Should I work as an analyst for a big company or a startup?
Where data analysis provides the most value
When you are deciding where to apply your analytical skills, choosing a big, established company or a smaller startup is one of the biggest decisions you will make. It will affect what technologies you work in, how you can be most valuable to the organization, and how you spend your day.
Something I didn’t appreciate enough starting out is that the opportunity to be a “pure analyst” only exists at relatively big companies that 1) have a lot of data to analyze 2) have the infrastructure in place to have clean data warehouses for analysts to play in and 3) have big enough business that optimizing them through analysis yields enough benefit to cover the analyst’s salary.
Big Companies benefit more from small optimizations
At the extreme end of the “big company” spectrum you have Amazon, who has an enormous amount of data and a huge business. If a clever data analyst figures out how to improve conversion rates by a few tenths of a percent, that’s worth tens of millions of dollars to Amazon.
Data analysis is great for situations where a small improvement can lead to big outcomes. As such, Amazon can pay hundreds of data analysts high salaries to query and analyze data each day, and if as a group they can optimize the business even a tiny bit it will pay off because of Amazon’s scale.
But you can learn more about other functions at a small company
On the other hand, a small e-commerce company that generates $1 million in cash flow each year stands to benefit much less from small optimizations. If the analyst finds something that improves conversion rate by 5% (a huge win!) that might only mean an additional $50k for the business that year, barely enough to cover the analyst’s healthcare.
That’s not to say that a small-ish business can’t benefit from hiring a data analyst. But the analyst will have to do a lot more than just analyze data to provide value to the organization. They will set up internal processes, automate tasks, and make the data the business is taking in cleaner and more structured. Their day-to-day will look more like some combination of product manager and data engineer.
So if you really love working in SQL and R or Python and having lots of clean data to play with, you need to apply to relatively big companies. For technology companies, this means bigger than 100 or so employees, and for less data-focused industries it is probably 2 or 3 times bigger.
On the other hand, if you want to have a more diverse set of responsibilities and become more of a generalist, you can target smaller companies. You will get to help build out their analytics capabilities and learn a lot about how data and data teams should be structured to best help the business. For tech companies, this might mean they have 25 employees or so.