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Building a Data Practice from Scratch

Building a Data Practice from Scratch

The first data hires at an early stage startup face numerous challenges — an infrastructure built to run the business but not analyze it, an organization hungry for information without a process for requesting and prioritizing it, and little documentation on how anything is done. What should they do first?

Adding Context to Your Analysis with Annotations

Adding Context to Your Analysis with Annotations

Anyone who has worked in digital analytics will tell you that day over day performance can be volatile. Shifts in marketing mix can cause fluctuating e-commerce conversion rates, new feature launches can lead to sudden and temporary swings in engagement rates and onsite bugs can result in anomalies in abandonment rates. Some of these scenarios can be diagnosed through extensive segmentation of data. Others, like a dropped analytics snippet or a bug with your payment processor, cannot be so easily uncovered. The simplest thing to do when events like these take place is to take a mental note and count on your memory for when you inevitably have to revisit that data in the future. Unfortunately, taking a mental note isn’t a scalable solution. While it’s not the most thrilling task for a data team, keeping a record of the online and offline events that affect your business is a practice that is well worth the (small) time investment.