Governed Data Architecture for AI
Key Points
- High‑quality, well‑governed data is the foundation of the AI lifecycle, reducing time spent on collection and cleaning so teams can focus on model work.
- Modern data architectures—whether data lakes, data fabrics, or other repositories—must adopt AI‑specific guardrails such as standardized organization, clear classification (personal, financial, etc.), and documented ownership.
- Data should be ingested from batch loads or event‑driven streams into a single, agreed‑upon location, where metadata records the data’s uniqueness, lineage, and sensitivity to satisfy compliance and improve model accuracy.
- The principles of data cataloging, documentation, and strict schema enforcement, once optional, are now essential investments to meet today’s AI performance and regulatory demands.
- Applying these governance practices across any existing data environment enables more accurate AI results and a faster, more reliable development pipeline.
Sections
- Data Governance for AI Development - The speaker explains how aggregating, governing, and architecting high‑quality data—using data lakes, fabrics, and management principles—reduces AI lifecycle cycle time, enables model experts to focus on modeling, and meets modern compliance and accuracy demands.
- Pre‑Ingestion Documentation and Automated Data Quality - The speaker stresses documenting dataset relationships, timestamps, and retention policies before loading data, then automating standardized, tested ingestion to enforce data‑quality controls in the data lake.
- Ensuring Data Quality for AI - The speaker emphasizes rigorous monitoring, change tracking, and proactive alerts during data ingestion to protect investment, control costs, and guarantee reliable, high‑quality data for downstream AI development and API use.
- Tagging, Vectorizing, and Governance in Generative AI - The speaker explains how to tag data before vectorizing for RAG, manage vector reuse through governance, and apply tagged data for fine‑tuning LLMs such as with Instruct Lab.
Full Transcript
# Governed Data Architecture for AI **Source:** [https://www.youtube.com/watch?v=AtXqpveCWQU](https://www.youtube.com/watch?v=AtXqpveCWQU) **Duration:** 00:11:57 ## Summary - High‑quality, well‑governed data is the foundation of the AI lifecycle, reducing time spent on collection and cleaning so teams can focus on model work. - Modern data architectures—whether data lakes, data fabrics, or other repositories—must adopt AI‑specific guardrails such as standardized organization, clear classification (personal, financial, etc.), and documented ownership. - Data should be ingested from batch loads or event‑driven streams into a single, agreed‑upon location, where metadata records the data’s uniqueness, lineage, and sensitivity to satisfy compliance and improve model accuracy. - The principles of data cataloging, documentation, and strict schema enforcement, once optional, are now essential investments to meet today’s AI performance and regulatory demands. - Applying these governance practices across any existing data environment enables more accurate AI results and a faster, more reliable development pipeline. ## Sections - [00:00:00](https://www.youtube.com/watch?v=AtXqpveCWQU&t=0s) **Data Governance for AI Development** - The speaker explains how aggregating, governing, and architecting high‑quality data—using data lakes, fabrics, and management principles—reduces AI lifecycle cycle time, enables model experts to focus on modeling, and meets modern compliance and accuracy demands. - [00:03:15](https://www.youtube.com/watch?v=AtXqpveCWQU&t=195s) **Pre‑Ingestion Documentation and Automated Data Quality** - The speaker stresses documenting dataset relationships, timestamps, and retention policies before loading data, then automating standardized, tested ingestion to enforce data‑quality controls in the data lake. - [00:06:23](https://www.youtube.com/watch?v=AtXqpveCWQU&t=383s) **Ensuring Data Quality for AI** - The speaker emphasizes rigorous monitoring, change tracking, and proactive alerts during data ingestion to protect investment, control costs, and guarantee reliable, high‑quality data for downstream AI development and API use. - [00:09:30](https://www.youtube.com/watch?v=AtXqpveCWQU&t=570s) **Tagging, Vectorizing, and Governance in Generative AI** - The speaker explains how to tag data before vectorizing for RAG, manage vector reuse through governance, and apply tagged data for fine‑tuning LLMs such as with Instruct Lab. ## Full Transcript
I'm sure you're interested in developing more A.I.
and how are you going to do that?
High quality data.
Let's talk about how we can aggregate and govern your data for AI development.
And let's highlight the high level architecture and guardrails for your data.
For AI results. A majority of the AI lifecycle really involves data collection and data gathering as well as data cleaning.
We want to reduce the cycle time so our professionals can focus on what they do best, which is working with models.
And there's been a lot of information already put out there about AI development, as well as data lake architecture.
And today we're going to be blending those two topics together
and talking about the best data management technologies and how those can actually set you up for better
AI development and more accurate AI results.
So what we're going to be talking about today can be applied to any kind of data architecture
that you may have or you may be working with currently in your environment.
This could be a data lake and data fabric, any kind of data repository.
And a lot of what we're going to be going through here are principles that can be applied to those architectures.
And a few years ago they might have just been a nice to have and might not even have been worth the investment.
But given the importance of AI today
and the compliance needed around AI,
these are now a must have and they're going to be important investments that we make
in our data architectures to bring them up to the standards that we need.
So let's get started at the beginning.
We have a data repository.
All data repository should be stored in a nice location
where we all agree data is going to be collected and then from there it's going to be brought in from other sources,
these other sources, maybe other data repositories that are brought in on batch
or maybe other systems of engagement that exist in the cloud and are brought over
on an event trigger. So they're more so on a stream schedule.
And so no matter what the kind of data or the kind of data behavior you have that goes into
your data lake, you're basically going to make sure that no matter what you're applying,
what's our first guardrail, which is standard organization.
So from here, we can make sure that all data that's coming in matches exactly what you expect.
And what do I mean by that? Is what data is this? Is it personal information?
Is it sensitive personal information?
Is it financial data?
You want to define what that data is and why you're expecting it
to be so that we can make sure that as we bring it in, that we're adhering to those standards.
So we want to make sure that we're very clearly documenting this,
no matter and again, no matter what the source or what the format
has to go through this process and a few other recommendations that you should store beyond just
what it is and who owns it, you should look into making sure you're documenting
exactly how this data is unique, what makes a row unique, basically?
How data sets can be joined or merged together,
and also what timestamp can actually define where data can be
is actually created and where we can look to know when it can be removed as per our retention policies.
Knowing all of that beforehand before you actually bring anything into your data lake
is essential and is going to save us so much time down the road.
And you'll see that this documentation phase really becomes the keystone for the rest of our process as we go on here.
So once we actually have that documentation
and again, that documentation can be applied in any way you want, any kind of code repository,
but once you have that, you're then going to actually be bringing in this data and that's going to come in an era ingestion layer.
And this probably is going to be really your first area
of enforcement of the data quality to make sure that as you're building your data lake,
that you are protecting it as much as you can.
So what that really means is we're going to be automating all ingestions or basically all rights to your data lake
So that just means that we want to make sure that all of our.
So we want to make sure that all of our rights that are coming in
are standardized, tested and deployed in an automated fashion.
So it's not just the Wild West.
We're not just letting anyone upload a spreadsheet and moving on with their day.
We have to make sure they go through the standardized process
and that we can make sure that everything comes in how we expect it.
Not only does this become easier to manage and easier to monitor,
it also allows us to easily link back to those data standards that we wrote before
and make sure that they are actually being enforced on ingestion
so that nothing is going to hit your data link that you need to fix later.
It's just coming in the right way at the beginning.
And then once we actually have your data like here, it needs to be stored in an efficient manner for AI.
So this is a bit different than a transactional database.
Most storage technologies will involve document storage or object storage
to make sure that we can organize data in these large pockets of information
that can handle the data behavior that we see from a data lake.
And that really is going to be large queries that happen occasionally
very different than what you would see in a transactional database, for example.
So as we're storing this, we're then going to make sure that we're enforcing
that any changes get tracked while in the process of that data leak.
And because very rarely do we see something on ingestion come in
in the perfect format that's ready for AI, usually there's some kind of post processing.
Maybe you're aggregating something that came in at a minute rate to an hour.
Maybe you're adding in more computations or you're adding in new calculations.
And again, those are new rights.
So those rights have to come under the same scrutiny as our second guardrail here of automatic rights.
But also, we need to just track any changes that occur to make sure that we are.
Tracking everything efficiently.
So from there, we can guarantee that the data will be ready,
because by the time you're ready for development, you've already made an investment.
You've already probably had many meetings, you've gotten a budget approved, you have resources assigned.
They're looking at the data.
They're ready to build.
AI You don't want to do all this work with ingestion.
And then something happened to the data that got that resulted in data corruption or just
mystery data or some kind of missing information that would cause more cycles down the line.
And also think of this, too, as an investment.
It costs the same amount of money to store poor quality data as it does high quality data.
So by making sure that you're investing at the ingestion layer and then
actually maintaining that throughout the whole lifecycle
to make sure that it's always in that state and then it cannot fall out of that state without proactive alerting.
You're basically protecting your investment.
And this again, is going to be an important cost control as well to ensure quality results.
So lastly, now we have the data an AI joint use case where we actually are going to be using that data for API.
This is where your data scientists and AI professionals come
into your data lake and they're going to be querying it to actually use that data.
And all the work that you've done before here gets carried through.
So that basically what we're going to do is we're going to tag our data
to make sure that as we use it, we know exactly where it was used.
And what model it was combined with.
So this is really the part of the organization where we can apply that AI
governance to make sure that we know exactly
what data was combined with which model and then what AI product was built out of that.
And that becomes the audibility that we need to make better decisions about what data we're using for AI,
but also so we can learn what data maybe we're not using and how we can even improve it further.
And the tagging is going to depend a little bit based on what kind of AI you're developing.
So, for example, we have traditional AI
And with traditional AI this is the air that we've been doing for decades.
This is our regression and this is our optimization.
We have our training and our testing set and that's what we can actually bring in to train on a model.
And we're going to make sure we're tallying that data, get all this good documentation linked.
And now for generative AI
You might be following something like a RAG pattern because you probably aren't
treating a large language model from scratch.
You're probably taking some kind of data out of your repository and you're vectorizing so we can enrich an existing LLM model.
And what's important here is that you're going to tag your data before you vectorize it,
because once you vectorize your data, it is hard to understand really what was in that data before it was vectorized.
So that's why all this pre-work is especially more important with the use of generative AI
And basically we also have to think about the fact that vectorizing is such a computationally heavy operation.
It's something we don't want to do lightly and you want to make sure your vectorizing the right data first.
And also this is where we can actually apply
governance again around that area so that we know how we already vectorized that data.
So you probably don't have to vectorize it again
as you can maybe find more opportunities for reuse within your organization.
And then lastly, and along similar lines, we want to maybe fine tune a model.
And this is along similar lines of the generative AI use case.
We'll be tagging the data that is being used to enrich
a large language model with a fine tuning process like an open source technology
like Instruct Lab so that basically those LLMs can be more educated on certain pieces of data that you would be adding to it.
So we want to make sure that through this whole process that we're making
sure that everything not only leads to easier development
so there's less time needed for that AI cleaning and to figure out where did this come from?
Did I fill out the right paperwork?
Is, you know, is all the compliance.
Okay.
It allows all that work to be done upfront so that you can move faster at the end.
So that's really the entire process here.
And all, of course, starts with our documentation,
comes through it, our ingestion layer, and then we enforce it through the whole process
and we make sure that it's then tagged and stamped.
And with our generative AI.
So by following all these data management principles,
we can make sure that we're supporting more accurate and faster development.