Getting Your Data AI-Ready Preparing JD Edwards Master Data for Success

So we're going to talk about some very specific challenges that that come with the data today, right? And again, I will emphasize, we're not just talking about getting your data cleaned up um or or or getting it unclean, right? We're going to talk about a lot of different elements of it. So, so let's dive into this and and and you know why does it really matter, right? Um it it matters because when you take data with AI and you're you're putting data inside of these AI models and these AI skills, those AI models and skills are going to learn and extrapolate and predict based on what data you're providing it. Right? You know, often I'll refer to this as an intern instead of a um instead of um you know, an assistant or anything like that is because if you think about it, right, the the way we we even teach interns and the way we teach people who are younger is we want them to go through experiences and we want them to go through scenarios where it teaches them things, right? So, it's the same with AI, you know, um if you don't feed it the right kind of data, number one, it's going to be garbage in, garbage out, right? um the data that you train and prep AI on is the data that you're going to is is is where the model will start to to give you results based on right so where you're not seeing where you're where you're providing data that's um you know not complete or data that has some biases in it or data that is um stale right you're going to get uh essentially take any of those acronyms and you're going to get outputs and outcomes that are exactly those you'll get outcomes that are biased or stale or or incomplete, right? Um data without without the right kind of data, right? Again, we're not talking about just clean data. We're talking about all the right kinds of data. You're going to create errors in your model without the right kind of data, right? You're also more likely to uh to to amplify any biases that may be in your model. So, if you think about it, right, you're trying to predict for um you know, orders at risk or you're trying to predict for customers at risk. If you're trying to predict for something like this, if you're not really providing the kinds of data where uh AI can extrapolate those insights and then or sorry, you pull out those insights and extrapolate that you know based on those insights to recognize those patterns, you're not going to really be able to find those biases when it comes into production, right? So, there's a lot of challenges that come with this. Um and and and typically, you know, I think the biggest thing we take about it and and it's one of those things that, you know, um it's it's like, you know, we always say about MRP or anything else, you know, garbage in, garbage out, or if you're not going to feed the right kinds of data to these AI models, you're not going to get the right kinds of outputs um from from these AI models, right?

And so the next two we're going to talk about these are a little bit more unique. So these start to come into play when we're talking about very scenariobased very outcomebased in insights that we're trying to pull, right? Um and this is where data biases and data diversity starts to really matter, right? Uh and these are concepts we don't typically think about in our day-to-day. We don't really think about data biases and data diversity. you know, most people don't even think about these things outside of, you know, um, social issues and things like that that are going on, right? But this is very important in data biases both in, um, public AI systems as well as, um, you know, business AI systems, right?

So, you know, you don't want data that's specifically biased towards one outcome or the other, right? uh you want to provide a healthy mix of data that's representative of the reality sample that you want to pull the insights out of. So what does that mean? Right? What that means is if you're trying to create a model that is trying to figure out orders that are at risk of delaying shipment on time, right? You don't want to just provide the model with data around orders that are always delivered on time and in full, right? You want to make sure that orders are you want to you want to provide data of of both.

Now, uh the other skews the other way. You don't only want to provide data of of orders that were delivered late and partially, right? Because that will now skew bias the other way. So, there's a balance here. The other challenge that we see is that data is not diverse, right? um you're not taking a wide breath of of the data that you need and introducing all of those scenarios, right? Sometimes people tend to get a little specific, right?

So, one of my examples on this is when you're for example looking at pricing to to do analysis around pricing optimization or pricing segmentation, right? Or or pricing adherance, right? You don't want to just in introduce pricing for one type of customer or one region. You want to have pricing for a variety of different customers that ensures that the model can really see what are the criteria I'm looking for. Um what what are the different criteria and what are the different points of the context of your pricing model that really matter, right?

And the last problem that I think is really really key is that your data is just sparse. usually don't have enough um you know and that happens sometimes when we're looking at certain types of data around um you know uh like you know usually with orders and things like that but if you're trying to be more predictive around uh uh specific types of purchase orders or specific types of orders or specific types of pricing right if you don't have data around those specific things it's it's kind of hard. So, we'll talk about how um how we address each of these and and where what we really do um when we encounter these problems.

But before I move forward, Manuel, I want to open up to you in case you want to add add anything here.

No, all good all good points, right? That the the the the you know, sparse data of course is is a challenge sometimes, right? Because if you have a small pool, you might not have a a stat statistically represented uh kind of amount of data and it and it doesn't feel good, right? You make decisions on on on an impartial view of the world. Um the private information one also hits home uh for everyone, right? Because we we probably all had examples heard about it in in um in in our lives, right? Where where in a note that wasn't intended to be private, you include someone's phone number. That's a problem. Now, now that's exposed.

So, um, and then the last thing I want to say is I think what Mo's talked about is great. It's fantastic and it applies to JD Edwards, but it's not limited to JD Edwards, right? One of the things that as we Mo and I have spoken to customers, it's not just customers saying, "Hey, they're looking at AI with JD Edwards." They're looking at AI for different systems, right? As we know, right? Whether it's CRM or security systems or others, right? there there there's AI uh components that are being incorporated in those uh solutions and others uh where you also have to take these same considerations into account.

Yeah. I think about some of our agentic solutions, right, that we that we talk about with customers and we build for customers. It's never can you just train it on JD Edwards data, right? It's always can you train on JD Edwards data? Can you train it on our product data? Can you train it on our customers other data? Can you train it on CRM data? And and you know uh and that I think was a big learning point for us too because you know we're just JD Edwards nerds. So we set out to do this with JD Edwards and we're like oh wow you know we're going to have to learn how to incorporate a very large universe into this. Um and it was a really um cool experience for us too as as we as we continue to go through it with customers.

So what does this all mean? What do you actually do with all this now? Right. So typically what we we address is there are going to be certain types of data that we want to make sure you're addressing um prior to even kind of starting building out the models that are in AI. Now I'm not saying that nothing happens here in AI before you do it but there's really not much precluding you from doing this type of data cleanup even now right uh we have some customers we work with where we uh actually help them develop a master data governance uh model right now so they can get prepared to do things like AI and things like um you know more accurate supply chain planning right.

So, uh you know what? So, what do you do about all this? What do you actually do when you need to have to to address this situation, right? Um you have to get your data ready. Uh, and one of my favorite things is sometimes there's just no substitute for rolling up your sleeves and and doing the hard work. That that is that is sometimes what it just comes down to with data readiness, right? Um, so as we're going to go through some of these, you know, we're going to talk about right now what are some data prep and cleanliness exercises you can do. We're going to talk about my favorite thing, which is how you're going to use orchestrations to help with your data problems. Uh, and lastly, and this is very meta of me, is that how you're going to use AI to help solve your data problems to get your data ready for AI. Wow.

Got it. Got it. Got it there. And And please any point if you guys have questions, commentary, um, uh, would like to, you know, uh, you know, get in on the conversation, please drop drop your comments below. There's also still our poll is still open. So, I'd still love to hear responses from everybody if I can. Um, you know, really want to know what your what your uh data is like right now.

So, how are we how do we actually do this, right? So, you know, the thing is that it just takes it takes a good amount of assessing and understanding where your work is. So, we're going to talk about the AI journey in in in a little bit, but one of the things we do, we we do a data readiness assessment. It's actually one of the first things we do, right, Manuel? It's like probably right almost at the beginning, right after alignment day almost, right?

Um. Yep. And then to use cases as well. Yep. Yeah. So, you know, that's the first thing we do is we help we help we want you to identify your use case, right? So, what is it you're going to do with AI and then we want you to identify we want to start working with you to identify where are your data gaps and challenges so you can get that use case out of AI.

So, if your data is incorrect, right? Um, this is one where there really just is no substitute besides just going and cleaning it up right now. There's a combination of thirdparty tools. There's a lot of great APIs nowadays. Um, and and and AI and orchestrations here can help where like for example, if you're missing customer revenue data or you're missing um data around products or you're missing um data around uh mass, you know, if you're if there's master data that's incorrect, right? It's you you you you have to use a combination of orchestrations, AI, and manual efforts, right?

One of my favorite examples is zip codes. There's a really great um zip code cleanup tool which is is not that expensive. Um, and you can pair it with a very simple orchestration. And what it will do is it will clean up all of your zip codes. And then not only that, it will ensure that going forward all of your every time you enter in an address, your zip code is going to get fetched automatically. You won't have to type it in. You can also then use it to continuously monitor zip codes and keep them clean. Right? So that's an example of where you can use thirdparty tools and APIs to really just keep this data um really really clean going forward and and making sure there's no incur incorrect uh you know data that's in there right so this is really helpful around addresses product information you know um a lot of times like product dimension data right um you know if for example you're not you're having a hard time creating keeping product dimension data up to date um there's uh there's both uh AI I enabled tools that you can buy now that are getting very accurate on on doing product dimensioning. Uh there's also old, you know, more legacy type tools like a cue to scan that you can get which would which help keep your data up to date, right?

Um if you have specs or drawings in a PLM or a CAD system, you can integrate those with JD Edwards and bring that in, right? So there's there's a lot of tools here um that allow you to do that. And the nice thing is don't mistake any of this as oh I have to build all these expensive integrations. Um if you need to you should right with your business if you need to there's value to it you should but you could also always just export the data out and start to do mass data updates in Excel or sorry in orchestrations right so orchestration can very much help with mass and targeted data cleanup.

Um lastly, if your data is Dex, if your data is kind of stale or incomplete, first thing you want to do is you want to assess to make sure you find where the data is stale or incomplete, right? Um usually this is a combination of conversations as well as system reports and things and system data we can pull. Same thing here with orchestrations. We can really really help fill in a lot of the blanks with those data.

in um you know typically when I have data that's stale or data that's complete um you know a couple hours in a data excel in an Excel sheet and then an orchestration to take all that data and ingest it back usually gets the data pretty pretty well cleaned up and updated right may take a round or two or a couple of rounds but um we've gone through this with many a customer where um we we've helped uh get those those that data uh cleaned up with them right

Absolutely. Thank you, Mo. And and and I touched upon this briefly yesterday in the uh keynote. Uh Mo and I talked about it right uh yesterday right after Julie Holmes keynote uh and and the point was in terms of adopting AI it's very similar to adopting or implementing an ERP system or another system right you need to have a methodology a process a proven process right that allows you to get a successful implementation and and and realize the the value that you're looking for AI is no different uh and as we as we worked internally at ERP suites and and speaking to customers right all these pe all these components that we have here are relevant there are relevant component it's this is a little bit of like a multi- a polomial equation right there's they each have their contributions to making sure that that what you get out of that polomial equation is a is a positive benefit for your business so uh I'll go through this and and just highlight some of the key components because it's important, right?

One is alignment day, right? There's like alignment day here is really making sure that you know company, you know, what what is understanding what's the company's objectives? What are the uh leaders of the company looking at from kind of a direction vision and what what what's looking to be accomplished? where are there some friction points uh that could help uh you know achieve uh the their goals better or maybe they're not reaching their goals and they need they need some assistance AI is in the mix that's that's a little bit about what a what alignment day is about and then uh we go into a variety of different areas that is really the assessment phase right we get a little bit of understanding of the of the company and then we do some assessment and roadmapping right in terms understanding design thinking where you know that that we had some conversations about potential opportunities in the business uh where things could be better.

Uh doing a design thinking workshop to to really assess that uh as well as the use case uh perspective is trying to find not just any use case but the ones that are impactful right because we like to talk about well there's like Mo mentioned right there's the relevancy of a use case there's the impact right and is also there's a third component I would add is that is like is does that fit for the company is a fit right and and and when I say the fit that's there's a cultural component to that right are there going to be is culturally are they going to adopt you know that solution or will users not really use it um that's what really the use case roadmap is about is to come up with a list of of use cases that will really help um and they are aligned to your objectives.

Security workshop of course is you know kind of summarized in a very short phrase is what is the CISO looking for at your organization or if you have a security officer in terms of what's what's what what are they looking for in terms of protections right are they going to allow any data in uh I would assume that most customers if not all are going to lock data from getting out uh but do you do you want uh ancillary information that will enrich the AI's capabilities to be able to provide you know better uh forecasts for example financial forecasts sales forecasts.

Data and infrastructure workshop I think this is a little bit what Mo just you know was talked about in this session right and looking at data being able to see okay where are the opportunities uh where where the assessments need to be done and how to address them. Um and then the last but not least is coming up with a road map, right? We have use cases, you have all the the things we produce a road map for customers so you can understand all the different pieces here.

Um and and and one thing I I failed to mention is what we're going through right here right now is kind of our our detailed AI journey. We have an abbreviated version of this for quick win scenarios, right? where we still go through all these steps but instead of kind of going breath kind of across all areas of saying and if we we we talked to a customer in alignment day and saying we know it's sales manuel we want to go Mo and Manuel we want you guys to look at sales and we have a lot of friction there we want to be able to uh reduce our our quote to to order signing process right it it's it's x number of days or um x number of hours we want to streamline that we believe we can do that um so Then the focus in these areas that I just discussed will be around those areas.

So just bear in mind this is the detail one. There is a streamlined one that would be really focused but still covering these essential elements. Phase two here is actually okay once you've come up with the use cases the road map we've we've you know we've identified the opportunities in the data so forth right then it's now okay how do we address those components right whether it's the data cleansing that Mo just talked about uh but but also security right does security need to be tweaked to be able to handle those scenarios as well as establishing those policies have those policies been established because remember it's not just JD Edward security it's the AI security and I would say more specifically an enterprise AI security constructs that they have that need to be set up so that you are protected right you get the benefits of being able to get data in but not necessarily leaking data out.

And then of course process improvements and then of the third phase actually okay now you have that foundation solid is uh implementing and delivering the AI solution And this is this is the methodology. So that's you know so at each step of the phase you get deliverables you get you know you get our findings you get our recommendations and then we work together on executing and then with this we have found that this is this is the way that we we're able to get customers not only having a successful implementation um you know but but it's it's it's also maintainable and it mitigates all the potential uh landmines sometimes that people talk about in terms of AI.