Use Case

Product Telemetry Data Management


Software companies use telemetry data sent back by their products to understand how customers use those products. But telemetry generates large quantities of data in multiple formats, posing a data management problem. By automating the processes of gathering and distillation, companies can incorporate telemetry data to their product development process.

Top-Line Growth

Invest product engineering time into the features and capabilities that customers use most often

Customer Experience

Design products around customer needs to improve the customer experience

Risk Reduction

Discover issues customers are having before problems become widespread

Business Problem

Software companies gather telemetry data from their products to better understand how they perform in production and how customers use them. The data, rich in clues about customer experience and business impact, is useful in product development and planning. Telemetry data is also useful in detecting and diagnosing problems before they affect customers. However, the variety of sources and formats makes it difficult to collect, blend, distill and store all the data a single company receives.

Alteryx Solution

With a data pipeline, marketers and engineers can collect user data, automate data cleansing, store data in an accessible format, and provide distilled insights to users who need them. At the same time, the pipeline can mitigate the risk of disrupted data flow. Advanced analytics provides an end-to-end solution for automating processes like telemetry data collection, with customized output for each type of user.

With Alteryx, you can:

  • Easily connect to multiple sources like MongoDB or AWS and multiple types of customer telemetry data
  • Create a custom batch sequence that prevents workflows from failing and requiring restart
  • Create custom, automated processes for data cleansing that output the same data in multiple formats
  • Automate connection and loading of data into Tableau dashboards for insight into customer data and health of data pipeline
  • Store distilled data in an easily accessible data warehouse, enabling end users to access and perform their own analytics.

1 – Batch Sequence

Design workflows with multiple steps that can run independently of one another

2 – Automate Data Cleansing

Fully customize and automate transformation of data before storing it in a repository

3 – Data Communication

Parameterize outputs to fit the needs of users – Tableau dashboard or distilled data sets – and enable self-service analytics


Additional Resources

Starter Kit for AWS

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Starter Kit for Snowflake

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Starter Kit for Tableau

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