Keydunov: Large enterprises abandon embedded analytics vendors to build in-house
Artem Keydunov · Powering your Copilot for Data - with Artem Keydunov from Cube.dev · Oct 27, 2023 · at 37:57
Artem Keydunov, co-founder of Cube.dev, explains why software startups focused purely on embedded analytics struggle to monetize and scale.
0:00 / 0:23exact quote · 23.8s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“If you look at the embedded analytics market, the bigger organization, the big gets, They're really more custom, you know, like it becomes, and at some point I see many organizations, they just stop using any vendor, and they just kind of build most of the stuff from scratch, which probably, you know, like the right way to do. So it's sort of, you know, like you got a market that is very kept at the top”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Artem Keydunov
Keydunov: Natural language AI will not consolidate the fragmented BI market
“I don't think it's going to be something that will change the BI market, you know, like something that will can take The BI market and make it more consolidated rather than, you know, like what we have right now. I think it's still re will remain fragmented.”
Keydunov: Modern data stack will not be dramatically disrupted by AI
“I don't see a lot of being compacted by AI specifically. I think, you know, that space is being compacted as much as any other space in terms of, yes, we'll have all this copilot capabilities, some of AI capabilities here and there, but I don't see anything so…”
Keydunov: AI will not make embedded analytics easier to monetize
“I don't think AI really going to change that just because it's using model, you just pay to open AI and that's it. Like everyone can do that, right? It's not much of a Competitive advantage. So it's going to be more like a commodity features that a lot of like…”
Keydunov: The semantic layer is the ideal repository for AI data context
“So our take of that and my take is that semantic layer is just really good place for this context to live because you need to give this context to the humans. You need to give that context to the AI system anyway, right? So that's why you define metric once an…”
Keydunov: Querying a semantic layer over raw SQL reduces LLM errors
“Now, the query, I believe, should be created against semantic layer, because it reduces the room for the error, because what usually happens is that your query to semantic layer would be very simple. It would be like, give me that metric grouped by that dimens…”
Keydunov: Semantic layer metrics should be managed as code via pull requests
“So I think we should treat our metrics and we are in semantic layer as a code, right? And then collaboration is a big part of it. You know, like if there are like a multiple teams that sort of have a different opinions, let them collaborate on the pull request…”