What is your primary use case for Alteryx?

I had a couple of experiences using Alteryx and KNIME for simple database relations. It provides some reports, develops some flows, and of course, extracts reports. We do not have a native connector to SAP, however, I structured SAP ERP databases for basic tables from purchasing processes. I related these tables and made calculations. Due to the large number of rows, I converted files to a file type from Alteryx, speeding up the reading process from hours to seconds. During the flow, I saved the process and performed only the part not previously saved, processing thousands or millions of lines, and then built the output report with the required calculations. For the basic processes, it's fast, however, learning the syntax for calculations takes time.

My primary use case for Alteryx is ETL more than analytics. I mainly use it for loading data from one server to another and loading files to databases. It is good for data integration. I find it very user-friendly compared to tools like Informatica PowerCenter. It allows me to easily create workflows and deploy them.

Alteryx is primarily used for data transformation and ETL processes within our environment. We also utilize its machine-learning capabilities for regression and classification tasks. Our team leverages it for back-end calculations to enhance visualization performance.

I use the platform for a variety of tasks. For example, I extract data from our Salesforce system and prepare it for analysis. It allows customized monitoring of the pipeline. I often prepare data to be imported into Power BI for further analysis. I also use it for personal data tracking and extracting data from our internal time-logging tool to monitor project progress with customers. It helps me stay updated without manually logging in and reviewing reports that may not contain the precise information I need.

In our team, we use Excel for a lot of calculations and automation workflows. We retrieve data from the database, perform calculations, and then distribute the results to various departments or customers.

We use the product for data transformation and analysis. We primarily use it for data preparation, analysis, and sometimes generation. So, it covers a wide range of tasks beyond just loading data.

I primarily use Alteryx to build process prototypes, exploring potential business benefits before scaling up and automating. It helps me handle various data sources, apply transformations based on business rules, and push outputs to the cloud. In the past, relying solely on documentation didn't capture the essence, so I used the solution through various approaches. Now, I use it as a live provider and send it to the IT team.

I am into tax automation. My clients have their data sets in different ERPs, such as Oracle and SAP. I design workflows in Alteryx to pull data from different sources, transform it into a nice layout, and perform data validations. We have 30+ tax registrations in other countries. This is important because tax data must comply with regulations in different countries. I also develop pre-agreed adjustments to the client's data and push the data sets into another system through APIs and SAPPs. From there, I can produce the tax returns and valuation outputs, which I present to the clients in a dashboard.

We utilize Alteryx for the purpose of transforming data to generate a report for data analysis that predicts the number of customers who will upgrade or downgrade within a specified time frame. After creating the workflow and building a dashboard on it, we set it up on a client-server to run live. When new product data is introduced, we fetch it directly from the database. This process is based on Microsoft SharePoint, and we have specific nodes for this purpose. With the appropriate access, the node will extract the data from Microsoft SharePoint and transform it into the desired format, which we then use to build KPIs and dashboards.

We use Alteryx to modify and modernize data based on different types of data requests. The data and the data source may be in different forms, resulting in unstructured data in the database. We use Alteryx to give good shape and size to such unstructured data.

I use the solution to build an ETL workflow.

The biggest use case for the solution is that it allows you to it allows me to combine data from different sources and different formats to create a unified data set to answer business questions. I can take data from my general ledger and combine it with my expense report data or with external data or with different parties or different questions, and it could all be in a different format. For example, one source can be from a database, one can be an Excel file, one can be a PDF, or a CSV file. Alteryx lets me very easily put them all together and create one database.

Our primary use case for the solution is merging multiple data tests and utilizing various data sources.

We have multiple use cases, but the most typical use case is to clean the data and use it for reporting.

It is mostly used for data validation rules. For example, for many legacy systems files, we check whether the count in one file is matching the logic that we have in another file. If it doesn't, then we fill that particular data load. The ultimate destination for that is a database, but to avoid data mismatch or the garbaging of data because someone didn't put files in a proper format, we are doing the validation with Alteryx. So, it's basically for data cleaning and removing some line characters from the data in the files so that there are no data issues while using SQL Loader.

We use this solution mostly for data integration.

We used the solution to design our models and databases, and then we used it to automate it through Excel.

I'm a contractor who works on different teams. Right now, I'm working on three different projects.

We are using Alteryx mostly for mission learning, forecasting, and predictive modeling use cases.

Alteryx is typically used for data preparation and blending. I have clients who use it for Special Analytics as well as Churn Analytics.

Alteryx is mainly used for data workflows and data preparation.

We have our data in several ACP-based systems, so we rely on Alteryx to extract the data from the system and build the data modeling in Alteryx. There isn't a cloud version. It is on-prem, but we can deploy it on the cloud, too. We do on-prem development and deploy it on the cloud.

The use cases range from basic EPL to predictive analytics and spatial analytics.

I primarily use Alteryx for connecting to various data sources, data transformation and modeling, and preparing reports or extracts or data that we can visualize in other tools.

At the time, I used the solution for data preparation and data handling, which is cleaning the data and then working with the data, and linking different data effects. The solution is on-premise. I use it on my desktop.

I believe we are using the new version. I use the solution for development and certain financial projections, such as making a business plan. I employ the solution for the amalgamation of big data. . I also use the solution for certain analytics deployment, such as that found in the commercial arena, for ascertaining the projections of sales.

I'm a reseller, so I have plenty of use cases. I don't have my own use cases. I actually solve problems for other customers. We have a few use cases with Alteryx - one for supply chain and one for finance as a whole. There is a very large amount of use cases for finance.

We have very similar use cases with Alyterx, Knime, and Trifecta. We are taking the data from disparate systems and transforming and making the data available for further visualization.

Most of my use cases have been around just helping customers out, specifically in the financial sector. Within our company, I've been automating the reports, project reports, and using it mostly for the automation of weekly reports in our company.

I use Alteryx for integration, data integration, and advanced analytics with several companies.

This is a data science solution, more like an ETL tool where you have data that you want to convert or transform. I'm a solutions architect and support the platform. The application is being maintained by a different team. We are customers of Alteryx.

I use this solution for KPIs and trending.

I have multiple clients that are using this solution and the use cases can be very specific to the domain they are coming from. I am an Alteryx designer and I have been trying to fine-tune the operations. Most recently I have been getting more involved with predictive analytics. I am transitioning from the ETL to an advanced analytics platform and then doing the seamless integration with the BI tools. These are the areas I have been exploring and currently, I am only working on use cases that are very specific to this area.

The primary use case of this solution is mainly for advanced analytics, including some bison-scripts for the machine learning on data, as well as data cleaning and data processing activities.

The solution is primarily used for data preparation, data cleansing, and, in some cases, predictive analytics. That said, the major use is in the preparation of data.

We were analyzing data trying to find anomalies with the servers and things that were going wrong. Normally in what we do, it can be a very manual process looking for that kind of stuff. Therefore, using this product, I created a workflow that went out and looked across all devices for problems that are happening, and managed to cut down the number of issues that were being created daily.

The solution allows us to create some reports that my organization uses, that my team uses. We also move some data from one particular legacy ERP into a shared SQL server to mix information from defense applications into one repository for analytic purposes.

We have several Alteryx certified analysts on our team, and we serve customers who want Alteryx. We don't use it in our company.

We primarily use the solution for automation, data cleansing, QA, some data science and machine learning software for the company.

We primarily use the solution for data processing and ETL.

I am the head of investigation on-site, I use the solution every day for merging and other management of records. I also export data to individuals that need the information we store, such as facilitators.

Alteryx is one of our primary tools in our company and we use it mainly for future analytics. We are primarily trying to determine what the past has looked like and then apply predictive analytics.

We use Alteryx for aggregating different Excel data and doing general analytics. Within our company, there are five employees using Alteryx.

We primarily use the solution for all machine learning projects, including perspective molding. We're using it for our marketing efforts.

Our primary use case is for ML related and deep learning related things like data cleaning and and then later we work on machine learning. I'm a business analyst and we are customers of Alteryx.

We are mainly using Alteryx for data preparation and processing data. We are also monitoring for some of our customers, in product analysis, regressions or customer segmentations, and column correlations analysis.

We're using the solution primarily to clean up some of our customer CRM data and to integrate it with some of our Salesforce requirements.

We primarily use the solution for ETL purposes and also for a lot of our modeling and scheduling.

We are using this solution mostly for ETL purposes, and we have just started using advanced analytics, demand forecasting, and some supply chain optimization tasks.

We have many use cases for this solution. For example, we used it for data prep and blending as well as machine learning. We built customer 360, have used it for labor optimization, and demand forecasting.

We primarily use the solution for basic data analysis and forecasting.

Our clients buy the product license and then ask us to configure and set it up for them. We also provide training on product use and act as troubleshooters if customers have problems. The company has a 15-member team that provides training and consulting on multiple technologies. We have clients who are from tax, audit, and automation. They also use Automation Anywhere and Robotic Process Automation Application. There are three primary use cases for this product. Firstly, we have a case study that involved lot of buy and sell transactions in Dubai (real estate). We wanted to create a model that takes up all the data sets that are frequently upgraded, and then be able to extract a ballpark transaction value based on multiple parameters. A big task was to sanitize the data including how to handle incomplete data sets. Secondly, it is used to automate reconciliation of tax related system entries. For example, or for tax reconciliation such as a client in India where there is something similar to VAT called GST, a requirement to reconcile different transactions and make sure both party's accounting books match. Finally, there is a case where you have your own accounting software such as SAP and it has to talk to another software application, which is not SAP compatible so we use Alteryx.

The team uses Alteryx for many things because they code various algorithms to create different dashboards. These are used as solutions for various things from sales to customer experience to whatever else the customer needs. So there are many areas where the team is using the product. There is not one, but as of now, there are some 30 different use cases that we have.

My company uses Alteryx differently for each of our clients' individual cases.

I am using the product for making machine learning models for particular uses that we have to focus on analytics.

We use this solution for self-service data preparation from data warehouses and data marts.

Data blending and complex data manipulation and augmentation.

ETL.

It collects data and manipulation and some Geo-spacial mapping.

I implemented Alteryx for a logistics company. It helps clean messy data and provides spatial analysis.

* Data acquisition, accessing all types of data services no matter the format or vendor. It is connected. * Data blending and descriptive, creating consolidated data sets, such as joints, unions, summarize, and all basic and advanced data manipulation tasks. * Advanced analytics, like predictive and prescriptive analysis, which are very good tools. * Preparation It involves reading multiple data sources, in different formats, reshaping and blending them together to answer business questions. We generate models for predictions and optimization.