Overview

With over 16 million users worldwide, Blinkist helps time-strapped readers fit learning into their lives through their ebook subscription service.

Gopi Krishnamurthy, Director of Engineering, leads the team responsible for data engineering, infrastructure, cloud center-of-excellence, growth, and monetization. For Blinkist, having trustworthy and reliable data is foundational to the success of their business.

Supernova Award Category

Data to Decisions

The Problem

As a high-growth company, Blinkist leverages paid performance marketing to fuel customer acquisition. Their 2020 strategy—with an ambitious 40 percent growth target—included a significant investment in channels like Facebook and Google, which would auto-optimize campaigns based on behavioral data shared between the Blinkist app and the channels themselves. 

Of course, like so many companies in 2020, the COVID-19 pandemic changed everything. Now, historic data didn’t reflect the current reality of their audience’s daily lives, and real-time data became essential—not just for determining advertising spend, but for understanding the current state of how users were interacting with the Blinkist app and content across the web.

As C-level execs and campaign managers grew increasingly dependent on real-time insights to drive marketing strategy, budget spend, and ROI, Gopi and his team were struggling with data downtime—issues with data quality, dashboard update delays, and broken pipelines.

Gopi estimates his team was spending 50 percent of their working hours firefighting data drills, trying to resolve data downtime issues while rebuilding trust with the rest of the organization. It wasn’t sustainable – something had to change.

The Solution

In the fall of 2020, Gopi and his team regrouped and refocused. They built a plan modeled on the thoughtful execution framework popularized by Spotify, setting a clear goal to build trust in data at their company. 

“At the core of this framework is data reliability engineering—that we treat data reliability as a first-class citizen, the same way engineering teams in the last decade have started to treat DevOps and site reliability engineering,” said Gopi. 

As they shifted to try to bring in data reliability engineering principles, Monte Carlo, a data observability platform, played a key role for them to easily adopt and meet these three expectations in a short timeframe.

As Gopi and his team worked to rebuild broken trust along with broken pipelines, they partnered with company leaders to build a shared understanding of data reliability principles and set concrete data SLAs (service-level agreements). 

The results

Monte Carlo detects anomalies across the Blinkist's S3, Redshift, and Perscipe data landscape, using machine learning algorithms to generate the thresholds and rules that govern data downtime alerting. This automated monitoring saves Gopi’s team up to 20 hours per engineer each week—and would have been impractical to develop in-house. This leads to cumulative time savings of 120 hours per week for Gopi’s team, energy that can now be spent building their product or otherwise innovating. 

“Especially given the timeframe that we were working with, a data observability platform is not something we could have built,” said Gopi. “This is basically the power of AI that runs behind Monte Carlo—to build this kind of tool, you’d need to have a lot of internal knowledge to build these business rules and create these alerts.”

As Blinkist was able to detect and resolve data downtime more rapidly, their marketing channels thrived, leading to increased revenue. 

“If we were able to identify and resolve issues within 24 hours, Facebook or Google could auto-correct and never scale down campaigns,” Gopi said. 

With more accurate analytics and newly restored trust in their data, Blinkist marketers are now able to make swift decisions to optimize their ad spend for better targeting and performance. 

Metrics

Data observability has helped Blinkist increase revenue, save time, and rebuild trust and transparency in data throughout the organization. With broken data pipelines under control, their data engineers are focusing on innovation and solving core business problems—not firefighting.

Among other benefits of data observability, Monte Carlo has enabled Blinkist to: 

Increase revenue by ensuring that marketing spend was allocated appropriately  by reducing time to resolution of tedious data fire drills and restore trust in data for vital decision making

Save more than 20 hours per week, per engineer by eliminating the need to troubleshoot tedious data fire drills, for cumulative savings of hundreds of hours per week

Drive greater efficiency and collaboration by gaining end-to-end visibility into the health, usage patterns, and relevancy of data assets.

The Technology

Monte Carlo, S3, Redshift, and Periscope. 

Disruptive Factor

As Gopi and his team worked to rebuild broken trust along with broken pipelines, they partnered with company leaders to build a shared understanding of data reliability principles and set concrete data SLAs (service-level agreements). 

Data stakeholders were also granted access to Monte Carlo reporting, increasing transparency about data health across the company. 

“The self-service capabilities of data observability helped build back trust in data, as users were seeing us in action: going from a red alert to a blue “work-in-progress” to “resolved” in green,” said Gopi. “They knew who was accountable, they knew the teams were working on it, and everything became crystal clear.”

With data observability, self-serve reporting and data SLAs ensure that stakeholders work more efficiently. 

For example, when a channel manager notices a campaign is underperforming, they can easily access data reporting and see if data reliability SLAs have been met and data pipelines are working properly. If so, they can eliminate bad data as the culprit and look at other solutions, like changing advertising creatives or adjusting the target audience—without ever requesting time or effort from their colleagues on the data team. 

Shining Moment

“The scale of growth that we’ve seen this year is overwhelming,” Gopi said. “Although the data teams can’t take full credit, I definitely think the things we were able to do—in terms of data observability and bringing transparency into data operations—improved how we target our audience and channels.”