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For our final Decision Modeling project, my team and I set out to construct a model that would help one of my group member’s companies assign office attendance by employee number. The resulting analytic application, built in Alteryx, allowed for dynamic inputs to achieve dynamic results. In a real-world application, the creator of the Alteryx workflow and analytic app could then seamlessly share it across the enterprise or automate the output to notify employees of their office assignments. In this use case, we used the basic capabilities of Alteryx Designer to apply business logic to an analytical process.
Some finance processes in the tax world require you to monitor the balance of various accounts over time. The problem is, just like your personal bank account there isn’t always a change in balance every day. Meaning if you were to plot this data on a graph there would be gaps in the information for which there was no change. The way to solve this in Alteryx comes in two steps, first we need to identify exactly what dates are missing data and create rows for them in our dataset. Then we need to run calculations on this data to determine what the balance should be on the days that had no change.
The Johns Hopkins University Center for Systems Science and Engineering has released the data they use to power their 2019 Novel Coronavirus Visual Dashboard. Since I was curious in exploring that underlying data to better understand the spread of the COVID-19 virus and seeing how Alteryx might be used to analyze that data for predictive purposes, I made a workflow to do just that.
In this article, we’re going to explore how we can leverage Alteryx to run analytics on fishing reports in Southern California. You’ll learn how to use web scraping to pull in data from 976 Tuna, macros to automate the cleansing process, spatial analytics to better understand where the fish are biting, reporting tools to visualize our analysis, and the Alteryx Server to automate this on a weekly basis.
As an experienced Alteryx user, it can be easy to forget that there is a learning curve for many folks just starting out. This article takes a look at a new Designer Cheat Sheet which was created to help new users get started.
You might want to (or need to) collect, store and process unstructured text items with associated metadata. Examples could be to set up a library for research purposes or to build a tool to watch the public discourse for relevant conversation pieces. This guide will showcase a just-the-basics approach to build one possible implementation of such a tool in Alteryx.
Having in-depth demographic information is often seen as the holy grail in the eyes of data analysts and their business stakeholders, and there is a good reason why: the context that can be added to your analytics, as a result, is hugely advantageous.