Are Data Teams Too Busy For AI?

This blog has been written by Matillions CTO and Co-Founder Ed Thompson. I am so excited about the applications of large language models (LLMs) in the world of data integration and, of course, so needy to talk endlessly about the AI features we have been building at Matillion that I have spent time talking to customers about using AI both in their data pipelines and to build their data pipelines.

It turns out that customers and prospects have shared a common theme. Front-line data teams are drowning in business data requests, leaving them unable to navigate this new and rapidly evolving landscape. It appears that the naturally insatiable appetite for information is not allowing them the time and space to understand and adapt to this emerging world.

This, of course, puts them in a dangerous situation because AI, when done right, is really about productivity. 

AI at its most powerful

At its most powerful, AI can take so much of the effort out of building and maintaining data pipelines that it can move the needle on a data team’s productivity. Not only that, but by applying AI to data within the pipeline, tasks that would have become projects in their own right (such as sentiment analysis or text summarisation) are now accessible without specialist skills. They do not require the ability to wrangle your own data into some highly tailored, highly specific, (highly expensive), and ultimately temperamental to the point that no one wants to touch the Machine Learning model.

Is this true for you? Are your data teams constrained? Well, the analysts tend to agree with the anecdotal evidence we see here at Matillion.

In 2023, research by Vanson Bourne highlighted that 90% of data experts seek to alleviate workload increases from fragmented pipelines and overwhelming business demands, with 84% of respondents describing the volume of their workload as exceeding their capacity.

It’s a strange situation. Our data teams are constrained, but the new technology that can help requires them to take a step back before they can accelerate. It's almost like a 19th-century coachman being too busy to take driving lessons. As data leaders, we need to make time to ensure our teams can continue to succeed this year, next year, and beyond.

Get serious about AI

If 2023 was the year everyone got (over?) excited by AI, 2024 is the year everyone gets serious. That means, this year, we will start to see techniques, tools, and solutions that have enough success behind them to drive confidence, productivity, and progress. It’s dangerous to be left behind.

In conclusion, in 2024, make sure you make some time and space for your overworked data teams to figure out how AI can make them more efficient and productive and unlock new ways to use all the unstructured data, wastefully eating up disk space in some data centre.

If nothing else, they may well thank you for the technical scenery change.

Want to hear more about AI? Watch Matillions AI Spring Launch on-demand webinar that announces our brand new amazing AI features and components! In addition, schedule an AI demo with one of our experts!