How SSENSE modernized their analytics platform with Amazon QuickSight

This post was written with Ranjan Vaidya and Brian Gibson from SSENSE.

SSENSE (pronounced es-uhns) is a global technology platform operating at the intersection of culture, community, and commerce. Headquartered in Montreal, Canada, the company offers a curated selection of luxury and emerging brands across womenswear, menswear, kidswear, and Everything Else™. With approximately 100 million monthly page views and a predominantly young audience between 18 to 40 years old, SSENSE has established itself as both an ecommerce leader and a producer of cultural content.

In this post, we share how SSENSE successfully migrated from a legacy business intelligence platform to Amazon QuickSight, enabling self-service analytics across the organization while reducing costs and improving operational efficiency.

The challenge

At SSENSE, our previous analytics solution presented several significant obstacles. We faced restrictive licensing terms that limited our ability to scale, along with integration challenges with our AWS environment. The platform required maintaining additional server infrastructure, which added complexity and created ongoing maintenance challenges. This server infrastructure proved to be a complex technical layer between online and on-premises layers that were prone to connection issues, was difficult and costly to scale, and was limited in functionality.

Scheduled updates from the solution provider always resulted in environment issues, which degraded trust in the solution. Furthermore, we noticed a decline in innovation and feature development from our previous vendor, which hindered our ability to advance our analytics capabilities.

Why Amazon QuickSight

After evaluating various solutions, we chose QuickSight for three primary reasons. First, its flexible licensing and cost structure aligned perfectly with our scaling needs. Second, because AWS is our main ecosystem, the seamless integration of QuickSight with other AWS services was particularly appealing. Finally, the rapid pace of innovation and feature development in QuickSight, especially Amazon Q in QuickSight, demonstrated the commitment of Amazon to advancing the capabilities.

Implementation and migration

We undertook an ambitious migration project, successfully transitioning almost 600 dashboards to QuickSight within 8 months. This significant undertaking was driven by an upcoming contract renewal deadline, requiring our team to not only complete the migration but also ensure uninterrupted reporting, maintain data integrity, and re-engineer each dashboard for optimal performance in the new environment.

The migration process involved carefully evaluating existing workbooks, recreating complex data visualizations and calculated fields in QuickSight, mapping and centralizing all data sources, reformulating user roles and permissions, and conducting thorough testing and user acceptance sessions. Additionally, we provided ongoing user training, documented new workflows, and collaborated closely with business stakeholders to ensure every critical metric and KPI was accurately represented. This methodical approach allowed us to deliver a seamless transition while minimizing downtime and maintaining the reliability of our business intelligence reporting.

Beyond the technical migration of both batch and near real-time reporting solutions powered by Amazon Athena and Amazon Redshift, we also managed extensive change management challenges involving people and processes. Despite these pressures, our team successfully migrated almost 600 reports to QuickSight while simultaneously training the team on the new platform.

Our data architecture, shown in the following diagram, consolidates information from various organizational systems into Athena and Amazon Redshift, creating a centralized repository for company-wide analytics. For secure access management, we implemented single sign-on through Okta, using an AWS-provided AWS Lambda-based synchronization solution, with plans to transition to AWS IAM Identity Center in the future.

Results and benefits

The migration to QuickSight has delivered substantial benefits across multiple dimensions. Dashboard development time has decreased by 25%, while maintenance tasks have been reduced by approximately 80%. Perhaps most significantly, the time required for new Athena data availability has improved by 95%, eliminating time-consuming manual processes.

Cost optimization

We’ve achieved remarkable cost savings, reducing our overall analytics expenses to approximately one-third of previous levels. These savings come from multiple sources, including lower licensing fees, eliminated server infrastructure costs, reduced support requirements, and decreased operational overhead. We provide near real-time monitoring of the warehouse order fulfillment process through picking, packing, and shipping, as shown in the following Operations Fulfillment Monitoring Dashboard.

Technical improvements

The seamless integration of QuickSight with Amazon Redshift has significantly enhanced our reporting capabilities. We moved from batch processing, which traditionally takes hours to complete, to achieving near real-time reporting with refreshes every 15 minutes. This direct Amazon Redshift integration enables true real-time use cases that were previously unattainable.

Beyond basic reporting, we’ve strategically implemented the built-in AI features offered by QuickSight, particularly focusing on the Topics functionality, which business users can use to independently investigate anomalies and issues without analyst intervention. While our self-serve capabilities have historically targeted analyst-level users, the implementation of Amazon Q in QuickSight represents a deliberate expansion of self-service analytics to frontline business users. This approach increases operational efficiency and productivity across business areas and strategically frees our analysts to focus on more complex, high-value analytical tasks.

User adoption

Today, approximately 530 users across SSENSE actively use QuickSight, comprising 8 administrators, 150 authors, and 372 readers. The platform’s intuitive interface has enabled business teams to self-serve their analytics needs, providing users with the ability to create and manage their own custom dashboards for faster insights. This self-service capability has empowered teams across the organization to take ownership of their data visualization needs.

Looking ahead

We’re continuing to expand our use of Amazon QuickSight, with a focus on increasing near real-time operational reporting capabilities. We’re also exploring advanced features such as Q Data Stories and Scenarios to further enhance our analytics capabilities.

Conclusion

The migration to Amazon QuickSight has transformed how SSENSE approaches business intelligence and analytics. Despite the aggressive timeline and the learning curve for our team, we’ve successfully implemented a modern, cost-effective analytics platform that better serves our organization’s needs. The combination of reduced costs, improved efficiency, and enhanced capabilities has validated our decision to choose QuickSight as our analytics solution.

Through this implementation, we’ve demonstrated that with proper planning and execution, organizations can successfully modernize their analytics infrastructure while maintaining business continuity and improving overall capabilities. As we continue to explore new features and capabilities within QuickSight, we’re confident in our ability to meet our evolving analytics needs and drive data-driven decision-making across SSENSE.

About the Authors

Ranjan Vaidya is a Director of Data & AI/ML at SSENSE, leading the organization’s journey toward data analytics and AI maturity. With deep expertise in cloud technologies, data architecture, and machine learning, he has architected and implemented a scalable distributed data mesh and self-serve AI/ML Hub grounded in MLOps best practices. He brings cross-industry experience spanning retail, ecommerce, healthcare, education, and agriculture.

Brian Gibson is a Sr. Manager in Data Platform for SSENSE, leading the data analyst team. He started his career as an ERP consultant and worked on numerous implementations nationally and internationally, across multiple industries, before shifting focus to wireless and web-enablement R&D. He later worked at a boutique web design firm leading the technology and project areas with a focus on cutting-edge video integration.

Bruce Tran is a Senior Technical Account Manager at AWS, where he collaborates with independent software vendors. With over 20 years of experience, Bruce specializes in high-availability and disaster recovery architectures. Previously, he spent 15 years in leadership roles, where he developed OSS and network solutions for major North American carriers. He excels at aligning technical solutions with business requirements.

Jonathan Experton is a Senior Solutions Architect at AWS working with independent software vendors. He has more than fifteen years of practical experience designing and developing technology solutions, including software and platform engineering. He specializes in helping companies implement generative AI technologies and developer productivity tools to drive business outcomes.

Daniel Rios is an AWS Data and Generative AI/ML Sales Specialist guiding organizations in leveraging purpose-built databases, analytics, and generative AI solutions to unlock new value streams and deliver measurable business outcomes. As a trusted advisor, he empowers customers to solve critical business challenges and enhance their service delivery using AWS Data and AI/ML services.

Sarath Byreddy is an AWS Data and Generative AI/ML Senior Solutions Architect. He has more than 15 years of experience with large scale data migrations, designing enterprise data warehouses, implementing data governance programs, and delivering scalable analytics solutions to customers.