Tricks of the Trade
Modern data teams have all the right solutions in place to ensure that data is ingested, stored, transformed, and loaded into their data warehouse, but what happens at “the last mile?” In other words, how can data analysts and engineers ensure that transformed, actionable data is actually available to access and use?
Tejas Manohar, co-founder of Hightouch and former Tech Lead at Segment, and I explain where Reverse ETL and Data Observability can help teams go the extra mile when it comes to trusting your data products.
It’s 9 a.m. — you’ve had your second cup of coffee, your favorite Spotify playlist is blaring in the background, and you’ve just refreshed your team’s “Marketing Analytics” dashboard for the third time this morning, just to be sure that the data checks out before your CMO’s weekly All Hands. Everything is (seemingly) right in the world.
Then, just as you are settling into your groove, you get the Slack ping heard around the world: “Why isn’t Salesforce updated with the latest numbers?”
If this situation sounds familiar, you’re not alone. In 2021, companies are betting big on data to drive decision making and power their digital products, yet up to 68 percent of that data frequently goes unused due to issues that happen after it is transformed in the warehouse.
All too often, there’s a disconnect between the numbers in your Looker or Tableau dashboards and what’s represented in your operational systems (i.e., your CMO’s Salesforce report), slowing down your stakeholders and eroding trust in data. We call this data’s “last mile problem” and it’s an all-too-common reality for modern businesses.
Fortunately, there’s a better way: Reverse ETL. In partnership with Data Observability, this new suite of data tools can help data teams unlock the potential of accessible, reliable data when it matters most.
If traditional ETL and ELT solutions like Fivetran and Stitch enable companies to ingest data into their data warehouse for transformation and modeling, Reverse ETL does just the opposite: it enables companies to move transformed data from their cloud data warehouse out into operational business tools. It’s a new approach to making data actionable and solving the “last mile” problem in analytics by empowering business teams to access — and act on —…
Continue reading: https://towardsdatascience.com/solving-datas-last-mile-problem-dbde5ce3825?source=rss—-7f60cf5620c9—4