Dropbase
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Dropbase

When’s the right time for startups to get a data warehouse?

We cover the quantitative (the IPO model) and qualitative factors to help with your team’s data warehouse decision.

  • You’re in a fast-growing startup fresh out of funding and you now have to tighten up on reporting to your angel investors.
  • Your company is thinking of an expansion to a new location and wants to consolidate its data. Or,
  • The lean startup you’re in spends way too much time manually collecting, cleaning, and visualizing data from your various analytics platforms … valuable time away from polishing your product

Quantitative factors for the data warehouse decision — IPO Model

Input factors

  • Data source count: If your team solely uses your application’s database as an input, then you likely don’t need a warehouse. But if you use your app’s database, CRM data, and product/marketing analytics then you have a high data source count. The higher the data source count, the higher potential need for a data warehouse.
  • Total time spent updating: This factor is a combination of the time it takes to update one data source times how often you update it within some time period (usually a year). e.g., let’s say you load data from Salesforce monthly and your suppliers once a week to your database. They each take 10 mins and 20 mins to give a total of 19.3 hours/year. You can then multiply this with your wage rate to understand the cost of manual data updates.

Process factors

  • Query complexity: If your business is relatively simple and the answers you want from your data are not complicated, then you have low query complexity. For example, if you want to know the number of units shipped by supplier.
  • Data type: For our purposes, there are two types of data. Transactional data are your everyday, operations related data. e.g. Sales broken down by channel, supply confirmations, number of log-ins, etc. Analytical data are an aggregation of transactional data to help drive insights. This could be linking sales broken by channel to user log-ins on mobile or anything that helps connect your data.

Output factor

Qualitative factors for the data warehouse decision

In Summary

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