Supercharging Time to Analysis at Harry’s
Company Size
1,000+
Region
- America
Country
- United States
Product
- Ascend.io
- Looker
Tech Stack
- Data Orchestration
- Business Intelligence
- Data Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Supply Chain Visibility
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Harry’s, launched in 2012, is a direct-to-consumer industry leader with a growing presence in online retail, and brick and mortar markets. The company has a revolutionary Direct to Consumer (DTC) model that is now widely emulated. The growing diversity of product lines, retail outlets, and customers has led to an explosion of new, disparate data feeds that are critical for timely and actionable business insights. The company uses Looker as its business intelligence and analytics tool of choice. The data science team at Harry's undertook an effort to expedite ingesting, transforming, and delivering these data feeds into a robust shared data model that connects all brand information across every retail delivery model.
The Challenge
Harry’s, a leader in the direct-to-consumer industry, faced a challenge with the explosion of new, disparate data feeds due to the diversity of product lines, retail outlets, and customers. The data science team needed to expedite the process of ingesting, transforming, and delivering these data feeds into a robust shared data model that connects all brand information across every retail delivery model. The retail analytics team needed a faster, simpler way to get new analytics up and running, and a platform to ingest and transform these disparate data feeds in a low-code sandbox environment. With a current ecosystem of mostly homegrown and open source solutions that rely on a heavily burdened data engineering team, it could take weeks to get new, critical retail data sources connected to Looker, the company’s business intelligence and analytics tool of choice.
The Solution
To accelerate the access to and bolster the analytics of new and existing data feeds, Harry’s head of analytics, Pooja Modi, and data analyst, William Knighting, spearheaded an initiative to deploy a new data orchestration platform that could reach securely across all their data infrastructure, and also work seamlessly with Looker. They chose Ascend.io for this purpose. Ascend.io offered a faster, simpler way to get new analytics up and running, and a platform to ingest and transform these disparate data feeds in a low-code sandbox environment. The flexibility offered with Ascend.io provides an ease of use that has been critical to accelerating the use of Harry’s data and analytics.
Operational Impact
Quantitative Benefit
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