Overview

Engagement overview

Lakeshore Learning Materials, a leading innovator in educational materials, sought to harness data for more informed decision-making across its business units. The organization needed a framework to gather and analyze data from multiple sources and distill it into reports suited to diverse stakeholder needs.

TO THE NEW worked with Lakeshore to build this reporting framework and, as the engagement grew, extended into data engineering, building a centralized, automated data platform on Amazon Redshift that unifies data from Lakeshore’s enterprise, CRM, and marketing systems.

Our client

E-commerce
 USA

Lakeshore Learning Materials is one of the most innovative manufacturers of educational materials in the country. Specializing in top-quality products for the home and classroom, Lakeshore Learning Materials is a thriving multi-channel organization with a robust web business, multiple catalogs for early learning and elementary programs, 60 store locations, plus a sales force that is second to none.

Lakeshore Learning

Business objective

The engagement focused on equipping Lakeshore with data-driven reporting and a centralized data platform to support informed decision-making.

01

Consolidate data from multiple source databases, including Redshift and SQL Server, for unified analysis

02

Generate reports and presentations using Qlikview and Qliksense to summarize data analysis results

03

Migrate the primary data source from SQL Server to Amazon Redshift

04

Centralize data from MySQL, Amazon RDS Aurora, SAP, Salesforce, and marketing automation systems

05

Enable high-performance batch ingestion for scalable and reliable data processing

06

Support structured and semi-structured data processing for timely, accurate reporting

07

Strengthen security, governance, and compliance controls across all environments

Business solution

TO THE NEW worked with Lakeshore to deliver a tailored BI solution alongside a modern, automated data platform on Amazon Redshift.

  • Gathered business requirements and raw data table details from the existing Data Warehouse

  • Collaborated with business analysts, data engineers, and domain experts to define data and reporting needs

  • Built dashboards to support stakeholders across different business teams

  • Supported post-deployment scenarios and ad-hoc data analysis requests as they arose

  • Mapped SQL Server columns to Redshift tables by analyzing existing SSIS packages and dashboards

  • Built data ingestion pipelines in Matillion for SQL, SAP, SAP CRM, Pardot, and SFMC sources

  • Developed automated replication jobs to load data into the Amazon Redshift raw layer

  • Implemented ETL pipelines to transform data into Core, Presentation, and Semantic layers

  • Configured parallel SAP and non-SAP replication with daily workflow orchestration using GitHub Actions

  • Automated downstream BI loads along with semantic and presentation layer execution

Business impact

The initiative improved reporting speed, data reliability, and stakeholder access to actionable insights across Lakeshore’s business units.

50% faster ETL execution through automated data pipelines

95% shorter Qlik dashboard runtimes for faster access to reporting insights

85% reduced dependency on legacy systems across the data platform

Automated data workflows reduced manual intervention across ingestion and ETL

Multiple dashboards and models developed to uncover sales trend insights

Improved data consistency achieved across Core, Presentation, and Semantic layers