The Top 3 Data Architecture Trends (And How LLMs Will Influence Them) | by Hanzala Qureshi | Jun, 2023


Embracing the Next Era of Data Architecture: Unveiling the Top 3 Trends and the Influential Role of LLM

Photo by Google DeepMind on Unsplash

I published an article last year on Data Architecture trends.

This was before Large Language Models (LLMs) became all the rage and influenced most industries. Gartner reports, “Venture capital firms have invested over $1.7 billion in generative AI solutions over the last three years.” It is, without a doubt, that LLMs will influence most areas of Data Architecture.

With that in mind, let’s explore three Architecture trends and how LLMs will influence them.

1. Cost Optimisation Using Co-Pilots

I’m a big fan of co-pilots that help the end user efficiently complete their tasks.

Being a regular user of Grammarly, I appreciate how it helps expedite the editing process of any written form of content. Similarly, co-pilots will take the main stage in most of our work, including Data Architecture.

A data architect’s daily in-tray will include aspects of data model design, setting standards, and implementing governance structures. Co-pilots like Microsoft can help finish off sentences in an email and create announcements based on spec documents. Similarly, a co-pilot for a Data Architect can complete entity-relationship diagrams (ERD) based solely on user requirements by understanding your design constraints. Co-pilots can work alongside the architect and help expedite their daily process.

It should be no surprise that companies will start looking at ways to optimise their cost if productivity starts to skyrocket. Some estimates of thousands, if not millions, of jobs to be impacted.

For example, management consultants have been helping organisations restructure and reduce overhead costs by finding efficiencies. Similarly, the implementation of co-pilots will see a reduction in human resources due to more reliance on AI-led task completion. Tasks such as writing design documents, following approved patterns to create data architecture diagrams, creating data models and associated SQL queries, auditing SQLs against approved





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