The Ask: Datalogz helps lean organizations map all their data fields from across multiple databases in one place. Best part, it doesn't require access to your government/sensitive data. If you or somebody you know might be interested in the product, please reach out or forward them this article.
This week I had the pleasure of chatting professionally with my friend Logan Havern. Logan and I have a mutual friend in common, through my Venture for America network. Logan wanted to sit down to talk about funding, given my work. I prepped some answers to frequently asked questions and we deep dove from there.
Logan, who works full-time in JetBlue Airways in Data and Engineering, has been building a prototype of his data science tool, Datalogz, on the software platform Bubble. (Bubble is a powerful SaaS tool that allows people to build full web apps without code.) His product looked legitimate, but I was too unfamiliar with the data science space to understand it at first glance. So, the task fell on me to understand the ecosystem on our meeting.
My specialty in startups is business operations and technology. Although I understood the value of data science, I had seldom been a direct beneficiary. To put it into perspective, if you're a startup with less than 10,000 customers/emails, it's unlikely you're going to have the budget/priority to hire a data scientist as well as the accompanying software tools they need. In my mind, the need for data science comes post-Series A (raising $3-5m round on a $15-$25m valuation.)
Happily, Logan's patience, Google Meet, and their new Jam board feature, this is where I landed in mapping the various players:
In essence, the purpose of data science is to help me understand insights from my organization. As an organization expands, different teams start to house data in different databases. Marketing keeps CPM, CPC and CAC in one set, Finance keeps ARPU, AR, bills owed in another, and so on and so forth. Further,
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