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By combining Alteryx and Microsoft Power BI, organizations can streamline and accelerate the process of preparing and analyzing data. This provides a faster way to deliver an end-to-end experience for data access, preparation, analysis, visualization and consumption — delivering deeper business insight faster with a more complete set of data.
The RegEx tool is kind of like the Swiss Army Knife of parsing in Alteryx; there are a whole lot of ways you can use it to do things faster or more effectively, but even if you just use the blade it's still immensely useful. Sometimes that's all you need, but if you do take the time to figure out how to use a few other tools in that knife, you'll start to see that there isn't much you can't do with it .
If you are building a predictive model, inevitably you will want to analyze the effect that your independent variables have on your dependent variable. This article is meant to shed some light on the Alteryx-specific options for this type of analysis!
The Multi-Row Formula Tool functions much like the normal Formula Tool but adds the ability to reference multiple rows of data within one expression . Say, for example, someone was on the ground floor of a house and had a Formula Tool. They would only be able to talk to the people also on the ground floor. If they had a Multi-Row Formula Tool, though, they would also be able to talk to the people upstairs, in the attic, and in the basement as well.
The Find Replace Tool is one of those tools that goes relatively unused and uncelebrated until you stumble into a data blending technique that would be extremely difficult without it – at which point, it becomes your favorite tool in the Designer. You can find it in the Join Category and it’ll make easy string substitutions in your data that would otherwise require herculean effort to work around. Today, we celebrate Find Replace as a hero.
Is there a way to avoid the Cross Tab Tool from transferring all the input information alphabetically? Simply add a RecordID to your records and add the RecordID field as a grouping field in your Cross Tab Tool to keep the order!
A large component of data blending is applying mathematical or transformational processes to subsets of your data. Often, this requires isolating the data that complies with a certain criteria that you’ve set. The Conditional functions build expressions that allow you to apply processes to data that satisfy conditions you set.
You know that a huge part of conveying information to your audience is your visual presentation. Here's a way to increase the amount of information that can be shared with just a glance: Segment the data in a chart with color.
Alteryx has a full set of integrated predictive tools but even with developers working at full speed , it is hard to keep up with the R community. Sometimes users want to install and utilize their favorite R packages. This post demonstrates how to use and install additional R packages.
Far more than just a window to your data, the Browse Tool has a catalog of features to best view, investigate, and copy/save data at any checkpoint you place it. That introspection to your data anywhere in your blending gives valuable feedback that often speeds workflow development and makes it easier to learn tools by readily visualizing their transforms. Be equipped, and browse through the catalog of useful applications below!
For any macro or analytic app – one of the inevitable questions that you may encounter is “how do I configure this to do what I need?” For example, if you build a macro that checks if two fields are equal, but sometimes you want to ignore the case such that “A” equals “a,” and sometimes you want an exact match. This is where the Interface Tool Category comes to the rescue, with a super-tool called Check Box!
Did you ever play that party-game called 6-degrees of separation where you have to figure out how you are connected to someone famous in less than 6 relationships (or the movie version – 6 degrees of Kevin Bacon)? Well that game just got a whole lot easier with the Make Group Tool!
Between the RegEx , Text To Columns , and XML Parse Tools , the Alteryx data artisan already has an exceptionally robust selection of tools to help parse uniquely delimited data. However, there are still some data sets so entangled in formatting that it’s labor intensive to parse even for them. Enter the Find and Replace Tool , which captures the ability to find your nightmarish parsing workflows and replace them with sweet color by number pictures. Just kidding, it finds bad jokes and replaces them with good ones. Seriously, though, you could do both if you wanted to because this tool has the capability to look up a table of any number of specified targets to find in your data and will replace them with a table of specified sources. With the help of a few quick configuration steps, this tool can simplify some parsing use cases significantly.
Any time you want to get a good point across, it’s best to show your data. Show your data off in style in your reports or presentations by adding formatting to otherwise bland data with the Table Tool! Found in the Reporting Tool Category, the Table Tool will make it easy flair to your raw data, and give it the pop it needs to really sink in.
While the Join tool is easily one of the most used tools in Alteryx, it can also be one of the most misunderstood. This is even more likely true if a new user hasn’t previously used joins in any other data-manipulating platform or they are joining big tables where they might not be keeping track of the records inside the fields they are joining on.
Sometimes, a dataset will contain numbers stored as text. I order to do calculations using those numbers, the datatype will need to be converrted to a numeric data type. If the data is clean, changing the data type in a select tool can do the trick. Another option is to use the TONUMBER() function in a formula tool or multi field formula tool (if you have more than one field to convert).
One of the best things about Alteryx is the ability to read in multiple files very easily and automatically combine them into a single dataset. This becomes a bit trickier when dealing with files that have different schemas or Excel files with multiple tabs. Adding both multiple excel files with multiple tabs, and having the schema change within each tab takes it to another level.