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What happens in the warehouse, stays in the warehouse. Or does it?

Data analysts are used to getting requests for data in Excel. It’s almost universally known – If you send someone a dashboard, they’ll ask for it in Excel.  But there’s a new request in town – “Can I get this data in Salesforce, and Hubspot, and Mailchimp, and Intercom, and….

Marketing, sales, and operations teams are quickly learning that a lot of very powerful stuff happens in the warehouse. And they want need that data where they work…ASAP of course.

At Hashpath, we are seeing more and more requests from companies who want to move their data from their warehouse to their SaaS tools.

A practical example

Let’s say an analytics team is responsible for calculating a Health Score for each individual customer. This data comes from a variety of sources: product data, payment information, survey results and support requests. 

In order to calculate this health score, the analytics team combines the data sources together in the warehouse and then adds customized transformation logic to produce the result.

Historically, Customer Success teams may have received the “Customer Health Dashboard” via their BI tool or downloaded it in Excel.  But wouldn’t it be better if the health score lived in the operational/marketing tools directly? If a customer writes in with a support request via Intercom, it would be good for the support agent to know that this customer might be at risk of churning or if they are a VIP customer. 

The problem is that the customer health score is only available in the warehouse and needs to be fed back out to the SaaS tools for use by business users.  This is one example of operational analytics.

Stepping back

For the last few years, business users have been focused on connecting their tools – moving and routing data from SaaS product to SaaS product to support their workflows.  Customer Data platforms (like Segment) and No-Code tools (like Zapier) have become wildly popular.

In parallel, data teams have been focused on centralizing and modeling data from these disparate tools. It’s now easier than ever to centralize data via the Modern Data Stack and analytics engineers have emerged to help companies model, transform, and make sense of this data. 

But now, more than ever, business users expect any data they see to be moved to wherever they want it.

It turns out that simply routing data between tools isn’t enough. Data has to be cleaned, combined, transformed and modeled to be trusted and useful. This process is now happening in the warehouse. Data teams are using tools like dbt to do this work.

The modern stack that we’re all used to seeing, with a new request entering the mix.

So how do you move data out of your warehouse?

No one – I mean no one – wants to solve this problem from scratch. Managing a “reverse ETL” of sorts sounds like a data engineer’s worst nightmare.  Luckily a new breed of tools is popping up to solve this problem. 

  • Looker actions: Send data from your warehouse to destination via pre-built integrations. Or create you own via the Action Hub
  • GetCensus: “the easiest way to sync your data warehouse to the apps you use. No engineering favors required.” 
  • Sync data from warehouse to sales/marketing tools with just SQL. Also includes visual interfaces for user segmentation and data governance.

If you’ve used one of these tools or are thinking about using one, I’d love to chat. As always happy to talk about data in general and I tweet about it a lot too.

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