Grinding Gears

Generating datasets for machine learning Preparing data and generating datasets is a crucial step to train a machine learning model. If you are lucky your data might come from a single .csv file. However in most cases pulling together the input features to train your machine learning model will require combining datasets from different sources. Combining data from different sources manually can be a time consuming process, prone to errors.  … Continue reading

This blog provides four useful SQL snippets you can use to debug poor Redshift performance. Continue reading

Over the past few weeks, we’ve configured BigQuery to enable us to combine our Google Analytics (GA) front-end data with our internal back-end data. In this post I’m going to talk about why we needed to do this, how we went about it and what we are hoping to achieve as a result. What’s the problem? Historically, two separate systems have been used at FreeAgent to track, store and analyse… Continue reading

Reporting Data at FreeAgent We keep track of a range of customer behaviours in the FreeAgent application as a means to understand engagement and to formulate and measure our OKRs. Examples of this include support requests raised through our ticketing system, Zendesk, the scores and comments provided by our users during NPS surveys and event data detailing specific actions taken within the application. As a concrete example of the latter,… Continue reading

Data is at the heart of our business. We use data to make business critical decisions on a daily basis. It is important that this data is not only accurate but also available when required. Traditionally reports would be generated at a set schedule which made it difficult to decide on next steps in a timely fashion. New technologies like Amazon Kinesis Data Streams enable us to generate these reports… Continue reading