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The headline of the second annual The State of Data Quality survey was, without a doubt, the fact that data downtime nearly doubled year over year (1.89x).
The Wakefield Research data quality survey, which was commissioned by Monte Carlo and polled 200 data professionals in March 2023, found three critical factors that contributed to this increase in data downtime. These factors included:
GenAI is everywhere you look, and organizations across industries are putting pressure on their teams to join the race – 77% of business leaders fear they’re already missing out on the benefits of GenAI.
Data teams are scrambling to answer the call. But building a generative AI model that actually drives business value is hard.
Hype is a funny thing. Sometimes you find yourself in a Godfather Part 2 situation where the hype is totally justified. You hear about it. You try it. Life is changed. Hooray!
Other times, you find yourself in more of an Avatar: The Way of Water situation…where everyone around you is muttering things like “stunningly immersive,” and you’re on the sidelines wondering how much time you can spend watching blue aliens be bad at swimming.
The hype around LLMs is unprecedented, but it’s warranted. From AI-generated images of the Pope in head-to-toe Balenciaga to customer support agents without pulses, generative AI has the potential to transform society as we know it.
And in many ways, LLMs are going to make data engineers more valuable — and that’s exciting!
Shane Murray contributed to this article.
My co-founder has been all-in on data contracts from the start.
Precise endeavors must be done to exact standards in clean environments. Surgeons scrub in, rocket scientists work in clean rooms, and data scientists…well, we try our best.
We’ve all heard the platitude, “garbage in, garbage out,” so we spend most of our time doing the most tedious part of the job: data cleaning. Unfortunately, no matter how hard we scrub, poor data quality is often too pervasive and invasive for a quick shower.
Occasionally, we’ll talk with data teams interested in applying data quality monitoring narrowly across only a specific set of key tables.
The argument goes something like: “You may have hundreds or thousands of tables in your environment, but most of your business value derives from only a few that really matter. That’s where you really want to focus your efforts.”
As companies increasingly rely on data to drive decision-making and power digital products, it’s critical that this data is accessible, discoverable, and reliable.
For decades, organizations have relied on data catalogs to drive these initiatives. But are we missing the boat?