Data pipelines automate the flow of data from one point to another. In a data pipeline, you start with defining how data is collected, and in what schema it should be collected. Then, you can automate the process of how to extract the data you need from the inbound pipeline, combine it with other data, and validate your team’s KPIs by comparing the data to your baseline metrics. This automated process reduces the risk of not collecting the correct data, and having to manually sort through the data you have collected.
Using Your Data for Experimentation
When software development teams run experiments for their products, the first thing they need to do is collect baseline data. This can include current conversion rates, average order value, click rates, etc. Once you have a baseline, you can then set a hypothesis of what you think will happen when you add a variant to the existing experience. If you are running an A/B test, for example, half of your population will have the existing experience (the control) and half your population will have a new experience (the experiment). When data starts coming in through the data pipeline from both the control and the experiment, you can compare the baseline data from the control to the experiment. If the experiment gives you better results for your KPIs, you can confidently release that experiment to the rest of your population knowing you are bettering the user experience. However, if the data coming in from the data pipeline shows a decrease in metrics, then you can confidently end the experiment knowing it would have been harmful to your user experience.
Establishing Causality between Features and Metrics
By using a powerful data and experimentation dashboard, like Split, you are able to drive deeper insights with advanced analytics. Because all business decisions should be based on data, you need to have a visualization of what your users are doing, and how they are performing based on the experience they get. With Split’s statistics engine, you can establish causality between feature releases and company metrics, and you can add as many variants as you want.
Powerful Data Pipeline Means Better User Experience
The more powerful your data pipeline is in handling your data, the better your user experience will be. The best data pipelines will automate the influx of the data from your customers, transform it into the schema you need, and make it easy to assess how your features are performing. With Split’s integration with Segment, you can collect the data you need, and send it to analytics, marketing, and any other stakeholders. You can ingest the user data you collect from Segment to power your A/B tests and feature release alerts. This data can also be used to send Split impression data to your warehouse or third-party applications. You should also be able to store data for future use in case you want to collect baseline data for another experiment later on. These properties of a strong data pipeline make for a solid foundation for A/B testing and experimentation.