Alteryx platform and its products
Alteryx platform is purpose-built to create data pipelines and leverage more insights easier than ever. With Alteryx, whether you are an analyst or data scientist, you can solve even the most complex analytic business problems, with less time and effort, to drive business-changing outcomes across your organization.
Alteryx has five different products – Alteryx Designer, Alteryx Server, Alteryx Connect, Alteryx Promote, Alteryx Datasets, out of which the most widely used are Designer, Server and Connect.
Alteryx Designer delivers a repeatable workflow for self-service data analytics, leading to quicker and deeper insights. Comes with a wide variety of tools for data transformations, data blending and machine learning – Python and R which gives us the flexibility to run AI or ML models inside the workflow.
Alteryx Server accelerates time to analytical insight and empowers analysts and business users across the organization to make informed, data-driven decisions. It’s the fastest and easiest way to deploy any workflow.
Alteryx Connect serves as a guide and simplifies the discovery and organization of information for analytics thus enabling users to spend more time collaborating and finding new insights.
Use Case 1
For Financial Services industry we leveraged Alteryx Designer to build a Fuzzy Matching workflow which automates the Business Analyst function by mapping the metadata between two disparate data sources and scoring them for alignment based on metadata name and datatype. The workflow is converted to macro and hosted in Alteryx Server to be used in our platform EXF Insights as a recommendation engine performing Fuzzy Match in real-time, reducing the time to integrate and master new data sources and targets by 40-60 percent. Issue_Name and Security_Name are analyzed based on the attribute name and underlying value and scored. All the relationships are cataloged and visualized for the Analyst as a starting point. The Business Analyst then has a palette of aligned data for which to do Source to Target mapping. BAs can do Root Cause analysis to go beyond just finding operational exceptions but applying rules to solve them, such as price tolerance exceptions arising from a corporate action.
Use Case 2
For the Manufacturing sector we used Alteryx Designer to build a workflow which is aimed at producing predictive analytics for fault monitoring, error prediction and preventive maintenance to the shop floor machines. The workflow performs sensor data pre-processing followed by python based ML predictions based on prescribed parameters and thresholds. The workflow was converted into macro and scheduled to train at frequent time intervals. Alteryx reduces the effort of building Predictive models and makes it available for everyone.
Use Case 3
For Healthcare industry address and credential changes for the Providers need to be continually captured, updated and verified against D&B data to ensure timely payments by the Payers and to avoid denials on claims. We leverage Alteryx Designer to wrangle, standardize and verify the source address data which could be in any format – excel, CSV, database connections etc. Also for the verified addresses get the spatial information for accurate results. Using Alteryx we enable geo-spatial coordinates by pulling the data from different data sources and taking standard address information and geo-codes.
Getting the data source from MongoDB by using the MongoDB Input tool to read and query the data from the MongoDB database.
Using Cass tool to standardize the addresses data and parsing the various address fields in the Cass configuration window for which we want the addresses standardized.
After getting the standardized addresses, applying formulae and spatial info tools to get spatial information such as area, centroid, etc. also for creating spatial points in the data using numeric fields to get longitude and latitude. Finally parsing the result into JSON by using the JSON parse tool and storing the result in the MongoDB Database.
Alteryx Designer allows us to prepare the data, blend and analyze it. We can easily enable geospatial data by pulling the data from different data sources and taking standard address information and geocodes.