2 min readSep 5, 2020



“Why did the Facebook/Google/etc install conversion tank on Friday?” Have you ever been asked this question as a data team? I have and everyone I know in San Francisco data science has as well. This question asks for historical context into an abstract user behavior problem. You need to make it your team’s goal that this question is already answered automatically and presented to the business stakeholder before the end of day Friday. I’m not suggesting bringing in machine learning to detect any possible question. No, I’m suggesting your data team should build self-service and data products into the core functionality/actions of the company.

There is one thing that separates a successful data org from one constantly building dashboards and reactively fixing reporting on old data. I am not saying I do not build dashboards and occasionally need to put out fires or deliver low impact, last-minute critical business concerns that tomorrow won’t be important or remembered. I’m talking about building the data best practices, design and core infrastructure. I’m saying data teams must be building data products. My point is you need to get your org to the level on the data science maturity curve that you are building products that can be consumed by the rest of the company instead of quick-fix dashboards and analytics. To put it one more way before moving on to what this looks like, focus on building predictive tools and data insights as opposed to building infra that only reports explanations of what happened last week.

Courtesy of Gartner.

What are Data Products?

Data products refers more to how your team builds insights, models or infrastructure projects than who sees the result. Data products usually aid other internal teams with well-designed and executed product verses only ad hoc analytics.

  • Documenting them to allow for future repeatability and versioning in some common metadata store like Github.
  • Building a unified data model that has defined datasets, single source of data and summary tables.
  • Setting up flexible, self-serve BI tools and follow up data training so most questions are answered with existing dashboards.

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