Azure Synapse Analytics is a limitless analytics service that is designed to bring the two worlds of big data and data warehousing into a unified, enterprise-grade, powerful platform. In this blog post, we look at four real-world use cases where global organizations have used Azure Synapse Analytics to innovate and drive business value through data. For a more detailed and in-depth coverage of how data analytics can help your business, see our e-book Analytics Lessons Learned: How Four Companies Drove Business Agility with Analytics and sign up for Azure to start exploring your data with Azure Synapse.
Why Azure Synapse?
Azure Synapse provides a complete, out-of-the-box solution designed to accelerate time-to-insight and empower business agility. Azure Synapse is the only end-to-end platform that unifies data ingestion, big data analytics, and data warehousing. It offers turnkey setup and configuration options on fully managed infrastructure to help you get results fast. It offers greater control and flexibility in terms of pricing by enabling you to choose the best pricing option for each workload with both serverless and dedicated options.
Use case one: Just-in-time inventory
Aggreko is a global leader in the supply of temporary power generation, temperature control systems, and energy services, providing backup energy and power supply whenever and wherever their customers need it. Aggreko uses Azure Synapse to increase operational efficiency with the just-in-time supply of their specialist equipment.
Aggreko’s data ingestion pipeline was set up to run every eight hours because it took four hours to run the ingestion (batch) jobs. Moreover, the data warehouse had to be rebuilt every day due to storage limitations. This meant that there was a lag of 8-24 hours between when the data arrived and when it was available for data analytics pipelines:
By adopting Azure Synapse, Aggreko was able to significantly improve its time-to-insight by reducing ingestion complexities and improving speed. Ingestion time was reduced from four hours to less than five minutes. This in turn meant that for Aggreko, data is now available for analytics pipelines in near real-time (less than five minutes’ lag). The team also estimated that they have saved 30-40 percent of their time—this time was spent solving technology problems in their legacy systems. By adopting Azure Synapse, data is now available for instant exploration, which means that the Aggreko team has more time to focus on solving business problems.
"Azure Synapse gives us a single environment to explore and query the data without moving it. So at a spectrum of the volume of data, we can achieve exponentially faster insights, by querying directly over the lake before outputting insight to Power BI." —Elizabeth Hollinger, Director of Data Insights at Aggreko
As mentioned before, this use case is based on a real-world scenario where Aggreko adopted Azure Synapse as their analytics platform.
Use case two: Fraud detection
Clearsale, a leading fraud detection company based in Brazil, used Azure Synapse to modernize their operational analytics data platform. Clearsale helps customers verify an average of half a million transactions daily using big data analytics to detect fraud across the world. Clearsale’s dataset doubles in size every two years, and the company needs to provide fraud detection services within seconds. This requires a great level of scalability and performance:
Using Azure Synapse, Clearsale has significantly reduced the time it takes to train new models to improve their fraud detection capability. Using their previous on-premises platform, it used to take close to a week to ingest, prepare, and train the machine learning models. Using Azure Synapse, this has now been slashed to under six hours. This is a massive improvement that has enhanced their capability, improved efficiency, and reduced operational overhead.
Use case three: Predictive maintenance
GE Aviation's Digital Group is a world leader in manufacturing airplane engines and developing aviation software. On top of manufacturing, GE also provides advanced data analytics to many airlines around the world. For each flight, GE ingests the time series data for the entire flight, which includes as many as 350,000 data points. Understandably, running data analytics on such volumes of data can be challenging. To solve this, the team adopted Azure Synapse:
Using Azure Synapse made it significantly easier and quicker for GE to build complex predictive machine learning models. Building something similar using their previous system would have required many complex steps, across multiple systems and environments. For GE, the native integration between Microsoft Power BI and Azure Synapse proved to be extremely useful. They can now explore data quickly and when an anomaly is found in the Power BI reports, analysts are able to do drill-down analysis to see why the spikes occurred and what corrective maintenance is needed.
Use case four: Marketing analytics (customer 360⁰ view)
Imagine a large multinational retail company that has stores in Australia, New Zealand, and Japan. The company sells consumer goods, electronics, and personal care items through its brick and mortar stores as well as through digital online channels. The company wants to leverage data analytics to build an end-to-end view of their customers. The goal is to improve customer experience and increase profit. To achieve this, the data team found Azure Synapse to be the best platform to achieve this:
Azure Synapse enabled the data team to unite their data, developers, and business users in ways that were not possible before. Azure Synapse has simplified ingestion and data processing and made it easy for the organization to have a central data store that holds all operational and historical data that can be refreshed in near real-time. Azure Synapse has also simplified data exploration and discovery without the need to transform data from one format to another or move the data to other systems. This has enabled the data team to experiment, map, and correlate different datasets to produce curated (gold) data that is ready for consumption.
Source: microsoft.com
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