Dickey's Barbecue Pit Gains Operational Insight across 500 Stores with Advanced Big Data Analytics in the Cloud
Business intelligence (BI) and data warehouse solutions provider iOLAP Inc. has combined Syncsort's DMX data integration software with Yellowfin's BI platform and Amazon Redshift for data warehousing to provide one of America's most successful food chains, Dickey's Barbecue Pit, with deep operational insight across its 500 U.S. restaurants.
The advanced analytics solution, Smoke Stack, uses the latest Big Data technology and is hosted on the Amazon Web Services (AWS) cloud-platform for flexibility and to minimize ongoing resource requirements. It was developed in response to the phenomenal growth of Dickey's business and inability of its previous store-by-store reporting tool to combine data from all restaurants and source systems to provide critical company-wide insights.
"Dickey's needed an easy and cost-effective way to integrate, analyze and act on valuable business data by creating advanced visualizations and performing data analytics with minimal on-site infrastructure and support personnel," said Laura Rea Dickey, CIO for Dickey's Barbecue Restaurants. "By leveraging the scalability and reliability of AWS, the power and speed of Syncsort's DMX data integration platform, and the intuitive data discovery in Yellowfin, the platform gives us a rich, immersive system to access, query and analyze our rapidly growing and changing business data. We can now quickly and easily understand and act on vital sales, labor, inventory costs and customer satisfaction KPIs throughout our business."
The hosted cloud application, delivered by iOLAP, includes:
Cloud-based Data Integration: Restaurant data is published to Amazon's Simple Storage Service, Amazon S3, and then parallel loaded into Amazon Redshift leveraging the lightweight, easy-to-use interface of Syncsort's data integration solution, DMX. The speed of DMX allows Dickey's to rapidly collect, transform and load thousands of very large files, with diverse data types from multiple servers across all of Dickey's locations, without performance bottlenecks.
Cloud-ready Business Intelligence: Running on Linux server-based Amazon EC2 instances, Yellowfin's 100% Web-based, cloud-ready and highly scalable BI platform makes it easy and efficient for Dickey's to perform a new level of analytics. Non-technical business users can independently leverage the easy-to-use reporting and charting features to create stunning visualizations. Dickey's also uses the advanced analytics functions to measure their performance against key sales, labor, inventory costs and customer satisfaction KPIs.
A Cloud-based Enterprise Data Warehouse: Amazon Redshift was a natural choice for cloud-based Enterprise Data Warehousing, allowing developers to quickly react to performance constraints using the many tools available within the Amazon Web Services platform. Performance expectations have been exceeded and Dickey's can cost-effectively adjust their storage needs as their data volumes grow.
Cloud Model Pricing: Smoke Stack takes advantage of low-cost AWS data storage and processing, as well as Syncsort and Yellowfin's cloud pricing models, to deliver the entire solution cost-effectively. The solution provides enterprise-class features without the enterprise price tag.
"Smoke Stack has made data-driven business insights quick and easy to attain, allowing us to focus our efforts on using our data instead of manually analyzing it," continued Laura Rea Dickey. "This significant improvement to our reporting and analytics environment translates into more dollars saved and earned. In addition, the ease-of-use has allowed us to seamlessly integrate the data technology into our everyday operations, introducing it to non-technical business users, all without painfully changing business processes. This flexibility has been key to user adoption and given us valuable insights. Smoke Stack has bridged the gap for us from data that is merely accessible, to data that is valuable, timely, manageable and actionable."
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