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The early reality of working in a fast moving, data rich company
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Are obviously the one primarily charged with getting data into the organization. What was a day to day what was a, you know, typical week look like? How is it struck how is the time structured? What was it feeling like? It was a bit of a scary time back then for sure. So when I joined MoonPay, as I mentioned, we were very early on. The data team was small. We'd kind of hacked together some nice looking dashboards, but, ultimately, we didn't have much visibility into many things. We we were lucky that we were kind of in a good phase of the crypto market and we were launching things. It's it was fine if the data was even a week or two late, and we could say, cool. Things look great. But, ultimately, there's no way you can actually launch products and run a business. So we kinda went through the stage where we didn't have much visibility, and then it quickly ballooned into this phase where we had almost too much data. And that's kind of what Emily was alluding to is we had too much. We had to sort through things that were kind of popping up here and there from different stakeholders and trying to juggle that and find out, you know, what was actually important, what was driving the business forward was incredibly difficult. So it kind of my time move pay is almost in three phases. The first stage, not enough data at all. So hard to actually help, you know, guide the business. Then into the stage of, you know, almost peak euphoria of we have data. It's amazing. Let's get as much as possible as we can into Slack, which was also just then on a day to day basis, you know, also extremely difficult. Things used to happen. There'll be a one or two percent dip in one metric, and it'll be all hands on deck, which took away, you know, the whole data team's time and took us away from the more important things of our day to day jobs. And now we've gotten to the stage, thanks to the metrics tree, thanks to kind of a lot of change that Emily has made within the team where we've, you know, trimmed down this data and, you know, can focus on what's important. And then from there, the data scientist, go into that and actually apply our knowledge into streamlining things and finding what's important rather than just throwing, you know, paint to the wall or seeing what sticks type thing.