 Oh, and welcome. My name is Shannon Kemp and I am the Executive Editor of Data Diversity. We'd like to thank you for joining this Data Diversity Webinar, Self-Service Data Analytics from Spreadsheets to Work Close, sponsored by Alteryx, just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we'll be collecting them via the Q&A in the bottom right-hand corner of your screen. If you'd like to tweet, we encourage you to share highlights and questions via Twitter using hashtag Data Diversity. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper right for that feature. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now, let me introduce two of our speakers for today, Matt Madden, Director of Product Marketing at Alteryx and Chad Martin, Solutions Engineer at Alteryx. Matt has over 17 years of experience in the analytics and enterprise software industry. In his career, he has held roles in sales and marketing both with the same goal to help organizations realize the power and benefits that analytics can have on their business. Chad has been working with the Alteryx software for over nine years today. He has been in the industry for almost just as much time working his way through being a data wrangler, content producer, client services engineer, and finally solutions engineer. Every day, he is still amazed at how much the software truly helps clients achieve factory results quicker insight, all while having fun doing with that. And with that, I will go to the floor to Matt to get us today's webinar started. Hello and welcome. Thank you, Shannon. And thank you, everyone, for taking time out of your day to attend this webinar, self-service data analytics from spreadsheets to workflow. So before we get started, let's kind of go over what we're briefly going to cover in this webinar. So I want to give people a perspective of who we are at Alteryx, what it is we do, and then we'll dive right into the content overall. We'll talk a little bit about spreadsheets. We'll talk about the benefits of a workflow, working hand-in-hand with spreadsheets as well as replacing some of the different processes and tasks that people are currently doing using spreadsheets. And then we'll have an interactive demonstration led by Chad Martin here. For those of you that want to follow along during that demonstration, you should have received a link to our trial download as well as a link to the sample data. If you did not, don't worry. One, this webinar will be recorded, so we'll be able to send that out to you if you want to follow along. Two, if you did get the trial downloaded, we do have a link to the sample data in the chat menu on the right-hand side. So feel free to follow along with any of that data if you feel like it. Okay? So who are we? Who is Alteryx? Alteryx is the leading platform for self-service data analytics. We provide analysts with a unique ability to easily prep, blend, and analyze all of your data using a repeatable workflow. Then you can deploy and share these analytics at scale for deeper insights in hours and not weeks. We give you the ability to easily connect and cleanse that data from virtually any source. You'll see here on the left-hand side some sampled data sources that you're able to access. Connect and then cleanse and prep that data. Join it together and enrich it using some third-party data that we have available packaged with our software. Analyze that data using a wide range of different techniques such as statistical, predictive, and spatial analysis. And then share the results to a wide range of data sources and capabilities, including analytic application capabilities, output to visualization tools like Tableau will click Microsoft Power BI and more. So the purpose of this webinar, as you know, is to really give you a sense of the benefits of using a workflow versus a spreadsheet for data analysis. Now spreadsheets are at the core of almost all businesses. They're never going to replace, never going to be replaced. We're well aware of that you as users are well aware of that as well. The purpose here is really to highlight how a workflow process can both augment and help the many processes and tasks that you may struggle with when using a spreadsheet. Now before we go into that kind of detail, let's look at some of brief information on why people use spreadsheets or in many cases try to use spreadsheets and where there tends to be issues. You know, the first and foremost reason people tend to use spreadsheets is because they're familiar with them. And second of all, in most cases, it is in a certain way free to them or low cost. What people like about spreadsheets is that you can instantly see changes to the data you make. You have an idea of what the data is going to look like instantly. And then typically it will cover most of what analysts need to do in their day-to-day work or activities. Now there can be issues when working with spreadsheets as many of you know. In reality, spreadsheets are really better for working with one to maybe two different data sources, whether that CSV or an Excel file or when we talk about two data sources it might mean two worksheets, et cetera. It doesn't mean you can't work with more than one or two different data sources. But when you start to work with more than these two different types or two different data sources that's when you can run into trouble both from a traceability standpoint but also from a scalability standpoint. Many times spreadsheets have limitations in terms of the size of data they can work with or they can work with it but running these different processes can be time consuming and long. The other thing to take into consideration are collaboration. This can tend to be a nightmare. In fact, we had one analyst that we worked with in the past that tell us that in reality no one really trusts each other's spreadsheets and to determine what somebody else did, where or what other worksheets they're referencing, how it's tied back to a formula here, a formula there can be very tedious. And then the other thing to take into consideration is what happens when you want to do the same type of data manipulation or process over and over again. You may have to create a macro or write a visual basic, et cetera. OK, so let's talk a little bit about how a workflow can help overcome some of these challenges. I mean the first thing we always talk about when you look at a workflow process is it's transparent. You can easily understand what's taking place throughout each step of that analytic process. And in many cases, many workflow environments, there are different icons or tools that people can look at to have a sense of what's going on at each step of that process. And in our case, at Altrick's case, there are a few other workflow tools that do as well. These different types of tools are color coded to reflect the type of processing that's taking place. So it could be a data preparation process, a data transformation process, a joining or a union process, those types of things. Now this self-documenting process can allow others to really utilize someone else's workflow with ease because they will easily understand what is taking place at each step. And then in terms of visualizing the data with spreadsheets, you can instantly see what's going to happen to that data. Workflow is virtually the same. The difference is going to be you basically run that workflow at any step of the process to see what changes the data makes and what it's going to look like. Then if you're building out a workflow and you notice an error, if you do something like that in a spreadsheet, you may have to start that whole process over. Using a workflow process, if you notice an error, you can basically just go back to that point in the workflow, make the change, not have to start over. In many cases, a large part of your job might involve building out a repeatable process where you have to prepare and update data in the same format, produce the same reports or tasks. This can be time-consuming exercise. That's anything but value added. Now while spreadsheets allow for some automation to simplify this repetition, in many cases you're going to need to create complicated macros or visual basic scripts again, which also means more manual coding and less time to do some sort of analysis. Now what a workflow environment can do is it can free you from these tedious tasks of producing these same reports over and over again by allowing you to build out that analytic process once and then use it again and again when the data changes. I think the best part about this is you can do all of this in a drag and drop environment that doesn't really require any sort of SQL code or complex coding or scripting. In fact, you can even automate that process to run on a scheduled basis, whether it's a certain time a day, a certain week or a certain month. Now, many of these things that we talk about today from workflow process, they are easier to do in Excel in some instances. But I think the repeatability aspect of this is something to really highlight here. Once you build out this process, it may take a little bit longer to do it from the first standpoint, but once you build out this process, running it over and over again saves you minutes to hours to days to weeks is what we hear from customers every single day about building out a repeatable workflow process. And then the last thing I want to touch on is once you've manipulated that data and produced the data set that you want, what do you do next? Automating a process like this, like we said, can require some coding, building a macros, et cetera. But what if you want to do something more with that data, making it easier for decision makers, for instance, to get the analysis of reports they need? This again may require you do some visual basic macro on the front end of a spreadsheet and then share it to other users or decision makers. Now, with a workflow process, in particularly one like Altrix, you can take that process, that workflow process, and wrap it up into what we call an analytic application. And that will allow a business decision maker, or any user for that matter, to access the report, the data, the analysis, anything they need through a browser-based environment. And to get the answers they need, we'll just require them to access this analytic application, click a few buttons, and get the report. And then finally, analytic flexibility. Spreadsheets are great for basic analysis and calculations. But what if you happen to need to do more analysis on top of that, some more advanced analytics? A workflow can incorporate these advanced analytics like statistics, predictive analysis, and spatial analysis all within that same analytic workflow. So that gives you a little baseline around some of the benefits that a workflow can bring over a spreadsheet. Now I want to turn it over to Chad to really show you how a process can be built out and some of the steps and different types of functionality that a workflow can have for them. Chad? Great. Thanks a lot, Matt. Can you hear me OK? Yes, we can. All right, great. Paul, there you go. Thank you very much. OK, sharing my screen now. So just to kind of recap a little bit of what Matt had said is, Altrix is essentially what we call a workflow construction environment. And really what that means is we try to give you an easy and intuitive way to connect to your data, perform various data analysis tasks in advanced analytics, and then ultimately output that back to any other data source. And so primarily whenever you're trying to augment maybe some Excel spreadsheet, this can be a really, really big benefit. So for those of you who have not seen Altrix yet, we're essentially looking at the UI. And it really consists of just a few key pieces. So the first of which is just the actual canvas right here where it says drop tools here. And this is where we're actually going to build out this workflow. And so you'll see these workflows start to take shape as we drag these tools, the second piece up here, down to the canvas. And so these tools are broken up into different categories. So you have your categories for input output, for preparation, such as filtering out records, adding unique identifiers, sampling your data, sorting your data, and so forth, all the way over to a join category. So if we're actually joining or blending data, unioning data together, parsing and transformation. Specifically with transformation, this is something that we're going to see a couple of examples of in today's lesson where we're going to be transposing data and cross-tabbing data. And so these two specific functions are really common in Excel. And so we'd like to show you how Alteryx can actually augment that quite a bit. So if we wanted to connect to a data source, what we would do is literally take an input data tool and drag it and drop it onto the canvas. Now once I do this, you'll notice that the configuration window here, this third piece of the puzzle of the UI on the left-hand side, changes. Now this is going to change for every tool you click on. Now the idea here is that every time you click on one of these tools, that muscle memory kind of kicks in. You know, I click on a tool and I can go configure that tool on the left-hand side. So in order to configure that tool, all I have to do in this particular case is browse to a data source. In this case, let's just browse to this yogurt sales data and click OK. Now what we'll see as I close these down is a quick sample of my data within Alteryx. Now this is going to be similar to what you will actually see in Excel. And then finally, if we want to visualize the entire data set, all we have to do is run the workflow and we'll see the data down at the bottom in our results window. Now I'm going to build a little bit of context around this. And so ultimately, what we're looking at here is a series of yogurt pricing data for several brands across several grocery store chains. And so let's say, for example's sake, we have two different Excel spreadsheets. One is this file. And the next is actually weekly sales figures for one single yogurt brand, in this case it's going to be Chobani, across several grocery store chains as well. The issue here is that the data isn't really in a good format for us to perform some of these advanced analytics pieces that we're trying to do. So ultimately, our objective is to look at this data and try to investigate the relationships between sales and pricing between the one yogurt brand, Chobani, and the pricing for all of its competing brands as well. And ultimately, see if we can try to understand how that sales and pricing information are related to each other for those different brands across different dates, different grocery store chains, and so forth. So the first thing we need to do is actually look at our data. So what I'm going to do is I'm going to actually open these up in Excel. So let's open up sales data and let's open up the pricing data. So if we open the sales data, we can see that we have a pretty standard spreadsheet. I can see I've got a little label here, labeled as weak ending. So I can see the different store brands that we have here. I can see all of the different measurements for all of the different summarizations or pricing information or unit information, depending on what measurement we're looking at, for each week. And in the next spreadsheet, we can see different pricing information for different grocery store chains, such as ALB, a whole USA, A&P, Banner, TA, and so forth, all the way down the line, and ultimately find out what all of these different data pieces are that are associated with each of these weeks for each of these grocery store chains for each of the different yogurt brands. So looking at these, we think, OK, if we want to try to run some sort of analysis on this, we first need to get this data together into the same data set. Well, traditionally, we might have to do some manipulation here in Excel, but what I'd like to do is actually go into Altrix. I'm going to actually close out my spreadsheet so I don't run into a conflict of the spreadsheet being opened. And I'll go right back into my input data tool. So I can see here that the first piece that we're looking at here is the yogurt sales data, specifically for Giovanni. But thanks to the way that some things are kind of separated out in Excel, we don't actually have field headers on any of my data. So what I'd like to do is do a little bit of preparation here. The first thing that I can see is I've got some specific options for my input data source here, one of which is to say, OK, well, the first row contains data. I can see that I've actually got a little data point here called week ending. So if I like, I can actually check the box for first row contains data and update my sample and see that I now have week ending as a data point. Now in doing that, I'm going to actually remove that value as well. So what I'd like to do is take a sample tool and drag this to my canvas. You'll notice a couple of different things have happened here, one of which we have custom configuration settings, specifically for that sample tool. But also, we've got this nice connection here between our input data tool and the sample tool. Now, this is where we're going to start to see that workflow take shape. And that real visual representation of what's actually happening to our data. So all of that data processing, all of that self-service analytics that we're performing here, this is that visual representation of all of those tasks. So in this case, what we're seeing is that we've got input data. It's flowing into the sample data tool where I'm actually going to tell the tool to skip the first one record. And then I'm going to rerun. And when I do this, I can see that, OK, now I've got all of these nice field names as my first row of data. So what I'd like to do is actually move these up one. So I'd like to actually dynamically turn these into my actual field headers. Well, there's a special tool in our developer tool category called dynamic rename. What I'm going to do is actually go directly to our tool search bar and type in dynamic. And I can see that I've got my tool for dynamic rename right here. So once I do this, I can actually drag this down. And one of the options for this is to say, take the field names from the first row of data. So once I select that, I can have this automatically by default, select all of the existing 54 fields that we have, and then click Run. Now you'll see that I've got a nicely prepped data stream with my store brand and all of the different measurements and all of the different values for each of those measurements broken out by week. OK, so now I've got one data stream in here. The next thing is, OK, well, let's bring in that next data source. So in this case, if you think back to our Excel spreadsheet, we've got that pricing data for multiple brands across all of the different grocery store chains by week as well. So what I'm going to do, just like I did before, is drag in my input data source, click on File Browse, and go to my yogurt pricing data. Now once I do this, I can now see, again, a quick preview of the data where I can see I've got average or Chabania sales information, standard sales information, Yo-Play sales information, and private label sales information. I can also see that the format of this is a little bit different than what we had with our first data source. So again, I'm going to do a little bit of data preparation so I can then ultimately join these two data sources together. So in a similar fashion, I can see that my first row of data has some information in that isn't really quite necessary to what I'm doing. So I'm going to drag down another sample tool, tell it to skip the first one record, do a quick double check here on my data, and I can see, OK, great, I have that unnecessary information. But what I do have is all of this weekly information in my F1 column in addition to the actual grocery store chain name as well. Now if I want to do some real analysis to this, I need to actually parse this out a little bit. I need to take this grocery store chain name and create a brand new field. Now there are a couple of different ways I can do this, but the easiest way that I can think of is to use a tool called the multi-row formula tool. Now the multi-row formula tool is very, very special because it allows us to actually create custom expressions on our data, but we can do that based on a row before or a row after our existing row that we're currently working on. So as an example, let's say we're going to create a new field called store brand. In this case, we're going to change the type of this to just a variable string here, and then we're going to go down to our actual variables. You can see that I can see our field names for the active row. So as this goes down and processes each individual row, I can then tell it, OK, well, if you're on this row, look behind to this row or look ahead to this row. That's what the row minus 1 or row plus 1 means. So in this particular case, I'm going to start with a simple if then statement. And so like in all things in Alteryx, we try to make things as simple as possible, including some coding. I can see Alberto asked a question about, OK, there's no coding, but it's similar to just kind of an entire data flow. And that's absolutely correct. So we try to make this with as little or no coding as absolutely possible. So that way, it allows you to spend less time actually preparing and analyzing your data and more time, or excuse me, less time preparing your data and more time actually analyzing your data. So in this case, I'm going to do a quick if then statement. And so the best way to do this is just to double click on the function itself. And I can see my template goes right down into my expression box. So what I'm going to do is say, OK, if is no Chobani, then I'm going to populate this new value with the value that's in F1. So essentially, I'm looking down each of these rows here, saying, OK, if this is no, and I could have chosen any one of these brand names, because essentially, this is my label field for my grocery store chain. But I can say, if this is no, then populate this with the actual grocery store chain. Now the real issue comes in is, how do I kind of bring that down? So you'll notice, sir, you remember in Excel, you used to be able to just grab the corner here and drag it down. Well, this is actually relatively simple. I can say, OK, if it's no here, populate it with this. Otherwise, populate this with the row previous to F1. So if I go down to here and say, OK, well, this is not no. So this falls into that else side of the offense statement. So it's going to populate the new value with what was in the previous row, which in this case will be the new field name. So when I do a quick run, I will now see my brand new store brand. And I actually did a quick thing here. Actually, let's do this. So instead of doing the row minus 1 F1, what we're going to do is row minus 1 store brand. There we go. And now we'll see an actual brand new field called store brand that's nicely populated for every one of my values in here. Now the final piece is to say, OK, well, I have a lot of new values in F1 that I don't need. So all of these values that are the actual store brand are kind of unnecessary because now I have a brand new field called store brand. So what I'm going to do is I'm going to look for instances where this and this are the same thing. So I'm going to do a quick filter on my data and go into F1 and then say custom filter. And so if F1 does not equal, which we're going to say, does not, just with a quick exclamation mark, saying not equal, and then we can say store brand. And so that's a very, very quick way to try to determine if these values equal each other. And the real reason why we're doing this is because we want to have this F1 field as only the date. So now we have nice, clean data. Dates here, values for Chobani, Dan and Yo-Play and private label, and then, of course, our store brand. OK, one more step. Since we have date fields here, I'd actually like for it to be labeled properly. So what I'm going to do is drag down the most commonly used tool, which is our select tool. So the select tool, it's pretty basic and pretty straightforward, but also has probably some of the most power in any tool in the entire set. The reason why is because you can change anything about the metadata that you're working with right in the middle of your data stream. So as an example, in this case, I can change F1 and rename that to date. But I can also take all of my values here. And instead of them being strings, I can say, you know what, let's go to my options here, and I'm going to change the field types of every field that I have highlighted and change that to a double. So now you can see I've got numeric data types for each one of my pricing values here. And then finally, the last piece is let's say we want to take this store brand that was at the very beginning of our data set, or at the end. And let's move that over to the beginning. So I can actually move this up very easily, just with the up arrow key. And so now when I run this, what we end up with is this nice list of store brands, all of the dates, and then all of the values that belong with each of the individual and unique yogurt brands. So this looks like it's in really good shape. But now going back to my other data, I can see that this is a little bit different. I have my dates across the top. I have my dates as field headers. And looking at this data, all of my dates are actually as rows. They're all nicely in one column, in a separate row. So what I need to do is spin this data around a little bit on this side. So what I'd like to do is take a transpose tool that's in our transform category. So in this case, it's just a matter of taking this transpose tool and then making a few changes here in my configuration window. In this particular case, what I'd like to do is turn all of my date fields into actual data. But then I need to also make sure that I have key fields for that store brand and measurement as well. So once I do this, I'll do a quick run. And I can now see that I've got store brand all as one field, measurement all as one field. But also, all of those unique dates that we were trying to separate out into a column, set along with all of the values. And so just like before, now I can actually take a select tool that I have here and drop this right after my transpose so I can take that name field, which is now date, and do a quick rename and say, OK, this is now date. But also, let's take that value field and let's change that also to a numeric data type, in this case, a double. That way we have the data prepped and ready to go. But I'm going to do one more thing. I have all of these measurements listed out in a column. What I'd like to do is turn these over into some sort of header. Well, with our crosstab tool, which spins the data back, it's like a little bit of the opposite of the transpose. The transpose will take horizontal data and turn it vertically, as you can see by the little icon here. And the crosstab tool can take vertical data and turn it horizontally. So what I'm going to do is say, OK, well, let's group again on that store brand and date, which will keep those in their own unique columns. But then let's also specify a new header field as that measurement. So think of all those different measurement types that we were looking for. And then finally, take that date field and specify that that's going to be that value field. So when I do a quick run, what we end up with is the store brand and date still in their own unique columns. But now we have all of those different measurements, so BL dollars, BL units, dollars here, ink dollars, and so forth. All of these are broken out into their own columns with all of the values in those columns. Now I can work with this, because what we have are store brand and date in a column in one data stream and store brand and date in a column in the other data stream with just a few tools here. Now finally, what I'd like to do is marry these two together. So what I'm going to do is go to my join category and just take a simple join tool. Now when you're joining two data streams together, you've got a couple of different options. What I'm going to do is actually specify actual field names. You can actually join by record position too. In some cases that could be really, really, really helpful. But in our specific use case, what we need to do is say, OK, well, let's look at store brand from each side and join those two together. But also, let's look at the dates as well, because we're really trying to look at that weekly data between just kind of taking this back to our original use case. We're trying to look at that weekly data between our Chobani yogurt and all of the competitive yogurts as well, such as the Dan and the Yoplait and the Private Label. So when I join these two together, based on the store brand and the date, that's going to give me that nice unified data stream. But I've got a couple of additional things that I need to do as well. You'll notice that the join tool has a lot of the same select functionality as well. So I do have the ability to say, OK, you know what? For those joined records, I don't need store brand and date in there twice. So let's remove that from my overall output. So that way, I have a nice clean data set. Now, once I run this, I can look at my joined records and I can now see I've got that store brand and date. But all of my left values or left data stream values from the Chobani sales data, but then all of the right values for all of the sales data as well. OK, so the next piece that we have can be really, really helpful. So going back to our original use cases, we want to look at the relationships between the sales and pricing for Chobani versus the competitors. Well, that to me sounds like we need to do some actual data investigation. So we've done some preparation so far using some of the common methodologies that you'll see in Altrix. But what we really want to do now is start to really investigate that data. So we actually have a data investigation category. And the one that we're really going to look at here is going to be the association analysis. Now, we have a lot of different things that you can do here. There's a field summary tool, which we highly recommend because this can actually break down on all of your data and tell you the min values, the max values for each field. If you're looking at string fields, it can tell you the longest string versus the shortest string and so forth. How many null values? That's probably the most important that you'll find in the field summary tool and so forth. But what we're going to do is look at the association analysis because what we're trying to do, our ultimate goal is, again, to look at those relationships. So what I'm going to do is I'm going to drop down the association analysis here. And I'm going to pick out a couple of different values. Let's look at the units, which is going to be those Chobana units. But then let's also look at the different values for all of the other relationships as well. Are they all of the other values as well? And then do a quick run. Now, this is going to be pretty interesting. But what we have here is a nice interactive output looking at the relationships between the two variables. So you'll see our correlation plot here, which shows all of our values on the left-hand side. So we have, most importantly, the units. And then on the bottom, we can see that we've got the private label, the Chobana, and the dana, and so forth. So if I look at units, I can see that, OK, I've got units to units as one, but units to Chobana is minus 0.33, because we're kind of looking at units to the correlation between the actual pricing versus the unit. So what this is a negative correlation with Chobani price versus the unit. So as the price goes up, the units will actually go down. But what this is really telling me is that there's not really a whole lot of information here. So what I actually want to do is look at the difference in price between these values. So what I'm going to do is I'm going to close this out really fast. And I'm actually going to go to another data preparation tool called the multi-field formula. So if you're following me along, go ahead and drag a multi-field formula down. But in this case, instead of dropping it after our association analysis, you can actually drop this right in the middle of your data stream. AlterX is smart enough to realize, OK, they're actually trying to do something before that association analysis. So what we'd really like to do or what it's going to do is break that data stream and insert your tool directly into that data stream. So what we're going to do, so if you remember back to the multi-row formula, that kind of looks at different rows behind and ahead of the active row. What this multi-field formula does is this allows you to actually perform one expression on multiple different fields. So in this particular case, what I want to do is actually create a kind of a diff field, like a difference between the actual Chobani value or the Chobani price with the price of the competitor. So what I'm going to do is scroll down here and say, OK, for each of these, the Dan and Yoplay and private label, I'm actually going to create a brand new field called diff underscore. And then I'm going to create an expression. So the expression is going to be pretty basic. All we're going to do is say, OK, take the current field, which is going to be each of these three dynamically and then subtract the actual price of the Chobani. And so what this is going to do is this is, again, going to create a dynamic new value for each of these fields. So when I run this now, I can see now, looking at my values, I can see that I've got these new diff fields, which can be very, very helpful, which I can then put back into the association analysis to look at units with the price of Chobani. We'll keep that in there because that is necessary. But I want to look at the price of the Chobani versus the difference between the other values. So now we're really going to start getting somewhere. So with this, what we're going to find out is the first thing we're going to do is actually open up our correlation matrix. And we're going to look at the units here. And you can see that the units went right to the top now. But I can now see that actually the Yoplait has probably the biggest correlation. So if there's a difference in the price of Yoplait, that could really affect the units of Chobani being sold. So going down the line a little bit, we can see that for the actual Chobani itself, it's not quite as much. This is a 0.26. This is minus 0.33, 0.19, and 0.10 for the private label and then for the Ganon. So what we're going to do is let's just focus on the Yoplait as an example. Let's look at the units of Chobani versus the actual difference of the price in Yoplait. And so now what we'd really like to do is do one final analysis. So that final analysis is going to be in our predictive tool category. So what we're going to do is try to really look at and analyze those two values. And so a very common scenario for this will be just to create a linear regression model out of this. And so what we've done with Altrix, going back to the previous question that I saw about not having to code anything, we're going to see that really, really prevalent here. And so if I look at my linear regression, you might have noticed that traditional methods might actually require you to code something to create some sort of a regression analysis. In this particular case, what I'm going to do, all I have to do is create a model name. And this isn't even actually required. But then select my target variables, which is the unit. We're looking at the total number of units that we're trying to sell or move. But then we can scroll down here and actually look at the difference of the price of Yo-Play versus the total sales for the total price for Chobani. Add one more browse tool and then click Run. And just kind of do a quick recap here. So we've got two different pricing sheets, worksheets here. We've done a little bit of data preparation by skipping some records here, dynamically renaming fields, dynamically creating a formula based on a row, spinning the data around, joining the data together, dynamically creating a brand new difference field ultimately so we can go down to this linear regression and have a look at our actual model values. So now what we're looking at and what we're really finding out here is the data for the coefficients is what we really want to look at. So what we're looking at with the estimate the price for Chobani versus the difference in Yo-Play, what we find out is that there is actually a negative relationship with Chobani, as you might expect. So the price goes up, the number of units pushed goes down. But there's actually a positive relationship with the price of Yo-Play. So Yo-Play goes up, the number of Chobani units will actually go up as well. So you see it have that positive correlation between the two. Now even this negative correlation, that will actually still be affected by this positive correlation as well. So that's actually kind of a good thing. So you see that, OK, well, if we raise our price of Chobani, the total number of units will go down, but they won't go down quite as much if the price of Yo-Play also goes up. So a couple of different things and a couple of different pieces of analysis there to keep in mind. And so that's our workflow. So Matt, I'm going to open this back up to you again. We've got about, I think, 14 minutes left in the session. And I think we'd like to open it up for questions, right? We have two more slides, and then we'll open it up for questions. So if you guys can take the ball back real quick. No problem. Perfect. Thank you very much. So really, in conclusion, we're not going to replace the spreadsheet from a work environment. But the beauty of the workflow is that it allows you, as an analyst, to really stop thinking about data and analytics in a columns and rows manner. It's really about incorporating a wider variety of data sources and looking at your data and analytics as more of a process, a little slow here. And this really allows you to understand where there's issues might be, where errors might be in that process. And then you can interact with that workflow using the drag and drop environment and easily insert additional data processes, steps to prepare, blend, and analyze that data. So with that, I think we're going to open it up for some questions. All right, Matt and Chad. Thank you so much for this great presentation. I'd answer the most immediate question that always comes in. We are recording the session, and I will be sending out a follow-up email with links to the slides, links to the recording, as well as any additional information requested throughout the webinar by End of Day Friday. So the first question that has come in for both of you, does Altair also integrate with MicroStrategy? So great question. When we talked about what we output to in terms of click Microsoft Power BI and Tableau, the reason we highlight those integrations is because they have a specific file format that we output to. MicroStrategy, as far as I understand, unless they've recently added something specific, doesn't have a specific file type that it needs to output to. So we can easily work with MicroStrategy from support on the data preparation, data blending, advanced analytics side, and you would simply output it to whatever type of data file format that you want to bring into MicroStrategy. Sure. And next question is, is workflow the same as a process? If not, what's the difference? Yeah, a workflow process is really what we describe it as. So it's the same. So do you discuss data flow processes where you must model in-user approval edit intervention input? I'm not quite sure I understand that, but, Jed, maybe you can give me another idea. No, I'm kind of in the same boat. It might be a good idea to shoot us a quick email with a little more detail afterwards, and we can always follow up with that. If the questioner wants to add a little more now, we should come back to that. So let me move on then to the next question. Is the tool more for data analysis versus being a platform similar to MSTR or Tableau? You said you integrate with Tableau. Correct. You know, as we mentioned at the beginning, it's really a platform for doing data blending, data preparation, and analysis. And then building out some sort of end result. And that end result could be outputting into Tableau. In fact, you know, we have close to 500 customers, joint customers with Tableau that we work with. MicroStrategy, you know, I mentioned a little bit how we could work with them. So a lot of people use this in different ways. A lot of people use this for that data prep, data blending, and output it into either a downstream process out to an analytic application or literally out to Tableau. Sure, and you know, we actually recently did a webinar with another group from Alcherx and also with Tableau. So I'll get people linked to that recording as well that talks about the integration there. Is there a way to export the metadata so that someone can view the formulas in one window? Yes, so Matt, I can take that one. There is actually a tool called Field Info, and essentially that will tell you all of the different information about each field, including custom fields that were created in a formula tool with all of their relevant formula expressions. Just the same, you could go even a little deeper. Every one of the Alcherx workflows that get created and saved are actually just a physical file on your local machine, whenever I'm developing in the Alcherx designer, that is. And so all of those actually have an XML wrapper around them as well. So in some ways, you could actually even use Alcherx to pull in the text values of another Alcherx workflow and parse out things like your custom expressions or your full data audit. That actually is completely possible. So another question on the functionality is, Alcherx mainly eliminates stops as much as possible in workflow for manual scripting. Once I know what I need to know, what I need to look at in the data analysis, for example, I need to understand what to analyze to prep the data to know my workflow and to figure out what steps can be automated and put into those to be automated steps in Alcherx. So I've got a couple of different things or comments on that. The quick answer is yes. Once you know what you need to actually do to your data, Alcherx makes that very easy. Now on the flip side, if you don't know what you need to do to the data, that's where our data preparation or data investigation tools can really come in handy. So those investigation tools such as that field summary and association analysis that I was talking about, that actually helped me reveal what my data was actually doing or some of those relationships that my data had because I didn't know what I actually needed to do to my data, so it helped me identify those relationships so then I could move forward with my analysis. So it kind of works in both ways, whether you know what you need to do or whether you don't know what you need to do. A lot of users use the tool for that kind of initial investigation, but then you also have the ability to put that into some sort of a production scenario such as a regularly scheduled process that automatically runs and maybe automatically refreshes something like your Excel output report or Tableau data extract or something like that. Sure, and back to the functionality. What extensions does Alcherx allow? Oh, we allow a lot. You could actually go to our Alcherx, primary Alcherx website. There's a technical specifications link underneath the products category, I believe, and that actually shows you all of the different file formats that we accept. I mean, most commonly, you'll see things like, yes, your Excel, Access, CSV files or any sort of text-illimited files, but also there's a series of databases that we also can connect to, including but certainly not limited to your SQL Server, Oracle, Teradata, Hadoop, Cloud Era and so forth. And then finally, we also have a series of connectors for Cloud Data Sources as well. So if you have Salesforce data, Marketo information and so forth, you can actually pull all of that into the exact same workflow to blend and prepare and analyze just as if it were another data source. Love it. So going back to the demo, Chad, that you were doing, what would you say or P value of 0.33 on the Diff under, or you'll play? So I will be the first to admit I am not a statistician. I know how to kind of configure the tools and start creating a couple of different models. But at that point, if there's something that kind of looks funky in my data or there's something I'm not completely sure about, such as your P value question, I actually will go to our data scientist or consult Google. So the Alteryx platform gives you that ability to actually create those models without having to code so you can kind of run those that ad hoc analysis to get your model honed in very easily without having to code anything. But whenever it comes to those specific statistics questions, I often will reach out to our predictive experts, our in-house predictive experts. So if you have some additional questions on that, we'd be happy to connect you with the correct people. Sounds good. And so what if I have 70 input files? How easy is it to process over a list? Good question. So we have a couple of options. The first of which is if your 70 files all have the same structure, they all have the same field names, you can actually just use one input data tool and use the wild card to asterisk. So you can say star.csv and it will pull in every CSV file in that same directory. So that actually makes it very, very easy. You can actually continue to process all of that information individually, even though you're only using one input data tool. There's actually an option that tells you to pull in the field or the file name as a data point in your data. So you could actually pull in that file name for all 70 files and then process individually, process them by groups essentially, like process each file individually and then output using only one output data tool to 70 individual files if you like or into just one summarized file as well. You really have the flexibility to kind of do both. So that's the easy way to do it. There is another way if you have, maybe the files are unknown, you could actually use what we call a dynamic input tool. And so that actually will read in a list of files. So if you needed to customize that list, you didn't wanna pull in everything under the sun. You only wanted to pull in the five most recent ones. You could actually read in just the directory list of those files and then pass that to the dynamic input tool and it will only read in the files that you give it. Nice. And we are running out of time. We have a time for a couple more questions. So keep your questions coming. I will be sure and get the questions over to the AltairX team so they can follow up with you at a later time. Is there a tool to split records to multiple rows only if certain condition is met like other fields contain certain values? A couple of different ways. I think I'd wanna learn a little bit more about the process but in my mind the way that I'm kind of picturing the workflow is that you could actually just start using a series of filter tools to filter out the specific rows that you wanna output or maybe a better option might be to identify which rows need to be separate using some sort of a flag field. So you use a formula tool to identify individual rows that say, okay, this row belongs in this bucket, the next row belongs in the next bucket, the next row in the next bucket and so forth and then use that feature I was talking about previously in the output data tool to say, okay, output a use one output data tool but separate out different output files based on our flag field or that bucket field that we just created and it will dynamically create multiple different output files based on the unique values in that field. All right, I think we have time for one more question. So gentlemen, the most important question of the day, how long is the free trial? The free trial is a 14 day trial from the day that you downloaded. So if you downloaded today, you have 14 days from today. I would encourage you to reach out to our sales team if you wanna try to get an extension on top of that 14 days, they are more than happy to work with you once they understand your business use case problems, et cetera and I think we'll have some contact information right here in front of us or you can visit our website at altrix.com or slash contact us. Absolutely and I'll be sure to include, if you haven't downloaded the free trial already, I'll be sure to include those links in the follow up email. I'm afraid that, because that is all we have time for today and also in the follow up email but I will send by end of day Friday with links to the recording and links to the slides of the presentation. Matt and Chad, thank you so much for this great presentation today and thanks again to all of our attendees as always for being so interactive in everything that we do. We just love all the great questions coming in. Again, any questions that we didn't have time for I'll send over to the Alteryx team for them to follow up with you on. And again, thanks to Alteryx for sponsoring today's webinar and I hope everyone has a great day. Thank you everyone. Thanks guys. Awesome, thanks everyone. Thanks Shannon.