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Past Analytics Excellence Awards

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Author: Mark Frisch (@MarqueeCrew), CEO

Company: MarqueeCrew


Awards Category: Name Your Own - Macros for the Good of All Alteryx Users


Describe the problem you needed to solve 

Creation of samples goes beyond random and creating N'ths.  It is crucial that samples be representative of their source populations if you are going to draw any meaningful truth from your marketing or other use cases.  After creating a sample set, how would you verify that you didn't select too many of one segment vs another?  If you're using Mosaic (r) data and there are 71 types to consider did you get enough of each type?




Describe the working solution

Using a chi-squared test, we created a macro and published the macro to the Alteryx Macro District as well as to the CReW macros  (www.chaosreignswithin).  There are two input anchors (Population and Sample) and the configuration requires that you select a categorical variable from both inputs (the same variable content).  The output is a report that tells you if your representative or not (includes degrees of freedom and the Chi square results against a 95% confidence interval).




Describe the benefits you have achieved

My client was able to avoid the costly mistake that had plagued their prior marketing initiative and was setup for success.  I wanted to share this feature with the community.  It would be awesome if it ended up helping my charity, the American Cancer Society.  Although this isn't quite as sexy as my competition, it is sexy in it's simplicity and geek factor.



Author: Scott Elliott (@scott_elliott) , Senior Consultant

Company: Webranz Ltd


Awards Category: Best Use of Alteryx Server


We are using the server to store Alteryx Apps that get called by the "service bus" and perform calculations and write the results into a warehouse where growers can log into a web portal and check the results of the sample.


Describe the problem you needed to solve 

Agfirst BOP is a agricultural testing laboratory business  that perform scientific measurement on Kiwifruit samples it receives from 2500 growers around New Zealand. In peak season it tests up to 1000 samples of 90 fruit per day. The sample test results trigger picking of the crop, cool storage, shipping and sales to foreign markets. From the test laboratory the grower receives notification of the sample testing being completed. They log into a portal to check the results. Agfirst BOP were looking for a new technology to transform the results from the service bus up to the web portal which gave them agility around modifying or adding tests.


Describe the working solution

We take sample measurement results from capture  devices. These get shipped to a landing warehouse. There is a trigger which calls the Alteryx Application residing on the Alteryx server for each sample and test type.  The Alteryx App then performs a series of calculations and publishes the results into the results warehouse. The grower is now able to login to the web portal and check their sample. Each App contains multiple batch macros which allow processing sample by sample. Some of the tests have a requirement for the use of advanced analytics. These tests call R as part of the App.  The use of macros is great as it provide amazing flexibilty and agility to plug in or plug out new tests or calculations. Having it on Alteryx Server allows it to be enterprise class by giving it the ability to be scaled and flexible at the same time. As well as being fully supported by the infrastructure team as it is managed within the data centre rather than on a local desktop.




Agfirst APP.jpg


Batch Macro:


Agfirst Batch Macro.jpg


Describe the benefits you have acheived

The benefits realised include greater agility around adding/removing sample tests via the use of Macros. We are able to performed advanced analytics by calling R and it futures proofs the business by enabling them to choose any number of vendors and not be limited by the technology because of the ability of Alteryx to blend multiple sources. It gives them amazing flexibility around future technology choices and it is all supported and backed up by the infrastructure team because it sits within the datacentre and they have great comfort in knowing it's not something sitting under someones desk.

Author: Alberto Guisande (@aguisande), Services Director


Awards Category: Most Unexpected Insight - Proving teachers wrong - Apples & Oranges can be compared! (thanks to Alteryx)


Describe the problem you needed to solve 

Our customer is a Public Transportation company, in charge of buses going around the city of Panama. They transport more than 500K passengers a day (1/6 of the total population of the country). Almost 400 routes, with 1,400 buses going around the city all days, working 24/7, reporting position every a few seconds. The company is supporting its operation with a variety of tools, but at the time to put all data together, they realized there was no "point of contact" in the data. They have to compare apples & oranges! Really? Why does the saying exist? Because you can't! So we started trying to do the impossible!


BTW, the business questions are pretty simple (once you got the data!): What route was every bus in, when every transaction occurred? What is the demand of every route? and for every stop?


Describe the working solution

Working with Alteryx, we were able to analyze data coming from three different sources, where the only common information was some LATITUDE & LONGITUDE (taken with different equipment, so the accuracy was, at least, questionable) at some random points in time. The data was received in several files:


  • Routes: Contains the ID & the name of every route. Stop Points: Containing every bus stop, its LAT & LONG, and the stop name
  • Pattern Detail: Containing every route, its stops and the sequence of those stops in a route
  • Some remarks: A lot of stops are used by different routes, and there are some stops, where the bus pass through, that are not part of the specific route the bus is at


So far, the easy part! We managed very easily to get all this info together. Now the tricky part: There mainly two operational datasets: AVL (Every position of every bus, every n seconds, where n is an arbitrary number between 0 and what the piece of hardware wanted to use). BTW, a huge amount of data every day.


Transactions: transactions registered in time, in a bus. As you may infer, there are no data in common that allow us to match records beside an arbitrary range of latitude and longitude in some random time ranges. Because of how everything is reported, the bus may be passing in front a stop that is part of another route, or stopping far from the designated stop.


Describe the benefits you have achieved

With this solution, the company can start analyzing activity per route, demand per bus, route, stop, etc. Without Alteryx, this customer information still be looking like apples and oranges! We were able to make it sense and allow them to use it to get insights.


Colorful note(and some ego elevator) : 5 other vendors took the challenge. No other one could reach a glimpse of solution (of course, "no Alteryx, no gain").





Author: Michael Barone, Data Scientist

Company: Paychex Inc.


Awards Category: Most Time Saved


We currently have more than two dozen predictive models, pulling data of all shapes and sizes from many different sources.  Total processing time for a round of scoring takes 4 hours.  Before Alteryx, we had a dozen models, and processing took around 96 hours.  That's a 2x increase in our model portfolio, but a 24x decrease in processing time.


Describe the problem you needed to solve 

Our Predictive Modeling group, which began in the early-to-mid 2000s, had grown from one person to four people by summer 2012.  I was one of those four.  Our Portfolio had grown from one model, to more than a dozen.  We were what you might call a self-starting group.  While we had the blessing of upper Management, we were small and independent, doing all research, development, and analysis ourselves.  We started with using the typical every day Enterprise solutions for software.  While those solutions worked well for a few years, by the time we were up to a dozen models, we had outgrown those solutions.  A typical round of "model scoring" which we did at the beginning of ever y month, took about two-and-a-half weeks, and ninety-five percent of that was system processing time which consisted of cleansing, blending, and transforming the data from varying sources.


Describe the working solution

We blend data from our internal databases - everything from Excel and Access, to Oracle, SQL Server, and Netezza.  Several models include data from 3rd party sources such as D&B, and the Experian CAPE file we get with out Alteryx data package.


Describe the benefits you have achieved

We recently have taken on projects that require us processing and analyzing billions of records of data.  Thanks to Alteryx and more specifically the Calgary format, most of our time is spent analyzing the data, not pulling, blending, and processing.  This leads to faster delivery time of results, and faster business insight.

Author: Francisco Aristiguieta, Audit Specialist


Awards Category: Name Your Own - Best Engagement From Management


Describe the problem you needed to solve 

With operations in all time-zones and more than 10,000 people, my company needed an effective way to ensure we don't have rouge employees exposing us to corruption.


Before our Alteryx tool, we had a very complete compliance program focused on prevention; but we did not had a viable method to verify the mandates were understood and followed across the globe.


Describe the working solution

Our plan was to every month inspect every payment the company had done for signs of potential problems. We would do this by searching each invoice line for keywords that could represent problems.


The plan was simple, although the implementation would have been an enormous problem if we had not had Alteryx.  Here are a few of the (multiple) humps Alteryx helped us address:


1. Payment information was broken in multiple tables. Even if we would be working with Oracle data, our IT department insisted that we worked with off-line copies of the tables instead of connecting directly. This made our data source a series of multiple monthly csv tables, where the tables had no meaning on their own.


>> Importing all files in a folder, and using "Unique", "Filter", "Select" and "Join"; allowed me to conquest this first challenge.


2. I used "find replace" to do the keyword searches; which was a great step forward. Sadly, in many cases our chosen "keywords" were part of innocent words, which caused a plague of false positives for follow-up. i.e. the word "magenta" would be caught when we searched for "agent". 


>> Using "Formula" to set-up some "If-Then-Else statements", and carefully using "and" to set-up my conditions, I was able to safe list some of these innocent words and get rid of a large portion of these false positives.


3. Because the outputs of each run is stored separately, my last big challenge was making sure I didn't report/investigate the same transactions month after month as we re-ran the tests.


>> Solving this was easy through a collection of file imports, "union", and "join" to compare the current results to the recent past (keeping only new hits) in my analysis.



Describe the benefits you have achieved

Even if (after follow-up) the tests have not found any real problems, we are very happy to finally have peace of mind regarding how our employees are behaving across the world. This test was a great way to demonstrate the value of analytics to the more traditional pockets of our company, and its results have been greatly celebrated, giving me and my team some great exposure to the highest levels of my organization. Here are a few quotes from our clients:


  • "This is another SUCCESS for the Data Analytics initiative.  There is NO WAY we would have ever even known this was an issue without this capability "
  • "I believe that this proactive approach is clearly one of the most significant advances in early detection techniques that (the team) has implemented in quite a while"
  • "The mere fact that the word will get out that we have tools like this to potentially catch such payments should be a powerful deterrent"
  • "Our analytics practices have changed the way we (work) increasing our effectiveness and efficiency"
  • "I am looking forward  to work on another (analytics) initiative with (the team)"