Shutterfly
A data warehouse reboot for a leader in printing
Shutterfly’s portfolio of brands, millions of customers, and unreliable analytics made it difficult for its team to see the wood for the trees.
DEPT®/DATA helped Shutterfly to update and modernise its data infrastructure, allowing its data team to make smarter and more proactive business decisions.
Multiple brands sharing a dated system
Shutterfly is one of the USA’s leading photography product shops, supplying consumers with canvases, photo books, business cards, and other printed products.
Within the Shutterfly parent company also lives brands like Borrow Lenses, Lifetouch, and Shutterfly Business Solutions. Collectively, they have millions of customers, many purchasing across the Shutterfly brand portfolio.
Shutterfly found itself with an aging and unreliable analytics infrastructure that was no longer serving its business. The outdated system slowed product rollouts, created challenges for marketing, and was expensive to maintain. They needed a modern, automated system to reduce manual labour and improve business intelligence across its brands.
A simplified data platform
DEPT®/DATA worked alongside the data team at Shutterfly to migrate their cloud data warehouse. This enabled us to refactor and rationalise existing data pipelines into a new, integrated data platform.
By connecting multiple data sources, the marketing team could see one master customer list with tracked activities across all brands. This allowed teams to understand shared users, create more relevant offers, and improve marketing efficiencies.
Perhaps most importantly, Shutterfly was able to reorganise and revitalise its data engineering team. With streamlined data systems, the team was positioned to respond more quickly to changing business and market needs.
Improved trust in data
The new and improved cloud data warehouse platform allowed Shutterfly to consolidate its analytics infrastructure. Consistent reporting and quality measures improved trust in the data warehouse, and the newly formed data engineering team could better support their business counterparts.
Overall, the new system decreased operational costs while increasing marketing and product ROI.
90%
Reduction in infrastructure cost
4X
Fewer data quality issues
20X
Less business-critical duplication
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Director, Data & AI
