 So hello and welcome to another webinar from D Brown Consulting. Today we're going to be talking about actuals versus budget with Power BI. So actuals versus budget. So this really is a typical thing that financial models are kind of interested in, they're comparing actuals against budget. So management accountants, accountants and the rest, they compare actual against budget and they do a variance analysis and stuff. So they do a lot of this in Excel and they could be considered, they could consider a bit of a modeling thing. So today we're going to be doing Power BI Excel and modeling all in one. We're just going to use actual data and see how use of power query in Power BI and using of Power BI itself can kind of automate this modeling, automate how we build a small model that has actual data and budget data and we do comparisons. So if you haven't joined already, please join our Power BI user group and also join our Meetup group and also join our Meetup group of financial modeling, our Meetup group for Excel and then there's the Power BI user group for Nigeria, which is pbiusergroup.com slash Legos. So join if you can. Great. So I'm David. I'm David Brown, Founder, Manager, Partner of D Brown Consulting. Also a consultant to the World Bank on all revenue modeling. I'm a Microsoft MVP and I have almost 22 years working with Excel and over 16 years as financial advisor, trainer, financial modeler, BI consultant. I'm also a master trainer and master instructional designer. So I do quite a lot of training and also create online courses on office training hub. So that's just a lot about me already. So let's just move on. So overview of Power BI. That's what we're going to break it down to do an overview of Power BI. Then we do an overview of modeling. Of course, how do you model in Power BI? And how to build a quick data model? So we're going to quickly build a quick data model for actuals, build a quick data model for actual data. And then we also kind of see how we bring in budget data, which is not as granular as actual data. How to insert the budget data and then how to calculate actual versus budget and the variance analysis. So that's what we're going to talk about. So let's just jump straight into Power BI. So have Power BI on the screen. And we'll just jump straight into Power BI. What is Power BI? So Power BI is a tool that brings together certain technologies for Microsoft. Microsoft has been kind of testing out in Excel for quite a long time. That's Power Pivot and the language for Power Pivot is DAX, Power Query and the language for Power Query is M. So Power Query is how we bring in data. So let's see. Let's see if we can have data. Let me show you what data we're going to be using so we could build a quick budget model and a quick actual versus budget model. So here we have our exercise files and exercise files we have. We have data, demo, let me just minimize this. So we have data, demo and exercises. So let's see if we go into data. So here in the data we have sales data and the sales data has some actual data where let's say we have a text dump of all our actual data. So these are all the actual data we have saved us text files. If I open one for example, you would see that this one we have data that looks like this. So I have all this data. Region, Market Store, Trade Date, Fiscal Period, Model, Line of Business, Day, Category, Revenue and Unit Sold. So that's the data we have. Right. So that data we have is what we'll call our FACT file and I'm just going to go straight in and look at the dimension files. So the dimension files are really what enriches our reporting and let's see what the dimension files contain. So looking at the dimension files, we have LOB, which is like a line of business. So just the LOB, what's that? It's a line of business. We have a model sheet. It's a model sheet. You have, okay. So if I click here, our model sheet has data on, right. So our model sheet has data, model ID and model code and model name. Then you have your dates. This is like our calendar. So I have all the dates for our reports. So I have dates. You can just check, change this to format. You can see January, first of January all the way. So calendar has to have days for calendar from 2014 to 2020. So calendar has months, years, blah, blah, blah. All those data there. That's cool calendar. And we have stored data. So these are all our dimensions that enriches our report. So we're going to pull in, we're going to build a quick data model now. So I'm going to connect to this and also connect to my fact file. So back to Power BI, back to Power BI, we're going to get data, under get data, we're getting data from Excel or really we're getting in fact, let's get our dimension files first. The easy one. So let's get data from Excel. We know it's in Excel. It's on the desktop. So we get our data. We need to get our data, get data. We go to Excel. And I just browse to my desktop. On my desktop, I can see PBI exercise files. I go to data and let's go to our sales data. We go to our dimension files and we connect to our dimension tables. So connecting to my dimension tables in Excel, we want to pull out our various dimensions for our report. So those are the tables, the typical VLOOKUP tables that you use. So you see a line of business, bringing our line of business, bringing our model, bringing our store, bringing our dates, where's our dates and the calendar dates. So those are our dates. So I load or could edit it first. So let's edit it in Power Query. Once you click edit, it's going to open Power Query. So typically these are the data that you need to use for your reporting. These are what enriches the transactions that you have in your sales. So that data is opening up. So it's opening in Power Query. Power Query is different to Power Queries and ETLs, an extract, transform and load tool. So we've extracted the data from Excel. Now we want to kind of transform it before we load it into Power BI. That's the idea. So line of business. Let's check. So typically you check your data types. See if they're all good. This data type, check here is text. This one is text. This seems fine. D model. Again, when it comes from Excel, usually it will be nice and clean. If it comes from a text file, you may have some issues that you need to look out for. So here we have our model code, which is a text. This is a model. Name is a text. That's fine. D store, the store ID, text, star name, text. Yeah, everything. The data type seemed good. D calendar. Now our date should be a date. Yes, our year should be actually our year and everything really else apart from date should be a text. Now only the month number should be a number and a whole number. We'll see why very soon. So this should be a text. And yes, I replaced this. So we have power query. Yeah, this should be a text. This should be a text text there. Then we have power query. Parkway already has some steps down here. It already changed the data type, but we're changing all of this with some here all the way to the right. Every single thing should be a text. So it should be text. We change this to text, right? All text, right? So because we're going to use them in the reports, like a category field, and that's why they should be text. So this is a date field. This is going to be a number field the rest of text field. So I think all the dimensions are fine. We're going to close and load. So close and apply actually close and load is what you do in Excel. So close and apply because it's not loading to me. You could load to Excel. In Power BI, you just have a connection to the data. So here it is looking and pulling the data into the data model in Power BI. And so it's going to finish that pretty soon. So we have detecting. So it's detected stuff. Let's just go to the tables and see what it brought in. So it brought in our calendar decalenda. It brought in our line of business, which is there. You can see there I can zoom in. So you have a look at our line of business. So that's our line of business. It brought in, right? And then you can check the model. So this is the model data and just zoom in to see the model data. Yeah. And we have our D store. So this is a store. This is all the data about the stores. So consuming to see that. Great. So if you check the model view to see the relationship. So we have all these dimension files. So these are just to enrich our reporting. We don't have the transactions. They have the actual fact files. We don't have that yet. So how do we bring that in? So I'm going to bring that in next. Let me just zoom this in. So you see that this is our data model. All we're waiting for is our fact files. So our fact files are going to come in here and our fact files update every day. You have data and new data coming in. So we're going to bring our fact file using a power query connecting to maybe a text file a folder that contains some text files. So that's what we'll do next. So we're going to get data. You get data. So we click it on get data. This time around we're going to go to more. So get data more and it's going to open a dialog box. So this is the dialog box is opening. We're connecting to folder now folder is under files. The same thing you can get on that also connecting to a folder we connect and we're going to go through and look for that text file. So we're going to browse go to desktop. Let me go to my desktop here. I check on all data. We click on this click on data come on to data come to sales then we go to the actual you can see budget there will come to that later and we're clicking on a text file. So we're connecting to a text file a text file with all the data in the text file. So these are all the data in the text file. So probably I you come to combine you click on this drop down for combine and say combine and edit. So we combine and edit is going to open power query. It's opening power query and in power query will be able to edit the data. So here we are we have is a tab delimited file. It's automatically checks what kind of text file it is. So if you check the drop down we have other forms of text. So if I if I zoom in there you'll see we have colon comma equal sign semi colon space tab. You can do a custom depending on what kind of a delimiter you're using for your text. So so this is the data. We're happy with it. We're going to go into edit it to click OK. So it's going to open power query application to evaluate an inquiry once it opens a power query application. We can now have a look at what it's done. So this is the power query. Nice thing about opening connecting to a folder is there's quite a lot of calculations quite a lot of things that power bear does by itself. It actually creates a function. If you look at this this is a function that it has created to automate the consolidation or the combining of the data. So this function was created by Pabii on the fly. So sample file parameter is going to let source which is CSV dot document sample file parameter delimiter is blah blah blah. So all this this is M really M. So trust me it's done a good job in consolidating it. If you want to have a look at what is done. This is exactly what it's done. So this is all the steps. So these are all the steps it took is consolidated the data and everything you do here. If I do any manipulations here is going to record those manipulations inside the function that consolidates the data this function here right. So that's the data and at the end of the day is going to give us that data to load and is going to call it here is calling it text dump. So this is the final data we're loading. This is all the manipulation you want to do do it here so that once you do it here it becomes a function and and that function just automates the process of consolidating any more text files that goes into that folder. So this text dump I'm going to rename it as my sales or let me just call it actual. Okay. So I just call it actual or you call it sales but that's my actual sales. So I just say actual because we're going to bring budget later. So that's actual. So I call it actual now these are all the steps that's happened right. So we we have filters is the source data down here to the right is where I am. So if you look to the right to consume and see so look to the right you see that's your source filtered filtered hidden files. That's what he did and then it invoked custom function then it renamed columns blah blah blah. So those are the steps it automatically. All right. So you can see the various steps nice steps and everything that led to this. So we have everything now. This is also consolidated. It's very simple and straightforward. Fully consolidated. Now if you want to be sure that you've really fully consolidated because it seems like just magic, right? It just did it by itself created a function. When you data goes into the folder it will update. So let me show you something. So if you see the sample file, so it used the sample file April as a sample file to kind of do the consolidation and all. So the sample file parameter and this is what two of the things that was used to kind of create this steps, which kind of created this function, which is what we now use to get this result, right? So if I come here, for example, this cleanup thing that this sample file from text dump, right, transform sample file. This would transform the sample file. If I come here and say, do you know what? Show me or show me keep the top. Let's say top one row. So if I say keep top, keep top, right? Keep only top one row. When I when I say this on the sample file, look at this. So this is the top one row of this sample file, this sample file, which is April, right? Now, because this is a function, right? This is what's going to be transformed to a function. And is this function to the left here that I'm pointing to the left, right? This function here, if I look, if you look to the left, so it's Clara. So you have this sample file, then this parameter that converts this sample data into a function based on the fact that this is now a function. I kind of worked on this sample file. Whatever I do on this sample file will transform the entire folder, which is where I've converted to actual here. So when I click on actual, you will see that actual here, I have just one record from every single file. So this kind of proves to you that what has happened is just taking one record from each file. So this is one record from April, 2014, April, 2015, April, August, 2014, August, 2015. So these are single, single records from all individual files, right? You can see that. Let me just zoom out. Right. So I can come back to my transformation of the sample file and go back to this and say, look, do you know what? Don't, don't let me delete this step to the right. See this step to the right. If you look to the right, I said keep first, keep first row. I don't want that anymore. So to the right, you see keep first row. I'm going to close that. Let's close that. And you'll see that if I come back to actually now everything is back. If I come back to actual, everything is back and all the sources, if I click on load more, you see that it has loaded everything. All the various text files are loaded. So it's pretty automatic. Pretty cool. Works well with text. Doesn't work as well with Excel. When they're consolidating Excel though, but that's a topic for another day. Anyway, so after transformation, what we need to now do is decide which columns we're keeping. So I'm not going to keep this source name column. We're not keeping this region column. I'm not keeping this market column. We're not keeping this because we just don't need it. This region and market. Well, we have store and we have a store. We have all this already as like our geography dimension. So we don't really need region and market is only stored. That would be our key. So I'm deleting this three. I'm removing these three columns. We're keeping store trade date. Yes, we need trade date, but we need to kind of transform the data type for trade date. This trade date data type shouldn't be a it should be a date, right? So we're going to change the data type, but let's delete things we don't need. We don't need this. We don't need more. Well, we need model. We need line of business. We don't need the category. We remove that. We need revenue and we need units sold. So now to transform. So we remove things we don't need. The ones we left are really just the ones that are going to connect to our dimension files and then the values that we're analyzing here. Right? So the store data should be text. So even though it's kind of done the texting here, I can convert it. So if you look at the steps, actually, you see that it's done a change type already. Don't want change type here. So even let me let me rewind a bit. I'm deleting the remove. So this change type that it did, it kind of identified and changed our data types. Let's delete that. So let's delete that. So we're going to create our types ourselves. We're going to determine our data types ourselves. So first before determining the two types, if you look up a bit, data types are so important. Every column you need to define what data type it is. And one of the worst worst data types you can ever have is the one called any. So if you look up here, it's called any. Yeah, see any any data type. If I click on this, you see the different data types. Any basically means nothing. And lots of formulas will not work with an any data type. You have to have the correct data type. So this obviously should have been a text data type. But first let's delete what we don't need. So I'm deleting what we don't need. I'm removing that we need dates. We need store. We don't need this one or remove this. We don't need we need model. We need line of business. We don't need a category. We remove that. So these category or these fields are what we need to connect to our various dimension files and then revenue and unit sold is our value. Now we can change this. Let me just take this to the right and then we take trade date to the left here. So trade dates should be a date data type. So I'm going to change that to a date data type and look at what happens by changing trade date to a date data type is messed it up. It gives me errors here. Why is it giving me errors? The reason it's giving me errors is because the date system on my computer is really day month year. So let me change. Let me delete this change type. So you see what's happening here currently that the date here is month day year but on my computer system. My default date type as my default regional settings or date type or I call it my way. My system understands dates. If I open Excel you see an example is it understands dates as first it understands dates when you type in our dates it will recognize it as month first. So if I do a control semicolon which is a shortcut for entering date and I enter so my system recognizes 16 0 4 19 which is 16 which is the day for month 19. Yeah, for example, so that is just an example of a date right? It recognizes day month here but my data is month day year so I need to write a little M. I need to tell the system that look this thing here is actually month this date they are seeing which is coming in as a text for example. I'm going to make it a text after make it a text first. It's coming as a text and I tell the system please convert this to a date but use the currently in English United States settings please convert it to a date based on the English United States settings that it is to do that I need to add a column. I'm going to add a custom column so I go to add column at the top here and choose custom column. So the custom column says okay what exactly do I want? I want my correct date and in this custom column just going to zoom in a bit okay custom column so see if I can make it bigger anyway so custom column I need to write a function called date dot and you can see IntelliSense is working nicely dates dot if I scroll down on dates dot you see various date dot everything about dates dates dot so dates dot from text is what I need so I want to take from text what am I taking from text this particular trade date yeah comma I need to kind of get the culture what do they mean by culture? The culture is currently what it stored as I put on double quotes is actually EN dot US what is stored as is EN dot US why because we know that the code that has month first then day then year is an English United State code so I close this bracket at the end and this should work fine and I say okay so I should have a new column that has my date in the correct order right so if I take this to the start this is my correct date and I make this the data data type so I come here and change the data type from transform I can actually go to home and do it as well come here and change this to the data type this we don't need anymore so I'm removing it this is this all the way to this this three text data type and our revenue and our revenue text data type and our revenue is the well is the decimal number data type and unit so it should be a whole number data type so now we've done the dates correctly and stuff now even if your dates were correct please always use this do that custom column and tell it that the dates that are coming in as text are coming in as this as English United States so everything is done transformed everything is consolidated we're just going to close and apply and once you close and apply it's going to build our small model for actual data coming in and there we go actual data comes in and here we are for some reason you decided to just connect for me without asking right it did auto detection it auto detected line of business and model it chose line of business to line of business and model to model code now does that make sense model to model code let's check so if I come here we could just manage relationships and look at it so if I click manage relationships maybe that okay now I need to go to the data data model or data for it to activate it's not opening for some reason manage relationships doesn't want me to manage relationships so typically this button should work I think this is small bug click click it's not working so fun to manage this I can just double click it if I double click it it should bring that manage relationship box so here is taking actual model connected to model code and that makes sense right what about other relationships want to create let's assume this date for example should be connected to this state so that's the one side and is the many side and for store we need to connect if we check store is the store should be connected to store name because we know it stored a store name not store ID so here we have it so these are the one sides and this is the many side this data model is nice and cool nice and everything connects yeah so this is a model is nice and cool everything connects so we'll now need to come into our report and we can easily create a very simple report so let's create one report let's say we go to our calendar we pick here we pick month and now this is looking strange doesn't it look at this year and month if I come to visualization let's just increase the size the size the default size is way too small I just hope Microsoft and wonderful guys of the Power BI team increase the default size it is way too small always small so April August this is not really sorting right so we need to go back to our data model to fix that we come come to our calendar here and what are you're going to say here is simply and you know what I need this my month here to be sorted by if you go to modeling come to modeling right so I just need to select our month I go to modeling and then take sort by month number and that's why we left the month number as the data type called whole number if you remember month numbers data type where are we if I come to home click on home so the month numbers data type was actually if I click on modeling you see it month number was a whole number data type I remember here was a text data type so I've sorted month by month number if I go back to my report everything should kind of re re kind of calibrated self so I have January February March April May everything seems fine so let's quickly create a very quick measure we're going to do our revenue so we're going to do our revenue now another thing we need to do if I check the file and I go to options sometimes you know those relationships are automatically calculated or created itself we really don't want that so if you go to data model you go to data model another thing we don't want this auto data and time this you shouldn't have auto data and time you should control how time intelligence works yourself and then auto detect relationships here we really don't want that either so when you come to options you need to set you need to set these options yourself all right so I said these options were auto detect no I don't want that and you decide whether or not you want all of these other options working so I advise you always come down here data load what happens when you load data and choose the options that make sense for you right so data data load if you look up here see this all the data load options as I said auto data and time really it's not nice automatically creates a hidden date table we don't want that for each field in the model no we want to create the date table ourselves we don't need that auto data and time it's like an implicit date table we don't need that right so let's quickly create a measure so click okay so I'm going to create a simple measure and that measure is going to be just zoom in a bit so let's zoom into the calculation let's just make it bigger so what's this measure going to be called so we just call it let's call it Revenue Revenue actual okay never any actual is equal to a sum of the table called actual the column called Revenue in the table called actual simple as that so I can enter when you enter first thing you should do really is format it just click on the format and I minimize this so we've done our first measure so that's our first measure now if you want to activate it so look at it here now I brought it here is under actual if I take this it comes in and this is our actual revenue so the next thing we want to do is bring in our budget and then we do a variance right so we have our actual revenue here want to bring in our budget and then we do a variance and that's us done what exactly we wanted to do how do we bring in our budget data so let's go have a look at what the budget data looks like so let me open I'm going to open the budget so I'm opening the budget data is there on our desktop I go to data I go to sales data you see budget so this is our budget data so let's see what it looks like so your budget data have to understand that your budget data is also a fact file you know we have fact and dimension so this budget data that we're looking at if you can just zoom in a bit what do we have here so we have here we have month we have store ID so that means we can connect this to the store dimension table we have store name so we just have store data store data we give us connections to geography as well so we have store ID and store name and then revenue so frankly we can only do reporting by store name and then maybe region name and then state name if you look at the dimension files you see that so anything related to stores the only kind of reporting we do and also date but we don't really have a date we have year and month so we need to kind of construct a date so we'll see how that works in Parkway so we need to use Parkway to pull this in so let's do that let me close this we go and bring in power query so we're going to bring in this budget data I can actually copy this link we'll use it when we're connecting Parkway so I come to home I do get data so we're going to get data from Excel I can just click and paste this link so I get in data budget data directly so I bring in the budget data it's opening so this our budget data I'm going to edit it in Parkway so it's going to open Parkway it's opening Parkway now now I need to edit this the first thing I need to do is construct a date from this year and month and really the simplest way to do it is this very nice wonderful tool called columns from example so that's under the view under the add column tab if you look up here so column from example right so if you look to the left sorry about that so column from example column from example so let me get my mouse working what's this one saying so column from example so if I click on this column from example it wants me to give it an example right come from example so I'm going to say do you know what I want this to be one slash one slash 2014 then I enter and let's see if it's kind of clued in so it kind of clued in see one slash one slash 2014 and hopefully it's done that for all the other ones let's see can you allow us to go down well one slash one slash 2014 it did just that let's see what it did in the formula bar so it transformed text combine text dot from month English and G this is English Niger actually slash text from month English and G slash text from year English and G well it sounds sounds good so I say okay this is actually flash fill something like flash fill that we know of in Excel something like flash fill so I can click on this and come to transform and I'm going to transform the data type from text to date so this is now a data type I can take this to the beginning I don't need this and this anymore is done is job I remove the columns then my store ID is a text can leave that this is a text I can leave that and then my revenue is already a decimal number so this is fine so everything here looks good ready for us to load into our model so I'm going to load this into our model come to home close and apply I'm bringing in our budget data into our model and for you to use your budget data in your model you need to come to this data here so this is our budget data so we now have two fact files so these two fact files for them to talk to each other this is the many and the many side you can't connect it you really can't just directly connected look at this merged what's merged let's go rename it I think let's go to edit query instead of calling that merge we should call it something that makes sense what does merged mean go to budget merge should be called date so we're going to double click that and call it date so date makes more sense then I close and apply so this is going to change to date so this is our data model now if I connect date here to date here it's really not going to work it just wouldn't work the relationship has cardinality many to many is not going to allow many to many this should only be used if it is expected that neither column date and date contains unique values and that the significantly different behavior of many to many relationships is understood well it's too much story for us right now date will connect to date here because this is like many to one and your revenue is revenue but your store ID will connect to store ID here right I can even do store name to store name so so what's happened here is that we have this budget connected to two different things but these two different things here this calendar and this store also connected to actual and that's how budget will talk to actual through these guys through date calendar calendar and store so now I can use budget and write a measure for budget so I can come to modeling for example or come into my come into my canvas I can come into my canvas come to modeling unless quickly write a new measure we're going to write on the modeling new measure and the measure is going to be if I drop this down so we're going to write a measure called budget budget so budget data so budget is going to be equal to some of my budget table and in my budget table I want to some revenue right so this is my budget enter and I can change the format thousand separator so I've just created a budget measure right so if I click inside here I can actually bring it in so I can tick budget where did I create budget okay I think I created on the budget say I wasn't too clean when I created budget where where did I do I did it here in actual that's fine see so I have budget data now next to revenue actual about budget so now we can do a variance calculation and next measure we do is simply say okay what's our actual minus our budget so percentage difference from budget from actual versus budget so here I do another measure and this measure is going to be called variance what should I say revenue variance revenue variance percentage or revenue V budget revenue variance or those V budget and many people have various ways to call this thing revenue vs budget or something percentage yeah so it's a variance percentage and we know that that's going to be revenue actual divided by budget minus one and we can make that a enter once we enter we make that a percentage and we can now bring this new measure into into our report so revenue budget actual where did we drop that we can clean this you see I have something in budget and stuff we can clean where all these calculations are so here we have this okay I think we should have it into two decimals or something less increase the decimals yeah so that makes sense right here we have our revenue actual versus budget and this is a report we want except that there seems to be no actual data for November how do we solve that well if you remember this our data was actually January to October we don't have November we don't have December so we don't have November we don't have December data and right now we don't have years 2016 we don't have 2016 year so we can pull in this new data from let's say we ask we're going to ask people in it to give us this data and then for 2016 since we're not there yet we'll go to the home tab maybe go to edit queries and we're going to edit the calendar table so the calendar table we're just going to edit out those gears that we don't currently need so under years in the calendar table I'll load more and I'm just going to say okay I don't need 2016 17 18 19 and 20 we don't need that quite yet so if I click okay I've edited that out of my query if I close and load and I look at my report you're going to see that these 2016 and everything is going to disappear so we stop at 2015 now we're going to load these two data we have as text files so if I go to my folder if I show you my folder now you'll see that those data are in my folder so I just quickly go into the folder I go to the data I go to sales data I go to the actual data I go to text dump or I go to IT downloads and go to text you'll see I have November and I have December data October is already in there so this to November and December which I don't have down here I'll just copy this text files which I just got from IT and I'm going to dump them into into this text dump so this text dump is where I dump all my data when I get from IT so I've just dumped December 2015 and November 2015 you can see those two there so now when I come back to my report here all I need to do when I come back to this report is refresh so if I refresh it's going to go and refresh all my connections to my dimension files my fact file actual and you'll see that this is going to now bring in and populate some data for November and December so just watch for your eyes down here at the bottom see that two data have now appeared and now I have my actual versus budget and the variance for budget and with this variance for budget I can easily do a quick chart so I could remove my revenue and my budget so I'm going to tick on tick this right click on this visual I remove revenue actual I remove revenue I remove budget data where is my budget data and I have this which I can easily create into a nice line chart and see exactly how we're doing when it comes to budgeting so you can see that this is how we're doing year on year this is how we're doing from 2014 to 2015 when it comes to budget so 2014 we had a figure here and then 2015 it seems that we did better against budget we did minus against budget and compared against budget so yeah we could put that there we can even change this chart I can change this chart instead of having month in my legend I can have month down here and then I have a different chart which is my line chart I can drill down a little bit you see my line chart now is by 2014 you can see we're up and down when it comes to budget all the way all all over the place budget and actual budget and actual right you can now bring in something like let's say you go to the calendar now if you bring in something like line of business you have to it wouldn't really work because if you remember our budget is only only connects to geography so I can only bring in something like a store so I bring in something like a store let's say I created as a tree map and I'm going to take store as my tree map I take store maybe maybe not store maybe region as my tree map and then I'm going to take actual revenue just revenue actual so this is my tree map if I click on north east like this is my budget variance data so you can see how how I compare against budget I did pretty well here this my best month March revenue versus budget was 11.79% well that's revenue above budget 11.79% and then below budget was kind of ugly right so that's that's the analysis you now have your budget and actual data all looking good and that's pretty quick with power bi right so that comes to the end of our webinar and we hope you enjoyed it and we'll see you again another week