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?
Here's where Reverse ETL, the newest kid in the modern data stack, can
help teams go the extra mile when it comes to trusting your data
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
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
goes unused due to issues that happen after it is transformed in the
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
this new suite of data tools can help data teams unlock the potential of
accessible, reliable data when it matters most.
What is Reverse ETL?
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---transformed data
directly in the SaaS tools they already use every day.
Reverse ETL pipelines can be custom-built, but like many data
engineering challenges, they require significant resources to design,
build, and maintain. Reverse ETL tools make it possible for teams with
less data engineering resources to design and build pipelines using only
SQL---no third-party APIs or custom scripts required.
Simultaneously, Reverse ETL reinforces the role of the data warehouse as
a source of truth while still democratizing access to data by bringing
it out of dashboards and reports and into the tools that sales,
marketing, and customer success teams are already using.
Reverse ETL powers operational analytics
By democratizing access to data and making data more accessible, Reverse
ETL is powering a new paradigm known as operational
practice of feeding insights from data teams to business teams in their
usual workflow so they can make more data-informed decisions. Reverse
ETL "operationalizes" the same data that powers reports in a BI tool by
ensuring it's accessible and actionable in downstream SaaS tools.
Operations and analytics teams are increasingly leveraging this new
approach to pipe transformed data from their cloud data warehouses into
their CRMs (like Salesforce), marketing automation tools, advertising
platforms, customer support and ticketing systems, and, of course,
Slack. This makes the vast amounts of customer data being collected and
stored in warehouses more accessible to analysts and business
intelligence teams, while ensuring that data engineers cover their bases
when it comes to delivering accessible, actionable data to their
Of course, as more and more data is being generated (and made
actionable), this leads to one crucial question: can companies trust
The risk of data downtime
Whenever companies increase their collection and use of data, the risk
when data is missing, inaccurate, or otherwise erroneous---increases as
well. And broken pipelines, delayed ingestors, and downstream impacts
all become more urgent when direct customer experiences are on the line.
Consider the possible scenarios:
If out-of-date data is powering customer support communications, teams risk sending irrelevant messages at sensitive moments in the customer lifecycle.
If data powering your sales sequences (such as the completion of a free trial) is missing or delayed, then timely messages won't be sent and opportunities may be missed
If the pipeline sending customer data to your advertising platform breaks, ad spend can quickly veer off-track---leading to missed revenue or higher customer acquisition costs
Beyond the concrete business impact of poor data quality, internal teams
may lose trust in data if downtime occurs regularly. For companies
working to build a data-driven culture, this trust is a precious---but
This is exactly why companies investing in Reverse ETL shouldn't skip
the final layer in the modern data
What is Data Observability?
applies the principles of DevOps and application observability to data,
using monitoring and alerting to detect data quality issues for the
Freshness: Is the data recent? When was the last time it was generated? What upstream data is included/omitted?
Distribution: Is the data within accepted ranges? Is it properly formatted and complete?
Volume: Has all the expected data arrived?
Schema: What is the schema, and how has it changed? Who has made these changes and for what reasons?
Lineage: What are the upstream sources and downstream assets impacted by a given asset? Who are the people generating this data, and who is relying on it for decision-making?
Simply put, data observability helps data teams ensure that their
pipelines and assets are accurate and trustworthy. Monitoring and
alerting through data observability
help ensure that when incidents do occur, the responsible data team will
be the first to know---and can intervene with business teams to prevent
downstream impacts of unreliable data.
How Reverse ETL and Data Observability work together to deliver trustworthy data---and preserve data engineering resources
For overworked data engineering teams, Reverse ETL and Data
Observability save valuable time and resources by democratizing data
access---while ensuring reliability and providing visibility into how
and when data is put to use.
Reverse ETL products
provide developers with change logs and a live debugger to enhance
visibility into operational data flows. Alongside Data Observability's
automated lineage and end-to-end monitoring of data assets and pipelines
throughout the entire data lifecycle, both tools provide increased
visibility and understanding of how data is accessed and interacted with
throughout the organization.
A live debugger, highlighting the API calls associated with each row of
Your data tools shouldn't be a black box, which is why leading Reverse
ETL tools provide a live debugger for understanding the changes and API
calls they make on your behalf. With Reverse ETL, business teams can
access data directly in their preferred tools, acting on customer
insights and events swiftly and designing workflows that automate
data-driven processes. Reverse ETL tools act as the glue between data
and business teams: data teams own the data models, and then business
teams can use UIs like a point-and-click Audience Builder to define the
data they need in their tools without needing to know SQL. This frees up
data engineers from one-off tasks while enabling business teams to
self-serve their data.
Reverse ETL solutions allow marketers to visually filter data models
And with Data Observability, those same teams can trust that the data
powering their customer experiences is reliable, accurate, and
up-to-date. With automatic, end-to-end coverage of your entire stack,
Data Observability supplements traditional testing by monitoring and
alerting for issues with your data at each stage of the pipeline across
five key pillars of data health: freshness, distribution, volume,
schema, and lineage.
Modern Data Observability tools can even help you root cause data issues
in real-time, before they affect business users in both BI dashboards
and SaaS solutions further downstream by centralizing all contextual,
historical, and statistical information about critical data assets in
one unified platform.
For instance, one common cause of data downtime is freshness -- i.e.
when data is unusually out-of-date. Such an incident can be a result of
any number of causes, including a job stuck in a queue, a time out, a
partner that did not deliver their dataset timely, an error, or an
accidental scheduling change that removed jobs from your DAG.
By taking a historical snapshot of your data assets, data observability
gives you the approach necessary to identify the "why?" behind broken
data pipelines, even if the issue itself isn't related to the data
itself. Moreover, the lineage afforded by many data observability
solutions gives cross-functional teams (i.e., data engineers, data
analysts, analytics engineers, data scientists, etc.) the ability to
collaborate to resolve data issues before they become a bigger problem
for the business.
While neither Reverse ETL nor Data Observability can save you from all
the early morning pings your stakeholders sling at you, taking a
proactive, end-to-end approach to data access and trust can certainly
help you navigate the "last mile" of the data journey -- with or
without the perfect playlist.
Curious how Reverse ETL and Data Observability can unlock the potential
of your company's data? Try out Hightouch directly for
or schedule a demo with Monte