists to spot quality data: Training your team to identify the best data available will improve the initial AI models.
Over time, invest in data quality improvement through better processes, policies, and governance. Like sourcing the freshest ingredients for a meal, this takes time, but the results are worth it.
Conclusion
AI has the potential to transform your business, but only if your data is ready.
Commercial large language models (LLMs), like OpenAI, are a commodity fuelled by generic data. While originally, these models will have been trained on exceptionally high-quality data, over time, this quality has degraded as the models have relied on user-generated internet data for training.
That's why they must be enhanced with proprietary data. By following these four essential steps: creating a data catalog, curating your data, ensuring compliance, and improving data quality—you can unlock the true power of AI. Companies that act quickly will gain a competitive edge, while those that delay risk falling behind.
