Real-world success stories illustrate the benefits of a modern data stack, from lower engineering costs to greater data literacy.
At Fivetran, we’re firm believers in the value of a modern data stack (MDS) for organizations of all sizes. By abstracting away infrastructure management and pipeline maintenance while accelerating query runtime, an MDS will expand your analytics capabilities and simplify the requisite data engineering.
The first component of the stack, automated data integration, eliminates the need to build and maintain data connectors or normalize data; the second, a cloud data warehouse, enables extremely rapid query speeds while separating compute and storage; and the third, a modern business intelligence tool, makes it easy to visualize data and generate dynamic reports and dashboards that can be shared company-wide.
Whatever the size of your organization, this suite of tools will improve your analytics program across seven important metrics. We describe each one below, and offer real-world examples from Fivetran customers.
A modern data stack can reduce your data engineering costs by 90% or more, primarily by eliminating the need to build and maintain data pipelines or normalize data from denormalized APIs. Prebuilt, fully managed data connectors launch in minutes and deliver ready-to-query data to your preferred data warehouse or destination.
Media and telecommunications company Ignition Group is a great example. When it looked to improve its analytics by centralizing data, its first option was using in-house resources to bring data into its existing SQL Server warehouse. It decided against the DIY route after evaluating a modern data stack anchored by Fivetran and cloud data warehouse Snowflake. Ignition Group’s Chief Digital Transformation Officer Russell Stather explains:
We had initially planned to bring our data sources into the existing SQL Server warehouse. This would have taken the efforts of three people across two years, and would have cost an estimated 6 million rand [~$400,000] just to get us to where we got with Fivetran in two months.
Document authentication software provider DocuSign had a similar experience. Here’s how Senior Manager of Business Intelligence Marcus Laanen describes the engineering challenge DocuSign faced, and the resources he saved by going with an MDS:
To build and maintain data pipelines, we would need a lot of people. Each source has its own API and its own quirks. It would take a highly paid engineer anywhere from three to six months to build out a data pipeline and up to 20 hours a week afterwards to keep things running. Our team would have to double in size to be able to do what we’re currently doing with Fivetran.
A modern data stack allows your data team to be more productive by increasing available data without consuming in-house resources. DocuSign again provides a good example. Before implementing an MDS, the business relied on manual ETL processes and could only centralize data from six sources. Laanen says that DocuSign has now added a dozen additional sources, creating new analytics possibilities:
[Our MDS] has opened up quite a few additional use cases and data sources for our teams. Before, people were analyzing from the data within each system or locally by extracting data into Excel.
Or consider digital betting platform Football Index, which freed up developer resources by implementing a modern data stack. After choosing BigQuery as its cloud data warehouse, Football Index decided against building data ingestion scripts internally, because it didn’t want to risk overtaxing in-house resources. Football Index CTO Christophe Popov explains:
I knew building internally could cause issues with data safety and there are so many moving parts that would be impacted if there were any issues or bugs. We implemented Fivetran and it was very straightforward — it just took a few clicks. Fivetran gives us data reliability and saves 10%–20% of development time.
More time and more data mean your team will be able to focus on new analytics projects. Before fitness app Strava implemented a modern data stack, it relied on an attribution partner for all of its customer data. It conducted modeling on local machines using R and Python.
Now, with an MDS, Strava can ingest data from all of its marketing channels in addition to its attribution partner. It runs analysis on that data in Snowflake. This allowed Strava to build an attribution model and gain insight into the entire customer journey. As Strava Data Scientist Michael Li explains:
We can see if our paid users are interacting with our social or SEO channels and determine if there are any cross-effects. Using our metrics, we can determine if SEO is better or worse than our paid acquisition or our partner marketing channels. These things weren't possible when we didn’t have the data in-house.
A modern data stack will radically decrease report generation time and ensure up-to-date reports. Delivery optimization platform Bringg, for example, used a modern data stack to reduce reporting time from five days to a few hours. Data Lead Ashley Rodan describes the new reporting experience:
You write SQL and get great-looking reports, and you can schedule them into Slack and email. It gives us speed, insight and flexibility in querying the data to build beautiful reports with filters and drill-downs.
Media company ALM offers another example of streamlined reporting. After replacing manual ETL and an on-prem database with a modern data stack, it was able to generate monthly reports in two days instead of two weeks.
A modern data stack will improve data reliability and eliminate the burden of ETL maintenance. Automated data pipelines detect and respond to schema and API changes without human intervention, so data teams don’t have to worry about pipeline failures or data gaps.
Wellness brand Ritual recently implemented a modern data stack and replaced its brittle data pipeline with a fully managed, automated solution. While the ETL pipeline failed regularly, the automated solution requires zero maintenance. Ritual’s Director of Data and Analytics Brett Trani explains:
With all the failures and data gaps, people lost trust in the data and would go out and find their own sources — spreadsheets, ad platforms, random notes — and end up with different numbers for the same metric. There was no single source of truth. Fivetran gracefully handles changes in data sources. We have no more data pipeline failures — it just works. Sometimes I don’t log in for days or weeks, because I really just don’t need to touch it.
Intuitive and easy to use, modern BI tools are designed to make data accessible to business users and technical employees alike. The experience of social media marketing platform Falcon.io illustrates the effect this can have on data literacy.
Before implementing a modern data stack, only its relatively small sales operations team routinely consulted analytics. Since adopting a modern data stack and BI tool, Falcon.io has seen active users of analytics dashboards increase by 10x. As Team Manager of Business Intelligence Nicolaas Wagenaar puts it:
We’re definitely seeing usage grow. Mangers are now actively using the pipelines and reports we set up for them.
Additional data sources and an easy-to-use BI tool allow businesses to create a host of new metrics. This can happen in at least two ways:
Richer data enables new kinds of cross-analysis
Greater data access across departments allows more employees to propose new metrics based on their specific competencies
Real-estate site Zoopla is a great example of the first way a modern data stack can increase strategic metrics across an organization. Zoopla used an MDS to reliably replicate all of its raw NetSuite and Salesforce data into a cloud data warehouse. Its data team then used a BI tool to build a continually updated 40-KPI dashboard for the leadership team. Zoopla Head of BI Steven Collings describes his analytics goals this way:
It was always in our mind that we didn't want to build a point solution. We wanted to ensure that all of the data we were landing could be leveraged for other purposes and we wanted to make this data available in a self-service capacity.
Low-code development platform OutSystems is a great example of the second way a modern data stack can increase key metrics. Pre-MDS, OutSystems had 15 top metrics, and struggled to ensure data accuracy. Its MDS delivers richer, more reliable data that anyone can access, so every internal department participates in establishing new metrics. OutSystems now has 50 top metrics instead of 15. Head of Engineering Pedro Martins explains:
Our people can only participate this way because we have the data available. Anything that we do here now has to be quantifiable somehow and it’s a cultural shift that brings a lot of value to the company. The whole company thinks this way.