Maximizing Your Application's Potential with AI-Optimized Data Architecture

How to architect your data to play well with AI systems

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In the age of AI, it's becoming more and more essential to make sure that everything you do aligns with AI because it will be much easier to implement and use AI to grow and improve your business.

Your data is important for your business and it can be a powerful force, giving you insights about your current business situation, as well as predictions on how to improve it.

One critical aspect we found out at InsightBase about the data, is the data architecture. It's essential to have an AI-ready data architecture in place because it makes analyzing the data much easier.

Even though the current AI systems are already powerful enough to analyze your data, it's even better if you architect your data in a way that will make it way easier for AI systems to explore it. Let's dive into some specific examples and actionable steps that you could perform to improve this aspect of your business.

This guide is database agnostic, and we won't dive deep into data architecture concepts that apply to any database.

 

Naming

I can say that this is one of the biggest problems for AI systems, and getting this right can offer you huge advantages when analyzing your data with AI. Having the right names for your tables and fields will make it way easier for the AI, and anyone else working with your data. Here are some things that you could do to improve this aspect of your data architecture:

  1. Avoid shortening the names. For instance, someone could shorten prospect_lifetime_value to pl_value
  2. Remove redundancy. For instance, if you have an orders table and a status field, make sure to name it status, instead of order_status. This will also help you save some spacing

 

Setting the right field types

When creating a table field, make sure that you user the right type. When an AI system is inspecting your database structure, it makes decisions based on your database schema, and having the right field types would help the AI systems a lot.

Here's what you can do:

  1. Use boolean instead of an integer with (for false), and for true values. When generating SQL code, AI systems will usually generate a code that implies that your field is either True or False, and not "0" and "1".
  2. Avoid using integers instead of "enums". For instance, if you have a status field that can be either: "pending", "received", or "shipped", make sure to use a string with those specific values, instead of using a number status, like "1" for "pending", "2" for "received", and "3" for "shipped". I know that someone will say that this can save storage space and make indexing faster, but indexing can also make database writes slower, and space is not an issue in our days.

 

Keep the important fields at the top level

Most databases allow you to have sorted fields in one way or another. Make sure that you keep the important fields at the top level and avoid nesting them. It will be easier for the AI systems to generate the right code to analyze your data.

 

Conclusion

If you follow the above steps, you can see 2x, and even 10x improvements in data analysis, and this will help not only the AI systems but also will make the software development process easier, it will make it easier for everyone that's analyzing your data to query, understand and analyze it.

 

InsightBaseAI is able to understand and analyze your data even if it's complex and unstructured. Feel free to create an account, and connect your database to see it in action.