Name: Precisely Spectrum User Group
Uploaded: 2026-03-12T16:31:16.020Z
Duration: 57 min 36 s
Description: Precisely Spectrum User Group

Transcript for "Precisely Spectrum User Group": Good morning, good afternoon, or good evening, and welcome to our next, and most current version of the Spectrum user group meeting. My name is David Lee, and I am a program, manager here at, Precisely, and we're glad to have you with us today wherever you're calling in on the world. We'd like to know where you're from. In the chat button, if you see, I'm in Fort Myers, drop where you're dialing in from. And if you feel comfortable, drop in your LinkedIn profile and share your profile with, other users and, and and precisely experts. We have a great show for you today, but we do have, we're really excited. This is our first program on the new GoldCast platform, and I wanted to walk through a couple of things here. On the top right hand side of your screen, you'll see a chat button. Click on that tab, and you'll be able to drop in any chats. That is a public chat. On the documents tab, we have some resources there for you that we'll be talking about during the event today. And finally, the Q and A section. At any time during the session, you have a question and answer, drop that in there and we'll address them as we have time or during the meeting. We're really excited too because of GoldCast platform. We have a new raise your hand. feature. So for instance, when Mike gets done with his presentation, click that raise hand button, and we'll be able to, if you're comfortable, promote you to the stage. You can ask your question, and then there will be a button to leave the stage on the bottom of your screen on the bottom right. So we're really excited about that feature. And if at any time you have a technical problem, click on the gear icon, and then you can play with your settings, but that's where you find that in this new platform. We're really excited for this. So that's the Goldcast walk through. You'll see the leave stage button. Once you get promoted to the stage, you'll be in orange. We'll be in red, and then we'll be able to hear your question and take it live. And with that, let's get started. So Mike Ashmore will kick us off with a welcome and a brief couple of comments. Mark Wilkinson will talk about the power of data Datalink and go through some, slides on that. Kyle Bingham, will walk through a use case with Burger King UK. And then finally, Jeremy will wrap us up again with some tips and tricks. Again, anytime you have a question and answer, drop it in the q and a box, and I'll be back to ask those questions as we go. And with that, as always, it is my pleasure to welcome Mike Ashmore. Mike, please go ahead. Thank you very much, David. Yeah. Good morning, everybody. It's 9AM my time. It really isn't the greatest time zone that I live in because as I look at where all you guys are from, you're many hours ahead, so it's first thing in the morning for me. As you know, we this community is very important for us, and we really appreciate you guys attending today. We just by way of introduction for those that don't know me, I'm the head of the product management group for primarily the addressing solutions, which, of course, is a big part of what we do with Spectrum, and I've been a Spectrum advocate and user for, oh, several decades now. So pretty dyed in the wool with me over here. Where I wanted to start today was to talk about some interesting things that we've been seeing with our Spectrum community out there. Amanda, if you could jump to the first slide, please. Thanks. Many people were using Spectrum on premise, and for the very longest time, we've had a platform called Spectrum on Demand, which is our hosted version of Spectrum. And we would have customers say, hey, we don't wanna, install our own hardware. We don't wanna, take care of our infrastructure. Can you just run what we had on premise and run it in Spectrum on demand, and that continues to this day that people offload their hardware to us, we're running their workflows. But what we're and I I should say that part of that story was that some clients would keep their spectrum in their own environment, their cloud, if you will, and they would connect to Spectrum on Demand. So they were offloading, say, their global workload and keeping The US workload. We were seeing a lot of that happening in our US clientele where the their book of business, whatever business that was, was really one geography focused, and then they were using Spectrum on Demand to supplement that so that they didn't have to deal with large data installs and complex maintenance and what have you. But here's the interesting pattern that we're starting to see going forward is that, and we covered this in a previous, user group, was that we started to connect our spectrum on demand to our data integrity suite and offload services to our elastic modernized SaaS APIs, the data integrity suite here. And, we showed you before Jeremy, showed how we could take Spectra on premise and connect that to our, data integrity suite, and that's been starting to happen. But what else has started to happen with our clients is they're starting to take our SDK, our, our SDKs, I should say, our spatial SDK, our geo addressing SDK, and they're starting to run that in their own environment and starting to offload work to that as they centralize. So they're going for a very long migration approach here to sort of true cloud native technologies, in their own cloud, which, frankly was a little surprising for us that we started to see that, but we're starting to see it. The other thing we're starting to see is that clients are using databases like Snowflake and Databricks. And for those that didn't know, we're actually running our SDKs and our reference data natively in Snowflake and sort of semi natively in in the Databricks environment. So customers are taking that running these things up. And what they're telling us is that parity of answer is critical to their business, and this hybrid model of being able to take our technology, move our technology to their data, and run against the data is is becoming increasingly appealing. And so we wanted to share that with you as an observation that we're seeing in the market and make it available and help everyone understand that that's all the kind of possible routes that people have got to create a hybrid environment with their existing spectrum. With that, I wanted to make an invite out to anyone with this Goldcast platform that we've got here that David told you about a few moments ago. We have the ability to welcome speakers up to the stage. So if there's anyone out there that would like to engage us and engage the community directly and to ask us any questions so that we can answer them throughout this presentation. Let me just give you ten to fifteen seconds to say yes, I'd like to come up and say hi. We'd welcome that, and if not, we'll move on. So if anybody would like to join, at the bottom of the screen, there should be a raise hand button. If you click that button, Amanda, who is making us all look good on the back end, will promote you to the stage. Or you can drop a question in the Q and A section. Yep. Mike, I'm not seeing anything. So, Mark, if. you'd like to promote yourself to the stage. Hello. Thank you very much. Great. So, Mark, I'd like to introduce you to Mark Wilkinson, who is in charge of our DataLink product, and he's going to take you through about the latest updates and why you should care. Go ahead, Mark. Hi. Thanks. And thanks for everyone for the the chance to speak to everyone today. I've joined precisely about two years ago leading the data link program. And I'll just kind of explain what we're doing there and also why it would be relevant to to hopefully to most of you on the call. So the data link program kind of I've put it in a fairly, way of kind of so what? Could I you know, the challenge we've we've seen from a lot of customers is that they it's the joining of data that they don't want to go out to multiple sources of data, join it. It takes a lot of effort. It takes approximately a third of an analyst time to keep joining data on ongoing basis. And that obviously costs time, costs expense, and also the risk of making mistakes if you don't join it correctly. You can get incorrect results based on the back of it. It kind of stifles some of the innovation you'd like to do. So the data link program, it links our data, which has got many unique and persistent IDs either precisely ID, which is the address, obviously, in the building, the ID or the postcode or the boundary, we can link to other, partners of ours, their, their data. And we can kind of either give the link out through either through an API or through a file, which will give you a link to their data so you don't have to do the co joining of that data. So reason for this was it obviously reduces risk, the time and effort spent, and it gives the analysts in your company or you, yourselves, a lot more time to do the interesting stuff, which is not joining data. It's actually doing the looking at new data sources, analyzing data differently. The the ROI of any data that brought on, you can get it on there quicker. So effectively having a community of partners, we can provide a more rounded view of, anywhere in the country and give you more data rich environment to, to play around with and and look at different data sources. So I've got a diagram here which I use, and, some people laugh a bit because it's a it's a very chaotic diagram of what would be a town or a city. But as you can see, like, in the where a house is or where a company is and where they're situated, there is inherent risks there. So if you look at the the top left there, there's a little house with my indication of a solar panel or something's happening on the roof, as well as onto the right of that, there is a tree and a pond. There's trampolines in the one below that. Can knowing what your neighbor's doing, the chemical processing factory that happens to sit next door, as well as what kind of how tall buildings are, how tall trees are. All of them can be risks or, alternatively, not a risk can be positives based on that. So so what we've done is gone out to selected partners, and I'll just drop their names in there. So we've I'll I'll go around them all, but so GeoX do aerial attributes. So we can on any building, we've got the attributes for whether it's got solar panels on there or tarp, tarpauling on top of the, roof or air conditioning units. And, obviously, depending on which one that is, if it's tarpauling, it implies that there's a problem with the roof. So if you're an insurer, for instance, you may be less likely to want to insure them. But whether it's got solar panels, you may want to actively talk to them about their solar panels or it gives you a different risk, perspective of them. They also provide whether it's got a trampoline in the garden or a pool, whether that pool is enclosed. And if there's a tree nearby, how big is the tree? Certain areas obviously are key to know if the tree's there because if you've got a defensible space, you might want to have a bigger space between you and a tree than the case of wildfire. We've also got in the data how far you are from the neighboring company that could be there. So in the case of the chemical processing, having a house next door to it is not a particularly desirable place. It obviously depends if you you know, the risk is higher if you're next to a pub or a club or something like that. But if it's a normal factory, then you you don't mind. It's what the purpose of the thing is. And that's why Dun and Bradstreet come in. We partnered with them, and they have data on the on the actual place, the address, and that can be who is there, who is present. So in the example of you might have some vacant companies that are there, or alternatively, you the the risk of ensuring or the risk of being next to a fireworks factory, for instance, is quite high. And then we can then look at the floor space as well within there to see to work out if it's things for, like, telcos, for instance, to look at the the what Wi Fi would be needed, what fiber cabinets would be needed outside of it. Jumping back to Overture, we've linked in with GERS. So if you're using GERS, then we can use GERS to access our data. It is obviously, their their data is there. It's it's easily digestible if you kind of pass that GERS in. We can pass back the with the relevant ID from our side. One thing that kind of mentioned today, it's kind of largely forgotten sometimes is the is the road that it's on. So TomTom, we can look at street attributes there. So is it a main road to the person's or a highway, should I say, for the kind of apologies for The UK terminology. But a kind of if if you're having to leave your house and gun it up to 50 miles an hour to join a a major highway that's sitting outside, that's a different type of risk. So if you're in a a nice suburban residential, very slow road type thing that's there, we can look at peak times if you're looking at retail to see whether you should be doing deliveries at last mile. And we've also got a link in with ReGrid so we can actually look at the parcel of data that the land sits in. So we've got a view from above. We've got a view from the companies that are around it, who sits in those companies, the the size of the buildings, what type of roof it is, whether it's showing signs of, vacancy. So the bottom right there with an empty car lot and a tree overhanging it would imply that that factory is not being it's partially vacant. It's not having it's not being well maintained. So you can have a look at all of these things as well as what street it's on and also what parcel of land it sits in to get a really good view of a address and what's around it. That's been a very quick run through, and I'm gonna ask for questions in a moment if anyone's got any. But we're gonna be ongoing with different partners that will give you more information around the any any Precisely ID, and we'll be looking to even further get more information so that you can get a really good view of the data. Oh, sorry. A very good view of the address, I should say. Great. Thank you, Mark, for that. Again, this is your opportunity to ask any questions of Mark. If you'd like to raise your hand, join us on stage, or if you want to drop a question in the chat, we'll give you just a couple of seconds to do that. Mark, one of the things that that I have as new connections are rolled out, where's the best way for our customers to find those? Is it the knowledge community? Is it press releases? What's what's the best spot? There's press releases, and we've also got a data link page on our website. Or if you have any ideas of who you'd like us to partner with, I'm more than willing just drop me a note and quite happily kind of let you know if and when or what we've got in those kind of that area in those attributes. Great. Thank you. Great. That's very good. So thanks, Mark. You can drop off stage. Kyle, if you'd like to join me on the stage. Thanks, everyone. Thank you, Mark. It is my pleasure to introduce to you Kyle Bingham, who is gonna walk through Burger King use case, Burger King UK, use case. So, Kyle, take take it away. Yeah. Thanks, David. Good morning and and good afternoon. The irony is not lost on me. We have the American giving The UK case study, but, we are a global company here at Precisely. So this has not hit the press yet. So everyone on the call is getting a sneak preview of the the Burger King, UK case study. I'll go through that in a couple slides. Before I do that, I do kinda wanna walk through at a high level kind of what my team does. So I'm on the services team here at Precisely and specifically the location analytics and data team. So we provide data driven predictive analytics solutions to retailers and financial services firms. So what I like to say is we answer the business question of where. So where are my customers coming from? Where's the next best opportunity? As some of you on the call think about, you know, your your city, your town, your neighborhood, and think about your shopping patterns, where you go to the grocery store, where you dine. Someone had to make a decision to place that location within your backyard, within your neighborhood. That's where my team comes in. We take data in. We make those critical business decisions, and we help our clients, our customers make those decisions. So answering questions about, you know, how many more additional locations can the market support, Where are the next best opportunities for expansion, relocation, consolidation? What is the value of the next best opportunity? Right? Don't just tell me where to go, but tell me what is the sales I can expect at that location. So, really, the foundation of what we do and and there's a couple, screenshots on the left hand side there of Spectrum. I'll show you a larger view in a few slides here. But, really, the foundation of what we do is the data. So it's a mix of precisely data, our clients' data, and at times partner data. We're ingesting that data. We're using precisely tools to to, look at the data quality, look at geocoding, increasing the the accuracy of the data, and we're building different things with the team here, at Precisely. So there's a team of data scientists that is on my team, and we build spatial models. So taking that data and creating forecast models. We can forecast everything from sales. We have clients, in a in a very or a myriad of industries. So we've done everything from forecasting oil changes at a location, number of car washes. We've dabbled in the health industry, so number of patients at a clinic. In the insurance industry, the number of claims expected. So if we can get at the data, if you're willing to share the data with us, we can build forecast models. We also do things called opportunity scans, which are are usually a good starting point for a lot of our clients. So it's it's it's saying, here's my network. Help me understand the the growth for the next three to five years. And that's something we did for Burger King UK. So that's where we started our our engagement with them. We do catchment analysis as well. So help me understand how far my customers are coming from, where are they coming from, who are they from a demographic perspective. We look at drive time. We can look at drive distance. Mobility data is very important these days, so there's spatial datasets that we create. And we also build what's called comparable analog model models. So that's taking the existing network, having attributes attached to those locations, and then using those for comparison when you're looking at a new site. So, typically, our solutions have three kind of tiers or pieces to them. And with Burger King, they use all three of these. So on the left hand side, there's spatial insights. So spatial insights or SI is the plug in or extension that goes on top of Spectrum Spatial Analyst. So it it extends the capability to allow our models to fit into that environment and forecast things like sales potential. So it's a hosted solution. Users can manage their spatial data within that application. And, again, that's where we place our our forecast models. The precisely data, like I said, is very important. So things like demographics, traffic counts, the mobility data, store locations, That's where we kind of start our engagements, and then, obviously, the consulting services. So a lot of the folks on my team have been doing this twenty plus years. They've worked around the globe in different industries really focusing on helping clients expand their network. So a little bit of background on Burger King UK. So they're part of Restaurant Brands International or RBI. They came to us a couple years ago, and and we won the business, but they're they were looking to obviously grow their brand within The UK. So they have about 280 company owned locations and about 280 franchise locations. So a fifty fifty mix in terms of company franchisee. Restaurants are typically located in, in high street locations, shopping center locations, travel hubs, retail parks, and drive throughs. On the right hand side there, you can see the growth potential. So looking at the number of units per a 100,000 population, and there's some other, competitors in quick serve type restaurants on that list, but you can see Burger King was very low in the number of units per a 100,000 pop in The UK. So again, pretty aggressive growth plans, and they need a tool, they need a solution to quantify that growth. Right? So you can see that there's a lot of upside and a lot of room room in that market. So, again, the challenge for them was, okay. We have this aggressive growth plan. How do we quantify that? How do we qualify that? So we built them a sales forecast model that resides in Spectrum Spatial Insights. The users within The UK can go into that tool, place a site, and get a sales forecast, get an understanding of impacts on the network, right, because that's important as well. And it's and it's become a heavily used, solution for the Burger King team. So the solution, includes our data, like like I mentioned earlier. It also includes a white space analysis. So it's that more proactive approach. Tell me what neighborhoods that I need to start looking for real estate availability. So put the pins on the map, and then also give me a forecast of of what those opportunities mean in terms of sales potential. In terms of the result, they've been extremely happy with our with our solution. The forecast accuracy is is the highest they've they've ever seen it, which is critical in in site selection. One mistake in terms of open opening a new Burger King could mean, potential loss of millions of dollars. So it's critical that they get these forecasts right, that they're placing these locations in in the right spot within the market. Last slide here I wanna share with you is just a screenshot of Spectrum Spatial Insights. So there's a wealth of things you can do within Spectrum Spatial Insights. I'm just showing you a screenshot of kind of displaying, some of the points and some of the data, but there's reporting tied to this as well. So we're here just North of Downtown London. A user has placed a a potential site within the tool within Spectrum. It's using the Spectrum Geocoder, Spectrum DriveTime analysis. And, again, it allows a user to go in here and kinda manage their their portfolio and think about where they wanna go next and what it means in terms of sales potential. So very powerful. Don't have enough time today to do a full demo. If there is interest after this call, happy to set that up and and do that with, folks who are interested. Great, Kyle. Thank you very much. Did have a couple of questions come in for you. First, what kind of data is Burger King sharing with you? Great question. So first and foremost, we're always looking to start with the store data. So the the existing restaurants on the ground so we can understand what the network looks like today. And I can tell you having done this for a long time now, I have yet to have a client give us accurate data in terms of location. So that's where that that kind of early data, analysis, data, review is very important in terms of getting that those locations accurate in terms of their their lat long, their x y. And then, also, they share with us sales history. So, typically, we ask for sales history going back ten years if we can get it, but we we need that sales history to create our sales forecast models. Great. So those are those are kind of two key elements. Well, we actually have a question from Sahan, too. Is Spectrum Spatial Insights an add on to Spatial Analyst, or is it a separate module that needs to be purchased? It is an add on or extension. It basically opens up the ability for us to place models within spectrum spatial analyst. It also gives you the ability to get some more comprehensive demographic and and data reporting. So if you're interested, again, happy to set up a a demo to to kind of highlight those things. Great. Thank you very much. And, Sahen, we can touch base with you after the call as well. Thank you for the question. And one more quick question as Jeremy, if you wanna promote yourself to the stage is, what role does AI play in all this, or is there a role for it? Yeah. So so AI is definitely the top of mind these days. I was at a conference last year, and everyone was asking about AI. I don't know that anyone was doing a great job of AI quite yet. Here at Precisely, we're focusing on getting AI ready data. I think that's that's the logical starting point. The analogy I always give is if you own a car and it takes unleaded gas, you don't wanna put diesel into it. Right? And the same thing is with AI. So the AI models need AI ready data. We're we're moving in that direction. It it's it's accelerating pretty fast in this industry. I expect to see a lot kind of new things on the horizon. In fact, we're working on some things behind the scenes today as we speak. But AI is is going to, rear its head one way or the other in the next couple quarters, and I think you'll see more of that in kind of the location intelligence space. Great. Thank you, Kyle. We will have another chance for question and answers at the end of the hour today. So thank you very much, Kyle. If you have any questions for Kyle, please drop them in the Q and A chat, and we'll address them at the end. And it is my pleasure to welcome, again, Jeremy Peters, distinguished engineer here at, Precisely for his tips and tricks session. So, Jeremy, take it away. Thank you, David. Okay. I'm gonna, here, share my screen. Very good. Okay. Very good. Okay. Today's session is for tips and tricks. We're gonna cover data quality exception handling best practices, and, we'll cover these four topics. We'll first look at the foundation principles of data quality exception handling. We'll look at some key categories of exception types in the resolution pass, and then we'll get into a live spectrum demo going over some highlights of, handling exceptions using the spectrum business steward model and, handling them exceptions and using the, new, global match code coming with the GAM, the global address validation module, and GGM, the global geocoding module, and using that for identifying address exceptions. Okay. So let's for the foundation principles, effective data exception handling is the key of maintaining reliable trust and business ready data sets across an organization. At its core, trust, exception handling involves identifying data anomalies, categorizing them by severity, and determining the most efficient past resolution so that downstream consumption is not negatively impacted affected. A robust approach begins with clearly defining what constitutes an exception, whether it's whether it's a formatting with clearly whether it's formatting, mismatch, failed validation rule, missing critical attributes, or deviation from business logic. Once identified, exceptions should be triaged to a separate systemic to separate systemic defects, process driven inconsistencies, or one off data entries. A best practice exception strategy also incorporates automation wherever possible. Automated detection through rule based validation frameworks minimizes manual oversight while ensuring consistency and exception queues or work lists, allow data stewards to focus on high impact issues and configurable work, workflow to streamline escalation remediation. These practices not only reduce operational overhead, but allow but also allow to create a repeatable mechanism that improves data integrity over time. Additionally, embedding contextual information such as the source system, timestamps, rule identifiers, and recommend corrective actions accelerates diagnosis and reduces dependency on technical teams. Finally, best in class exception handling requires continuous improvement through monitoring analytics. Exception trends can reveal systemic issues worth addressing through upstream process changes, enhanced data standards, or targeted training. Heat maps and dashboards, highlighting exception frequency, severity, and, aging helps organizations allocate stewardship resources more effectively while demonstrating governance maturity. So data quality exceptions usually fall into several major categories, each requiring handling approaches to ensure efficiency and accuracy and remediation. The first structural exceptions occur when data fails to meet required format type or schema constraints. For example, invalid date formats, numeric field, numeric fields containing alphabetic characters or records exceeding length limits. These are typically the easiest to detect and often resolve through automated transformations or validation rules. Then you have, semantic exceptions, and these arise when values are valid in form but incorrect within the context of the business domain. For instance, a address may follow proper formatting rules, but may not correspond to a real physical location or a customer record may contain inconsistencies between demographic attributes. Address verification systems, external reference datasets or domain specific rules help mitigate these types of issues. Then you have logical exceptions that stem from violations of predefined business rules such as an order date appearing after a delivery date or a product code not matching, an approved list. And finally, completeness exceptions indicate attributes vital to downstream processes such as missing unique identifiers, contract information, or categorization fields, and these often require enrichment either through automated lookups or manual investigation. So the Spectrum Business Steward module provides a centralized platform for managing data quality exceptions, enabling organizations to streamline stewardship workflows and improve accountability and transparency. The module cons consolidates exception records into intuitive worklists that allows stewards to quickly identify priority and resolve issues, and these worklists can be configured based on severity, source system, business domain, or type of validation rule to allow each team to focus on the exceptions most relevant to their responsibilities. The module also supports customization of remediation workflows and provides configurable interfaces so that stewards can view detailed exception metadata, including failed validations, rule identifiers, and field level context, allowing them to take in in informed corrective actions. So let's do a demo and take a look at some of the, DSM capabilities. And we will flip over here to Spectrum Enterprise Designer, and what we'll cover is a an exception aware data flow for, you know, scalable data quality. So the basic building blocks of of of an exception management process in a spectrum data flow include building an initial data flow that performs a data quality process such as, in this case, it does customer data standardization. This data flow, we won't go into all the details here, but does misfielded data handling, incorrect data handling, does address geocoding and validation, personal name parsing and business name parsing and email normalization and standardization, as well as phone standardization and normalization. Then the second part to this implementation is an exception monitor stage that identifies records that cannot be processed that should not be processed, that could not be processed to a desired quality. So you have to determine in this case, we're gonna look at address validation and geocoding issues that we want to send for to a business steward for remediation, and we'll take a look. We've added an exception monitor stage from the business steward module from the pallet to the end of our subflow here that does all this data cleaning. And if we click on this on the stage, we can see that we have configured a rule to determine which exceptions we want to send for remediation and review by Business Steward. So we can we'll take a look at what I've configured here, And there is so we have we've won't go into all the details here, but we define the data donate domain that we're dealing with. In this case, we're dealing with address exception, so I specified address and the data quality metric that we're reviewing. And we, you know, have a list here that we can add to accuracy, completeness, consistency, and so on. And we're this is an accuracy metric that we're looking at. So we specify accuracy. We give a name to the to this particular stage, applying global geocoding exception one, and we say who we wanted to assign to to be reviewed, whether it's a particular user or business steward. In this case, I'm just assigning it to the admin, but we can assign it to a particular business steward here for review. And and we can also specify notifications, email notifications here when we have exceptions that are caught and sent to the business steward portal. And here we define our rule. So here I've defined a we'll take a look. I'll bring it up over here. And here we can I'm using GroovyScript to define a fairly your rule can be as sophisticated as you want it to be, but in this particular case, what this Groovy script code is saying, it's gonna review the global extended match code from GGM, and it's going to look at these check if the global extended match code starts with a, v four or a v four. And the v in the global match code means it's verified. It's a complete match with a single record in the reference data. So it's saying it's it's a doesn't. There's a doesn't there. If it doesn't start with a v four, that means if the record hasn't been geocoded, it's not a complete match with a match with a premise verified match. The four represents premise verified match. That means if it's not an exact match to a premise, and and then it's gonna look at the end of the code and check if the end of the code ends with a four and a five, meaning that the four and the five represent high confidence is the five, and four is moderate high confidence. So, basically, what this is saying is if our address was not premise verified and with a very with the highest confidence or the next highest confidence, then send it to the business steward to review. Now there is we'll just take a quick and then we have here from the exception monitor, it goes to we have from the port of the exception monitor, the exception port, it goes to a business steward, right exception stage, and we specify which fields that we want to send to the portal for the business steward to review. So we and and we wanna send all the fields that it would take for the business steward, all the information that the business steward would need to be able to determine, you know, if this, you know, if to be able to correct the record, give them enough information to correct the record, and also enough information that to send it back for reprocessing afterwards after he corrects the record so that it can be reprocessed using using the business steward module and sent back for reprocessing using this flow again. So we also I'll just just point out here is I was just showing a more sophisticated you know, I'm using GroovyScript sophisticated logic, but the exception monitor also gives you the ability if you if you're not doing GroovyScript. Here, we're just using an exception builder without writing the actual code. I'm just using the a exception builder to create the exception logic here. But you can only get so sophisticated with the exception builder. If you wanna make a complicated exception rule exception rules, then you might need to use the Groovy script like I was doing. In this particular case with very simple rules in this one, I'm just saying if the extended match code doesn't start with four and the extended match code doesn't end with five, then, you know, send it to the Business Steward portal. So we can now so that that we have that all configured. This is all in a reusable subflow. Now we have a a batch job, and that's gonna read in customer data with that's and it's gonna go through that subflow. It's gonna call that subflow and do all of the address verification and geocoding and name parsing and email normalization and so on with this small set of records. And we will run that, and we'll just run that. It'll just take a second to run. And we had 73 records processed. And now we can go to the, Business Steward portal. And, we'll go to the dashboard here. We'll log in. And we have, on the dashboard, some screens telling us the status, how many, exceptions have been resolved, how many are remaining, the daily progress of handling a process for the business stewards of processing those exceptions, and all the data flow stages and the exceptions coming through. And so we'll and now we'll go to the editor, and we will choose the exceptions that we just processed. So I just processed them using this data flow over here. So I'm gonna choose this data flow here. And of those 73 records, only two records did not premise verify with a high level of confidence, and those came into this business steward module. And so and we have all of the we can see all the information that we sent here, all of the address information, date of birth, the, email, all the information about the customer. And, we wanted to send in not just enough information so that we have we can figure out what was wrong with the address, but we also wanted to send in the rest of the information because we're after we correct the address here, we're gonna send it for reprocessing. So here, I'm just gonna correct one of these two addresses to start with. I will go for so I'm going to I I know that this address was mistyped. It should be 7 Brantwood Drive, or I do my research, I figure out what's wrong with the address if I can figure it out, and and then I can correct it here. And once I have my address corrected, I can correct any part of the address, and then I can save my changes and accept all, approve all my changes, and refresh. And now those changes have been approved, and now they're out of the processing queue for the business steward, and now they're ready to be reprocessed. So now we can go to our second flow. So this is the flow we just ran. Now we have an equivalent reprocessing flow, and this reprocessing flow calls the same subflow for customer data cleaning that we just ran, except it doesn't start with a read from file or read from DB reading our customer data from wherever it resides. It starts with a read exceptions from the business steward module. And here, we can see that it is reading the exceptions from that data flow from those approved records from the data flow, and we specify the the user, which business steward that we want to review records from, and which data flows we wanna review records from. We have all sorts of other settings, whether we wanna review all records, max records, and we specify the fields that we want to process and review. And we can see the preview down here, those two corrected records. And now we can just you know, we'll read in those two corrected records, run them through the data flow again, and if there are still any mistakes, they'll go back to the business steward. And if there are no mistakes, they'll all the ones that are good will go to the it's the right to file to write to a flat file. So here we can run those two records, process those two records, two records succeeded. And, if we flip back to the, business steward portal, and refresh here, there's no records to process because we corrected them, and they're all good. Very good. Back to you, Dave. Thank you very much, Jeremy. Very interesting, very comprehensive as well. So we'd like to offer opportunity for last minute questions and answers. If anybody would like to raise their hand or drop a question and answer in the chat, we'd be happy to take them to any of our presenters. Right now, we actually had one question for Mike and Mark, if you would join me on stage. So you talked about data link connecting to Spectrum, and, Mike, you talked about APIs earlier. Is that how that is connected? Is is that what we is that how we connect those two? Sorry. Yes. Can you oh, go on. Go ahead, Go. go. on, Mark. You jump in. No. I was just I just had to ask if it could be repeated the question. I was just struggling to get onto the stage. Yep. So go. ahead, And so. how does data link connect to Spectrum? Is that through one of the APIs that Mike talked about? We have data link is via data graph API that we've got. Or, alternatively, you can take the data in house. There's two two options there for connecting. Yep. So it does fit in, into that chart that I showed earlier where where, clients can, connect their spectrum directly with the data integrity suite APIs of what which one of them is called, data graph that Mark just mentioned. And the data graph will do that connectivity between those precisely IDs and the other IDs that he was talking about. Yep. Okay. Great. And you, and just to to to say it again a different way, and customers can take some or all of the data onto their own premise. And so one of the things this lends itself to the hybrid message quite well is that you can take part of the data that's most valuable, take it on onto your site, and then if there's extra connections that you need, you can hit an API. Great. Thank you very much, both of you. With that, I don't see any other questions. We still have about five minutes. I'm going to invite Amanda Lovejoy to the stage, and she's going to start the wrap up. So I'll be back at the very end to see if there's any additional questions and close this out. But Amanda is going to take us through our wrap up slides. I'm just gonna quickly take you guys through some information we would like to share with you. The first one is around precisely support plans. We have recently refreshed some of the work plans that we had in place. This is a new version of it. And the goal of it was just to ensure that you have the right level of responsiveness, technical engagement, and operational coverage for your business. If you do have any questions, our team's also happy to answer them as well. The next thing that I'd just like quickly to mention as well is that we would love for you to join our steering committee. This is a great way for you to get more engaged and also help make sure that we are providing some of the right content that is relevant to you guys. We really wanna make sure these user groups are beneficial to you and that we're providing information that would help keep you with up to date with the most relevant information. If you do want to join, you can reach out to David Lee. His email is right there. You are also welcome to you can see the share your thoughts in the top right corner of your screen. You can click on that as well, and we can follow-up with you from there. Another resource that we also have available to you is our resource center, which houses our past recordings of our events as well as our PDF that will include today's session as well as the deck that we were sharing with you today. We try to keep as relevant information on there as possible. We also would be interested in learning more about if there's any information you'd like us to add. So I am gonna go ahead and I'm going to launch a poll. You can see here it's just wanna know what kind of resources you might like to add to there, like links to technical documents, community links, precisely.com content, as well as any other content that we may not have listed in the survey. If you do have something that you wanna add, you can go ahead and you can put it in the chat because there's not really a function with the poll setting to for you to list it. So just throw it in the chat if there's anything you would like to add. I'm gonna let this sit for just a few minutes to make sure you're able to answer that. And then I will think I have, like, one more slide oh, two more slides, and then I will pass it back over to David. Let me see at the give you guys just a few more minutes, and then I can move it over. By minutes, I mean seconds. I just need to pull up this the deck real quick again. So thank you to those that did submit their answers. We will take a look and see what we can add. If there is any specific information that you want added for, like, technical documentation, again, feel free feel free to throw that in the chat. The next thing I would just like to quickly highlight is the knowledge communities. This is a great way for you to stay in touch with your peers, answer various questions that you guys might have, as well as just keep up to date on different product documentation and just other resources that might be available. If you would like to join, there is a link in the docs section of the chat. It goes chat poll, docs, Q and A. You can click on the docs and you will have a link to the knowledge communities for you to go ahead and join or at least check out. And finally, if you are fortunate enough to have a customer success manager, they are only like a call or a click away. They're happy to answer your questions, and they will get back to you as soon as they're able because they're they're here for you if you are a if you do have a customer success manager. With that, I will pass it back over to David. Thank you very much, Amanda, and thank you very much to all our presenters today, Mike, Kyle, Mark, and Jeremy. We will be back next time. You'll be seeing some invitations coming up shortly once we get the next event set up. If you have any ideas for what you'd like to cover, please email me directly, david. Leeprecisely dot com, and we'll see if we can get that covered. But on behalf of all of us here at Precisely, thank you very much for your time. We can't do what we do without you, our customers, so we're very excited to have you join us today. Thank you very much, and have a great day, and we'll talk to you next time. So long.