The Real Challenges and How to Overcome Them

1 - > Fraser Gallop: Welcome to Hard Hats and Data Chats. 2 - > This is our fifth episode, The Real Challenges and How to 3 - > Overcome Them. 4 - > So I am Fraser Gallop, and I'm here today with Steve Gross. 5 - > Good morning, Steve.

6 - > How's it going? 7 - > Quite well. 8 - > Good morning to you. 9 - > What's new and exciting?

10 - > Any new uh bike gadgets to tell us about this week? 11 - > Steve Gross: Well, no new gadgets, but some new plans. 12 - > So I'm going to be doing a gravel race in Steamboat 13 - > Springs, Colorado next month. 14 - > And I'm trying to decide if I'm going to fly and put my bike in 15 - > a case or if I'm going to drive.

16 - > So that's my big uh debate that I'm going through right now is 17 - > how I get my myself and my bike there so that uh I can 18 - > participate in this race. 19 - > So anyway. 20 - > Fraser Gallop: You see these guys traveling with bike cases. 21 - > Um I see them at least in the airport from time to time.

22 - > And I'm always like on their behalf, I'm a little bit 23 - > nervous, like knowing that the way that those guys handle our 24 - > baggage and like just like throw it, right? 25 - > And then you got this like crazy fancy bike, and and you're 26 - > you're trusting these airline guys. 27 - > So I can identify there. 28 - > Steve Gross: There's definitely risk involved.

29 - > So I've had to do I've done other races where I've flown in 30 - > and one time in uh it's another part of Colorado, and I had to 31 - > do some last-minute wrenching to get the bike put together 32 - > properly. 33 - > So hopefully that won't happen this time. 34 - > I have a better case now. 35 - > Fraser Gallop: So okay.

36 - > What about you? 37 - > I'm actually leaving tomorrow morning. 38 - > I'm going to Paris. 39 - > What?

40 - > Uh for for a bit of a family vacation. 41 - > Steve Gross: Oh my gosh. 42 - > Fraser Gallop: Yeah. 43 - > So it was uh I I was on the fence for a long time, and and 44 - > my wife is like, you know, let's go, let's go.

45 - > And so I I kind of I finally caved. 46 - > But it requires a lot of uh kind of pre-planning uh these 47 - > days uh for go for going to these cities that have like all 48 - > the different tourist attractions. 49 - > The the last time we went, we we tried to go to you know many 50 - > of the the tourist stuff like Eiffel Tower and the Louvre and 51 - > Notre Dame, and and every place had these huge lineups, and you 52 - > couldn't actually go the day of. 53 - > You have to pre-purchase your tickets ahead of time.

54 - > This makes me think of like when I was a lot younger, I 55 - > would like plan a vacation and say, um, this morning I'm going 56 - > here, this afternoon I'm going here. 57 - > But in recent years, I'm just like, ah, I'm on vacation, I'll 58 - > figure it out as I go. 59 - > And now I have to go back to this mode of kind of thinking 60 - > ahead of time. 61 - > And, you know, you got to buy the tickets ahead.

62 - > Um, I feel like this is like a holdover from COVID and you 63 - > know, these time entry windows where they just kind of put 64 - > these precautions in place and now they've kept them. 65 - > You have to really plan ahead when you go on these trips if 66 - > you want to do the tourist type stuff. 67 - > Steve Gross: Good luck with that. 68 - > That sounds fantastic.

69 - > I hope it all collides together. 70 - > Fraser Gallop: I I have I have little appetite for lines, so 71 - > we'll see how the live works out. 72 - > So today we we wanted to chat about um real challenges and how 73 - > to overcome them. 74 - > Subtext, the theme here is, you know, kind of building the 75 - > foundation for doing software deployments and and data 76 - > deployments and working with data.

77 - > And the first kind of thing that we wanted to kind of talk 78 - > about was this idea of getting buy-in from teams. 79 - > In the ERP space, maybe you could kind of tell us a little 80 - > bit about how you go in there and set up uh the deployments. 81 - > Steve Gross: Yeah, you know, it's the worst thing you can do 82 - > is uh decide you're gonna do something at the executive level 83 - > and not involve teams in it. 84 - > And uh so we we try and avoid that at all costs.

85 - > We we pull in as many of the team leads as possible as early 86 - > as possible. 87 - > And I think one of the secrets is assigning ownership to 88 - > certain sections of the project. 89 - > So in this, in this example, I mean, if if you have multiple 90 - > dashboards are going to be rolling out, you might identify 91 - > an individual that will own that dashboard and be a QA and 92 - > support vantage point for the rest of the organization. 93 - > Just early on, establish who those people are.

94 - > And then secondly, you know, the vision, the corporate vision 95 - > that's driving this, everybody, this team that you're 96 - > assembling, they need to be disciples of that. 97 - > They need to totally embrace that idea and and believe in it, 98 - > you know. 99 - > So, so just activities all surrounding getting them on 100 - > board with that kind of an approach is really well worth 101 - > it. 102 - > Time invested is well worth it.

103 - > Fraser Gallop: Well, especially when you're doing an ERP type 104 - > deployment, it's not like if it fails, you can necessarily go 105 - > back to the old way of things. 106 - > You're preparing the whole organization for this shift to 107 - > happen. 108 - > Right. 109 - > And if there's a goaline date, it's gonna happen on that date.

110 - > Um, so you you kind of have to get all the ducks in order and 111 - > stuff to be able to make that happen, right? 112 - > Steve Gross: Yeah, where this can be this is a little more 113 - > difficult in that it it whether or not you use the product we're 114 - > describing here at dashboard, some may view that as optional, 115 - > you know, and and it uh it's not as cut and dry. 116 - > There, I would think there would be a much more importance 117 - > and in that upfront effort to get everybody bought in and and 118 - > sold on the idea.

119 - > Fraser Gallop: Yeah. 120 - > When we when we started working with Tableau many years ago, 121 - > they were still in kind of startup mode as a company. 122 - > And their thing that they like to say was land and expand, 123 - > where they would just sell one license to an analyst, rely on 124 - > that analyst to get other people on board and expand within the 125 - > organization. 126 - > But it it's harder to do that because that analyst is not 127 - > necessarily a decision maker for the entire organization.

128 - > We still think that the better way to go is to get the 129 - > executive and the C-suite engaged and on board with these 130 - > projects at the start, because they're going to basically be 131 - > the sponsors, be the key people that are going to push the rest 132 - > of the organization to kind of follow through and be part of 133 - > the process. 134 - > So getting the teams on board was the first piece. 135 - > The second piece that I think for building that strong 136 - > foundation is cleaning and standardizing the data.

137 - > There's a lot of different kind of questions that you ask. 138 - > I think they're the same questions when you're deploying 139 - > an ERP, when you're setting up a data warehouse, you may have 140 - > years and years of history in in existing systems, and you don't 141 - > necessarily want to bring that with you as you go. 142 - > What are some of the things that you guys ask in in the 143 - > questions that kind of you would go through when you're doing a 144 - > migration from one system to another and questions about the 145 - > data that you would want to ask and establish before you kind of 146 - > go down that path?

147 - > Steve Gross: Well, one of the obvious ones is duplication, 148 - > duplicate vendors, duplicate customers, just whatever your 149 - > database is that you're talking about is is some something 150 - > represented multiple times. 151 - > Getting that cleaned up. 152 - > Because like in ER ERP implementations, you know, this 153 - > kind of a project, if your data is not in good shape, it's going 154 - > to show up pretty, pretty predominantly when you uh view 155 - > it in some sort of a graphic or a some sort of a report.

156 - > You want you want the data to be cleaned up so that it's you 157 - > can get the impact that you're looking for and the results that 158 - > you're looking for. 159 - > So another, you know, basic step that's well worth the 160 - > effort is the cleanup involved to get your data in good shape. 161 - > Fraser Gallop: Yeah, the one that we get and we see quite 162 - > often is that this idea of who are my top customers? 163 - > And executives know who their top customers are, but you go 164 - > and you connect up to data, and lots of times you won't see the 165 - > customer that you expect as the top customer because it's 166 - > actually split into maybe five different companies, because 167 - > they have different locations, they want you to bill slightly 168 - > differently, but it's actually all the same customer.

169 - > So one of the tasks is sometimes identifying those 170 - > hierarchical hierarchical, if I can say that properly, 171 - > relationships. 172 - > So like all those customers tie together under one line, it 173 - > does reflect that they are my best customer because you know, 174 - > we've actually got it split out into different places. 175 - > And you can do that drill down and see how that distribution 176 - > works, but identifying those relationships is kind of key.

177 - > One of the other ones that we get when we're looking at 178 - > setting up data warehouses also how many, how many years of data 179 - > do you want to have? 180 - > Uh that's a good one too. 181 - > Like what do you kind of typically migrate if you're 182 - > doing a migration? 183 - > Do you only look at five years of data?

184 - > Is there a rule of thumb or is it different every time? 185 - > Steve Gross: Well, that's a good question. 186 - > Because, you know, it it actually adds some difficulty in 187 - > migrating data if you're filtering out past a certain 188 - > date. 189 - > I mean, there's just more to that.

190 - > But it's also a very good consideration because older data 191 - > tends to be less organized. 192 - > You know, this whole what we were talking about a minute ago, 193 - > if you go back 10 years, you know, there may have been some 194 - > bad practices in play at that point that makes that data less 195 - > useful. 196 - > So, I mean, it uh that's very astute. 197 - > You know, you need to really zero in and understand the 198 - > history of how that customer got there, that client got there, 199 - > and the changes they've gone through in their in their 200 - > capture of data and their record keeping, and you know, really 201 - > zero in on what's relevant that will help the readers of this 202 - > output in making decisions and so on.

203 - > If it's garbage, it's it's not going to help that much. 204 - > It'll just skew things. 205 - > So that's a really good point in terms of the age of the data, 206 - > what makes sense there. 207 - > Fraser Gallop: Yeah.

208 - > So cleaning, cleaning and standardizing data is a 209 - > must-task to build, again, back to building the foundation. 210 - > The other thing that we usually talk about a little bit when we 211 - > get started is what are your KPIs the organization uses? 212 - > Building kind of data projects, and you know, if we think about 213 - > like the first project is going to be our executive dashboard. 214 - > The executives have a one-stop shop, they can kind of see 215 - > everything on their executive dashboard.

216 - > It's not necessarily just regurgitating the income 217 - > statement and the balance sheet and giving you that. 218 - > You've got to go a little bit further. 219 - > So when we talk about training, you know, data analysts, the 220 - > thing that we tell them to do is to always ask what questions 221 - > are you trying to answer with this data? 222 - > Don't tell me which columns and rows you're looking for in the 223 - > report.

224 - > Tell me what kind of questions you're trying to answer. 225 - > So you're trying to calculate DSO and DPO from the accounts 226 - > receivable data. 227 - > We don't need to know what rows and columns you're interested 228 - > in. 229 - > We need to know what that end goal is.

230 - > Because oftentimes you'll see where people build a report that 231 - > just has stuff from your income statement, your balance sheet, 232 - > and then they just export that into Excel and then they do 233 - > those manipulations over and over again every month. 234 - > If we can capture what are you doing in Excel after you export 235 - > the data, that's how we can provide the best value because 236 - > we can just give you those numbers directly. 237 - > We can automate the piece of exporting and calculating these 238 - > things over and over, doing those pivot tables so we can 239 - > give you that end result directly.

240 - > So your KPI is actually that end result, not all the work 241 - > that you had to do to get there. 242 - > What are some of the other things that you might do when 243 - > looking at KPIs and trying to identify that? 244 - > Steve Gross: Well, earlier we were talking about the 245 - > importance of getting the right buy-in, but there's another side 246 - > to that, and that is getting the right person that 247 - > understands the business and where the exposure is. 248 - > Uh, and to help you with what those KPIs are, you know, within 249 - > construction, the different vertical markets, the different 250 - > types of contractors, that can change drastically.

251 - > Like, for example, a mechanical contractor, very labor-intense. 252 - > What I've learned from some customers in that niche that 253 - > I've dealt with is that the name of the game is understanding 254 - > how much labor you need. 255 - > Not only from the perspective of being able to carry out the 256 - > projects that you've won, but also in terms of keeping your 257 - > talented team busy. 258 - > What is that benchmark you need to reach to keep your workforce 259 - > out there productive?

260 - > And at the same time, don't overcommit, don't sell so much 261 - > work that you don't have the resources available and you have 262 - > to start hiring temporary contractors, which eats up your 263 - > marger quicker than anything. 264 - > So a lot of forecasting into labor requirements. 265 - > And then if you're in heavy highway, it's material 266 - > requirements, equipment requirements, that sort of 267 - > thing. 268 - > You need some guru to help grow the business, help you identify 269 - > what those KPIs are.

270 - > And you know, if you can do that, then the dashboards really 271 - > are wonderful. 272 - > Fraser Gallop: You know, they make a that domain expert that 273 - > has the years of experience and trying to tap into that. 274 - > Exactly. 275 - > Use that.

276 - > Yeah. 277 - > Steve Gross: It may or may not be somebody in the finance team. 278 - > They may have come in from another industry. 279 - > You know, you need that, you know, that person that grew the 280 - > business that understands, you know, where the exposure really 281 - > is.

282 - > Fraser Gallop: Before they retire, right? 283 - > Yes. 284 - > Yeah. 285 - > The next thing I wanted to mention on here, too, getting 286 - > into this idea of integrating systems effectively, what are 287 - > you doing in Excel after you export the data?

288 - > There may be a systems impact there because they're doing some 289 - > manipulation of the data to get the result that they want. 290 - > That's where like we can look at that and we can say, well, 291 - > maybe you need to use a custom field in your database to 292 - > capture this at the source rather than applying it in Excel 293 - > after you've done the export every time. 294 - > Well, our paths have crossed many times in the past. 295 - > And I don't know if it was you on a call or somebody else, but 296 - > we were talking about custom fields, and they actually showed 297 - > us how there actually wasn't a limit on the custom field.

298 - > You could use more and more custom fields, and there was a 299 - > way to accomplish that that was part of the database. 300 - > And so we were just amazed and we thought, that's great. 301 - > Now we can start putting all of this into the source database. 302 - > We can use that in the reporting.

303 - > You just have to change the process a little bit, but then 304 - > everything's gonna flow a lot more smoothly because we've got 305 - > the data in the right systems, we've got that integrated 306 - > nicely. 307 - > Steve Gross: Developing that attribute, you know, and making 308 - > it available in the reporting is so important. 309 - > And the good news is usually you can do that. 310 - > Fraser Gallop: That's usually not a, I mean, as long as you 311 - > identify it's and it's yeah, it's really it's not that hard.

312 - > It's just getting everyone aligned, getting the right 313 - > teams, getting them aligned. 314 - > In terms of integrating systems, we we talk a lot more 315 - > these days about setting up data warehouses and setting up 316 - > semantic layers. 317 - > The reason that we're doing that, especially today, it is to 318 - > kind of get the data ready for AI. 319 - > Because one of the things that we're not doing a podcast about 320 - > AI, we're doing a podcast about data.

321 - > But the idea is that if your data is nice and clean and 322 - > you've kind of built this data warehouse, you've gone to the 323 - > troubles that cut it up nicely, you're going to be a lot more 324 - > ready for an AI deployment. 325 - > Taking raw database table names and column names that are not 326 - > human readable, gibberish most of the times, and translating 327 - > that to something that the AI can understand so that it has 328 - > more meaning. 329 - > One of the great examples that I've recently been playing with 330 - > is this idea of pulse metrics.

331 - > So you have a data source that has data coming into it every 332 - > day. 333 - > You know, it could be like time cards, for example, and you can 334 - > hook up these pulse metrics and get the daily report to see how 335 - > you're tracking this month versus last month, what the 336 - > forecasted number of hours are going to be to month end. 337 - > Those tools are getting way better than they were just a 338 - > couple of years ago. 339 - > And uh what I saw is the one that I use it now has an AI 340 - > connection.

341 - > So I can just ask the LLM questions about the data in 342 - > natural language. 343 - > And I can say things like, I don't have to say username. 344 - > That's the, you know, it the field in the data set is 345 - > username, but I can say which person or you know which team 346 - > member. 347 - > And then the AI can make that connection for me to say, oh, 348 - > I'm actually talking about username and give me information 349 - > about the user.

350 - > A time saver. 351 - > Yeah, it's it's huge. 352 - > And because the data was kind of set up correctly in the first 353 - > place, once those AI features got turned on, I could just use 354 - > them. 355 - > I didn't have to do any additional setup.

356 - > Um so that's um a lot of this is setting up things, thinking 357 - > to the future, and and we're trying to get it right so that 358 - > we don't have to do rework uh later on. 359 - > Steve Gross: That's a really that's a really neat insight. 360 - > I mean, it it's kind of that's just the next level of cleaning 361 - > up your data, really, is making sure it lives within these 362 - > parameters so that machine language can run with it later 363 - > on, you know. 364 - > I mean, that's that's kind of probably the essence of learning 365 - > to work with AI, you know, is understanding that.

366 - > Fraser Gallop: Yeah. 367 - > Um, and so talking about AI, that's another great uh segue 368 - > here. 369 - > Because then the next thing that I wanted to talk about was 370 - > this idea of building trust in the data. 371 - > We are gonna build something, we're gonna deploy something.

372 - > Training is definitely part of that and getting the teams on 373 - > board to use them, but uh it's also building trust. 374 - > How do you guys typically approach rollout plans when 375 - > you're doing appointments? 376 - > Steve Gross: We make uh put a lot of emphasis in uh in a 377 - > sandbox approach, uh allowing them to do the proper testing. 378 - > They're they're not really testing software, they're 379 - > testing the process and and their workflow and to make sure 380 - > they're that we've accounted for the exceptions, you know, and 381 - > and also it's familiarization.

382 - > Half of training is familiarization. 383 - > You can go through a bunch of classes, but if you don't 384 - > actually sit down and use it, you know, day one, go live is 385 - > going to be scary for you. 386 - > So it's to you know, alleviate that stress of the newness of a 387 - > of a new system and a new workflow. 388 - > Well, you know, in that same vein, this kind of a project 389 - > lends itself to that as well.

390 - > I mean, to be able to understand the drill downs and 391 - > just conceptually what you're looking at, if you can mock that 392 - > up in advance using some historical data and use that as 393 - > a training tool, wow. 394 - > Those people are going to be, number one, much more likely to 395 - > use the thing and number two to understand what they see. 396 - > Fraser Gallop: So yeah. 397 - > Well, and you have to have that go through the basics, right?

398 - > Like how do you connect? 399 - > And I kind of feel I'll just assume people know how to go on 400 - > the web and log into this tool and access these things. 401 - > And that's not always the case, right? 402 - > There's such a wide variety of skill levels and experiences 403 - > that that are out there.

404 - > You do have to have that rollout plan and kind of go 405 - > through it. 406 - > Um one of the examples that I had about building trust in the 407 - > data is having a place where you can explain what are the data, 408 - > where's the data coming from? 409 - > What are what are some of the calculations and the formulas 410 - > that are in use? 411 - > A new user doesn't need to see that every day, but they need to 412 - > have like a reference material in terms of things like data 413 - > refreshes.

414 - > The traditional thing that we would have is date stamp. 415 - > If we were going to PDF a report and start distributing 416 - > that to people, we would want to have date stamp on there to 417 - > say, when was this report generated? 418 - > Um it's the same thing in in dashboards, right? 419 - > Like you want to have when was this data refreshed?

420 - > When when is this data coming? 421 - > Where did it come from? 422 - > Steve Gross: Very important in construction. 423 - > I mean, it's such a timely business.

424 - > You know, it's it's a it's a business where the, you know, 425 - > it's it's you have to be able to react quick enough to fix stuff 426 - > before it's too late. 427 - > Fraser Gallop: Yeah. 428 - > And if you're looking at last month's export, you're probably 429 - > too late already. 430 - > The other one that we did was we we had a an executive 431 - > dashboard that we were building that had maybe five different 432 - > data sources that that were kind of coming into it, like all 433 - > these different data feeds.

434 - > And the challenge that we had was that they they just want to 435 - > know is the is the data good or is there a potential problem? 436 - > And it it actually becomes, you know, a difficult question when 437 - > you have a bunch of systems that you're consolidating 438 - > together. 439 - > What if your your your job cost data is correct, but then your 440 - > payroll data is behind? 441 - > So we actually made just a little indicator.

442 - > Um, it was just a green and yellow indicator. 443 - > So if all the data had refreshed like we expected, we 444 - > gave it a little green dot on the dashboard. 445 - > But if one of those data sources was not, we we would 446 - > show it as yellow. 447 - > And then they could click through to see what was 448 - > successful, what was unsuccessful, and kind of make 449 - > that decision on the data.

450 - > Um trying to distill it to just that little indicator in the 451 - > corner so it's not overwhelming, it's not in your face where 452 - > you're trying to get your data, but you have that little 453 - > assurance that you can trust that everything's working like 454 - > it should be. 455 - > Steve Gross: I bet that really helped. 456 - > Fraser Gallop: Yeah. 457 - > Steve Gross: You know, another thing on on training, going back 458 - > to that, I was thinking of that I think would really be 459 - > valuable is you have to, I think, once you roll roll 460 - > something like this out, it has to become integral to the review 461 - > process within your company.

462 - > You know, and so like if if I'm managing work for a company, 463 - > projects, inevitably I'm gonna be sitting in some sort of 464 - > review meeting on a monthly basis going through my jobs. 465 - > Why not, you know, and a lot of our customers they have some 466 - > key report that they use to drive that meeting. 467 - > They're reviewing this report. 468 - > Why not make it the dashboard?

469 - > Start with the dashboard, use that as a segue or a drill point 470 - > into the details of the project and make the people use it. 471 - > Make those users, you know, explain things that you see on 472 - > the on the dashboard. 473 - > And that that is what makes it what's the word I want to use? 474 - > It's it's just it's it's commonplace to use that tool.

475 - > Fraser Gallop: It's a it's a generally accepted tool when 476 - > you're as a focal point for for the like the meetings and the 477 - > discussions, yeah. 478 - > Yeah. 479 - > Even internally, we try to practice what we preach, right? 480 - > And so when we have review meetings, I have one coming up 481 - > here in in a couple hours, like a monthly review.

482 - > It's actually bring up dashboards and go through the 483 - > dashboards. 484 - > We're not necessarily going and running the reports ahead of 485 - > time during the meeting. 486 - > We can just come and review the dashboard, see how things are 487 - > going, point out the problem items and discuss them. 488 - > So yeah, you're you're a hundred percent there.

489 - > Um the other thing about overcoming challenges and moving 490 - > forward is this idea that we won't get it right the first 491 - > time. 492 - > So it's it's gonna take more than one shot. 493 - > We're gonna have to iterate to get it right. 494 - > If you're having those regular meetings and whoever's working 495 - > on dashboards and reports as part of those meetings, they can 496 - > hear from the users to say, This is working really well for 497 - > me.

498 - > Um, this is not so much. 499 - > Can we swap this out and put something else here that would 500 - > be more useful? 501 - > Take that communication to know whether you're getting it right 502 - > or not. 503 - > I like to say if we're not.

504 - > Hearing from the end users, then that means we we've either 505 - > got it 100%, everything is working great, or they're not 506 - > using it at all. 507 - > Right. 508 - > And it's like, how do you how do you know the difference 509 - > between the two? 510 - > We can go and look at server logs, mine that information and 511 - > see if if people are clicking on things, but it is really 512 - > important to have those conversations that are ongoing 513 - > to say, yeah, you know, what's working well, what's not working 514 - > well, um, how can we iterate, how can we make it better.

515 - > Steve Gross: Um well, I think that also speaks to the 516 - > importance of not trying to pull off too much at once and to 517 - > make it make your project winnable, make make it something 518 - > that you can really succeed at. 519 - > You know, you get this much done, they're using it for this 520 - > reason. 521 - > Okay, now we're gonna phase in this. 522 - > You know, it's it's it's a step-by-step process, it really 523 - > is.

524 - > And uh otherwise, you know, there's a pretty, pretty high 525 - > risk of something not clicking right and and or something not 526 - > being rolled out properly, and that no one uses it. 527 - > You know, that's and and like like we were saying earlier, you 528 - > know, it's not uh you know, these things can be optional if 529 - > if you allow it to be, whether or not they're used or not, can 530 - > be optional. 531 - > And that, you know, first of all, you gotta structure it so 532 - > it's not optional, and secondly, you gotta structure it so that 533 - > what you do roll out is you know, smash and success and 534 - > everybody likes it.

535 - > And you know, that's that's the goal, of course, is to get that 536 - > get that um readership and that usage level high. 537 - > Fraser Gallop: Yeah, exactly. 538 - > Get the minimum viable product and then improve upon it. 539 - > Um requirements later on.

540 - > Yeah, it's it's that's what I'm trying to say. 541 - > Steve Gross: You know, something achievable, you know, make it 542 - > don't kid yourself on trying to do too much at once. 543 - > Fraser Gallop: Yeah. 544 - > So that's the big takeaway from today to overcome challenges 545 - > and deployments and trying to roll out software and introduce 546 - > new things, it's coming from aligning people, process, and 547 - > technology.

548 - > All three are are equal parts of the puzzle. 549 - > It's not just buying the technology and putting it out 550 - > there. 551 - > Um, it's it's not gonna succeed on its own necessarily. 552 - > Uh, next time we're gonna talk about what good looks like.

553 - > I think it's gonna be a bit of a challenge. 554 - > We titled the episode What Good Looks Like, but of course it's 555 - > not necessarily a video podcast, it's just a podcast. 556 - > So we'll have to be very descriptive when we talk about 557 - > this next time because we won't be able to show and tell 558 - > necessarily. 559 - > In the next couple months, we have a user conference.

560 - > We're gonna see each other in person at that. 561 - > So we'll look forward to that. 562 - > The other thing is uh before the next episode lands, I'm also 563 - > gonna be presenting my Tableau conference session. 564 - > So earlier this month, I was presenting a session on building 565 - > KPI dashboards for company leaders.

566 - > On June 9th, I'll be presenting that to a Team Data Fam Tableau 567 - > user group. 568 - > I'll put a link in the show notes if people are interested 569 - > in seeing that. 570 - > Otherwise, we will uh meet again in a couple weeks here for 571 - > what good looks like. 572 - > So if you haven't subscribed, please subscribe now and we'll 573 - > talk to you again soon.

574 - > Uh Steve, enjoy your bike race. 575 - > I'm I'm interested to see what the results are next time we 576 - > chat. 577 - > Steve Gross: We have to set the expectations properly. 578 - > Finishing might be my expectation.

579 - > Fraser Gallop: That's that's if if that's the goal, then then 580 - > that's the goal, right? 581 - > As long as and having fun and and you know just getting it 582 - > done is is is also like big, right? 583 - > All right. 584 - > Have a good weekend and we'll we'll talk again soon.

585 - > See ya. 586 - > Steve Gross: Bye. 587 - > Fraser Gallop: Bye.