My top five AI predictions for 2025. What do you think?

Hi everyone,

I'm Stijn, Collibra Co-Founder and Chief Data Citizen. I just published a list of my top 2025 predictions and wanted to spark a conversation with you all about it in the Community.

If you're like me, you were wowed by AI in 2024. AI is fueling innovation around the world–but that doesn't come without its fair share of consequences.I've shared my thoughts in this ebook: The future is here: Five ways AI will redefine technology in 2025.

After you read it, I'm curious to know what you think. I've started this conversation so you can share your insights on these predictions.

What excites you?
What concerns you?
What did I miss?

I look forward to hearing your perspective.

-Stijn

Responses

gcoliveira

1 Message

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55 Points

1 month ago

Hi Stijn,

I believe my main concern lies on Data Quality being a clear requirement for a strong AI implementation. With AI being marketed everywhere as a must-have or a silver bullet to generate business insights, my take is that many organizations are prone to put a lot of effort in AI capabilities without doing their due diligence on data quality first. There is a considerable risk C-levels will shoot the messengers when the results disappoints them.

From a product perspective, I´m overly concerned about how difficult it is to integrate Collibra Data Quality Tool and Data Intelligence Platform (aka The Catalog). We must have a strong foundation to perform a single ingestion that makes those data sources available in both tools at the same time. Otherwise Data Product teams will keep using their custom tools to filter and expose curated data without relying on Collibra's DQT.

Thanks for your post and questions - sorry for not seeing the brightside. Maybe that comes with product updates throughout 2025.

Cheers,

Giuliano

2

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stijnchristiaens

4 Messages

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315 Points

@gcoliveira Thanks for the reply Giuliano - and all the best wishes for 2025.

Fully agree with you that AI success will depend on DQ being put in place. My best bet is that many AI initiatives will skip that step initially as they are racing to "get something out the door" for the rushed executives. So some will do it right and tackle DQ early, some will learn the lessons the hard way and they'll have to tackle DQ later anyways.

On the DQ and DIP integration: thanks for the feedback, and I know our R&D colleagues are working on this topic as we speak so let us know if you'd like to learn more.

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1 month ago

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sclark

1 Message

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615 Points

@gcoliveira Completely agree on the need for data quality to responsibly wield GenAI. Established data quality techniques (completeness, accuracy, consistency...) can help when LLMs are used to query structured data through methods like text-to-SQL, but the real innovation area I'm keen on is data quality for unstructured text. To use LLMs in an enterprise context where facts and sources matter, I think we'll need to have both well in hand.

Regarding your challenges with the existing integration between Collibra DIP and DQ&O, I absolutely hear you and agree. Our data quality team's top priority in 2025 is unifying the experience across the entire Collibra product ecosystem. Can't wait to share more details later this year.

Cheers and Happy 2025,

Seth

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1 month ago

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My top five AI predictions for 2025. What do you think?

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collibra_data_citizens

My top five AI predictions for 2025. What do you think?

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