Google Cloud Platform Case Studies Rafter nimbly outpaces its competitors by using flexible, real-time analytics in Looker

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	Rafter nimbly outpaces its competitors by using flexible, real-time analytics in Looker Google Cloud Platform

Rafter nimbly outpaces its competitors by using flexible, real-time analytics in Looker

Google Cloud Platform

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Real Time Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Education Retail
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Inventory Management Predictive Quality Analytics Real-Time Location System (RTLS)
Services	Data Science Services System Integration
Challenge	Rafter, a company that operates a cloud-based platform for colleges and universities to make educational content more affordable, effective, and accessible, was facing challenges in maintaining a competitive position in the market for online book rentals. The company was bottlenecked by a nightly ETL execution that took up to 10 hours and caused slowdowns during the day. Excel and an existing data visualization tool couldn’t handle analytics at scale. In addition, most of the Rafter staff was dependent on data analysts for writing routine queries, causing further delay before their questions could be answered. These limitations hampered critical activities across the business such as finance, inventory management, pricing and campaign management, and operations. Read More
About Customer	Rafter operates a cloud-based platform that helps colleges and universities make educational content more affordable, effective, and accessible. The Rafter platform drives college textbook rentals through Rafter’s own BookRenter.com website, as well as more than 300 white-labeled college online bookstores and popular websites such as half.com, amazon.com, and staples. com. The company also offers an integrated suite of applications for course materials management, which allows professors to discover, adopt, and supply textbooks online; and EasyRent, an in-store textbook rental offering that integrates college bookstore point-of-sale systems with the Rafter platform. The company relies heavily on a modern data warehouse and sophisticated data analytics to stay ahead in a fiercely competitive market. Read More
Solution	Rafter installed Looker as its BI solution to run on top of a Pivotal Greenplum MPP data warehouse. This allowed the company to move from a daily data refresh to near real-time updates used for operational decision-making in a dynamic market. Because the LookML modeling language makes it easy to create reusable queries, everyone in the company has immediate access to the data they need, while analyst resources are freed up for high-value projects such as automating book liquidation and streamlining cumbersome accounting processes. With the LookML modeling language, the data team had the metadata layer they needed to define and maintain consistent metrics, empower end users, and further reduce demand for ad hoc queries. Frontline teams could access near-live datasets with the ability to drill down, create their own metrics on the fly, and pose queries without knowing SQL. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Real Time Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Education

Retail

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Inventory Management

Predictive Quality Analytics

Real-Time Location System (RTLS)

Services

Data Science Services

System Integration

Challenge

Rafter, a company that operates a cloud-based platform for colleges and universities to make educational content more affordable, effective, and accessible, was facing challenges in maintaining a competitive position in the market for online book rentals. The company was bottlenecked by a nightly ETL execution that took up to 10 hours and caused slowdowns during the day. Excel and an existing data visualization tool couldn’t handle analytics at scale. In addition, most of the Rafter staff was dependent on data analysts for writing routine queries, causing further delay before their questions could be answered. These limitations hampered critical activities across the business such as finance, inventory management, pricing and campaign management, and operations.

About Customer

Rafter operates a cloud-based platform that helps colleges and universities make educational content more affordable, effective, and accessible. The Rafter platform drives college textbook rentals through Rafter’s own BookRenter.com website, as well as more than 300 white-labeled college online bookstores and popular websites such as half.com, amazon.com, and staples. com. The company also offers an integrated suite of applications for course materials management, which allows professors to discover, adopt, and supply textbooks online; and EasyRent, an in-store textbook rental offering that integrates college bookstore point-of-sale systems with the Rafter platform. The company relies heavily on a modern data warehouse and sophisticated data analytics to stay ahead in a fiercely competitive market.

Solution

Rafter installed Looker as its BI solution to run on top of a Pivotal Greenplum MPP data warehouse. This allowed the company to move from a daily data refresh to near real-time updates used for operational decision-making in a dynamic market. Because the LookML modeling language makes it easy to create reusable queries, everyone in the company has immediate access to the data they need, while analyst resources are freed up for high-value projects such as automating book liquidation and streamlining cumbersome accounting processes. With the LookML modeling language, the data team had the metadata layer they needed to define and maintain consistent metrics, empower end users, and further reduce demand for ad hoc queries. Frontline teams could access near-live datasets with the ability to drill down, create their own metrics on the fly, and pose queries without knowing SQL.

Impact #1	Queries now run fast. It’s easy to download data, even at high volumes. Tabular views let users drill down with a click.
Impact #2	Data analysts easily make updates to the model in LookML, achieving in one day what can take up to six weeks in other systems.
Impact #3	Built-in version control through GitHub helps ensure that the metadata won’t be corrupted, so multiple analysts can safely work at the same time.

Impact #1

Queries now run fast. It’s easy to download data, even at high volumes. Tabular views let users drill down with a click.

Impact #2

Data analysts easily make updates to the model in LookML, achieving in one day what can take up to six weeks in other systems.

Impact #3

Built-in version control through GitHub helps ensure that the metadata won’t be corrupted, so multiple analysts can safely work at the same time.

Benefit #1	Finance department can close the books—with full GAAP and audit compliance—within three days (down from a full month)
Benefit #2	Significantly improved conversion rates
Benefit #3	Reduced losses caused by discrepancies

Benefit #1

Finance department can close the books—with full GAAP and audit compliance—within three days (down from a full month)

Benefit #2

Significantly improved conversion rates

Benefit #3

Reduced losses caused by discrepancies

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Overview

Rafter nimbly outpaces its competitors by using flexible, real-time analytics in Looker

Operational Impact

Quantitative Benefit