#status/done/processed ![Cover Image](https://readwise-assets.s3.amazonaws.com/static/images/article2.74d541386bbf.png) ## Metadata Author:: [[Instacart]] Title:: Doing Data Science Right — Your Most Common Questions Answered Full Title:: Doing Data Science Right — Your Most Common Questions Answered Import Date:: 2023-05-13 Source:: #source/readwise/instapaper Source URL:: [Source URL](https://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/) Review URL:: [Review URL](https://readwise.io/bookreview/26339318) ## Document Tags:: [[Data Analytics]] [[Data Science]] [[Data Products]] ## Highlights - The first version of your product has to address what data science calls the “cold start” problem — ==it has to provide a "good enough" experience to initiate the virtuous cycle of data collection and data-driven improvement==. It’s up to product managers and engineers to implement that good enough solution. - Date:: [[2019-03-02]] - Find: [View Highlight](https://instapaper.com/read/1167833481/10333545) - Note: This was especially apparent when designing the credit limits system. When a customer signs up, how much info do we really need to have to be able to serve up a credit limit? - It's possible that you need data right away because your business is all about delivering data products. ==But it's more likely that your minimal viable product (MVP) won't be data-driven==. Rather, you'll be betting on an instinct and seeing if the market validates that instinct. In that case, prematurely investing in data acquisition and data science will cost you precious money and time that should go toward bringing your MVP to market. - Date:: [[2019-03-02]] - Find: [View Highlight](https://instapaper.com/read/1167833481/10333547)