Looking for recommendations for a data readiness assessment before an AI kickoff

[u/one-step-back-04]

My team is exploring an AI initiative, and before jumping into it, I want to understand whether our existing data is actually ready for it. We r also looking at a few mid-sized companies that have worked on similar requirements before.

The main things we are trying to assess are data quality, gaps across systems, inconsistent records, and whether our current data infrastructure can support the use case without a major rework.

I’ve been looking at a few case studies, and these companies have shown up in the list: InData Labs, Aimpoint Digital, DataToBiz, Syntelli Solutions, Kanerika, but I would prefer to hear from people who have actually gone through this process before a project launch.

It will also help to know whether the assessment gives you a clear picture of what needs fixing before implementation, or if it ends up being a generic audit with a long list of recommendations.

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[u/Aggressive-Role-2472]

a lot of these assessments end up being a 40 page pdf nobody actually reads. the useful ones zero in on maybe 3 or 4 dealbreaker issues and tell you what order to fix them in and what the timeline looks like

i went through one last year that flagged our customer id mismatches across crm and billing as the thing that would torpedo any model before it got off the ground. that alone made it worth the money. the rest of the report was fluff

what i'd ask any of those firms upfront is how they weight recommendations. if everything comes back as high priority you know you're getting a cta generator and not actual analysis

[u/86AMR]

What is your use case? A little context goes a long way…

[u/Thinker_Assignment]

ai data readiness assessments are a sales tool that says you're missing what they're selling?

otherwise what do you really wanna know? AI will.have some accuracy on retrieving your data based on how clean the data model is (closer to canonical, better) and your ontology definitions.

do you have canonical models, ontology and semantic layer? if no then its not ready for chat bi
do you have guradrails and policies to prevent leakage of access or data? if no then not ready

other dimensions don't matter with AI, they just matter generally. I work at dlthub and you can find lots of free resources about canonical models on our blog

glad to answer your readiness questions in more detail if yoo prefer to dm etc

[u/Uros_Trstenjak]

Why not doing an initial AI data readiness check using AI in the first place?

It could be similar to improving AI prompt with an AI. It can at least guide you, so you'll see the main points that you have to consider before reaching out to someone to help you with the assessment. Maybe you can already filter out a lot of minor stuff yourself and ask for the assessment only on the major ones that you're not sure of or would like to double check. For example paying to someone to find "inconsistent records" doesn't sound optimal nowadays, unless your data is ultra complex and you'll need to look for the needle in the haystack only.

[u/ITAltitudesDatasets]

i agree, it's better to catch data quality issues early. knowing what needs to be fixed first would be much more helpful than getting a long list of problems