specifically about analytics, right, which was what we’re here to talk about today, as we’ve discussed quite a lot already, the underlying data quality infrastructure issue that’s real. That’s top of mind. I think that exists everywhere. Really interesting hearing Qiraat talk there about, in essence, ROI Right?
Return on Investment. I think that’s a real challenge for a lot of organizations around these projects. I love the way keyra framed it. As you know, don’t worry about the FOMO kind of thing, like, like, drill down to the value.
At the same time, organizations are worried about being left behind and from an innovation perspective.
But I do think that issue of ROI is increasingly becoming a space where some organizations can see AI helping right data preparation, in and of itself, can consume a huge amount of time and resources due to those difficulties in finding, accessing, cleaning, transforming, sharing data efficiently.
I think the increasing number and complexity of data sources coupled with the need to access them across distributed ecosystems, again, both the guys spoke about that that demands significant resources and expertise, and I’m starting to hear it buyers think maybe AI can help with some of that complexity, like applying AI to an imperfect system, as I mentioned earlier.
Um, other things I think IT teams are often overwhelmed by the rising requests for self serving data access and integration, varying data requirements from different users, complicating the process further.
So again, starting to see some opportunities for AI supported data platforms to help like reduce some of the challenges around data preparation and management, incompatible data types, formats, aging data. These things all pose obstacles to effective data access and collection.
Skills Gap, which Qiraat hit on, I think the data related skill gaps are further hindering the development of robust data management as well as AI related skills gaps.
It’s another area where actually, I think organizations are starting to think about AI supported data platforms, or potentially agentic AI helping to winnow data into insights. Again, there’s a risk involved, because you’re not doing it through human insight, but potentially it could be helpful and work.
But with all of these, these pieces, all of these challenges, I think there is one underlying challenge that that we see and pretty much everywhere, which is, you know, you have the question of, can it be done?
And after you answer, and this is where Qiraat was coming in, there are the questions of, will it work and should we do it? Because I think.
Think the other questions that I’m hearing a lot, the other challenges that I’m hearing a lot are this year as opposed to last year. And Keith, you and I have spoken about this many times.
The other challenge with AI applied to analytics is, okay, we can generate insights, but will those insights help us? Can we trust them? And then there’s the big question of, how are we managing the risks? Because some of those risks are unforeseen.
So it definitely is all of the above, from what what the guys were saying before, plus some others, Keith Shaw
