Monday, April 15, 2024
No menu items!
HomeData Integration5 Real-world Examples of Companies Using Striim for Real-Time Data Analytics

5 Real-world Examples of Companies Using Striim for Real-Time Data Analytics

According to a recent study by KX, US businesses could see a total revenue uplift of $2.6 trillion through investment in real-time data analytics. From telecommunication to retail, businesses are harnessing the power of data analytics to optimize operations and drive growth. 

Striim is a data integration platform that connects data from different applications and services to deliver real-time data analytics. These five companies successfully harnessed data analytics through Striim and serve as excellent examples of the practical applications of this valuable tool across industries and use cases.

1. Ciena: Enabling Fast Real-time Insights to Telecommunication Network Changes 

Ciena is an American telecommunications networking equipment and software services supplier. It provides networking solutions to support the world’s largest telecommunications service providers, submarine network operators, data and cloud operators, and large enterprises. 

Use cases

Ciena’s data team wanted to build a modern, self-serve data and analytics ecosystem that:

Improves the customer experience by enabling real-time insights and intelligent automation to network changes as they occur.
Facilitates data access across the enterprise by removing silos and empowering every team to make data-driven decisions quickly.

To meet its goals, Ciena chose Snowflake as its data warehousing platform for operational reporting and analytics and Striim as its data integration and streaming solution to replicate changes from its Oracle database to Snowflake. The company used Striim to collect, filter, aggregate, and update (in real time) 40-90 million business events to Snowflake daily across systems that manage manufacturing, sales, and dozens of other crucial business functions to enable advanced real-time analytics.

 With its real-time analytics platform, Ciena has offered customers up-to-date insights as changes occurred in its network, thus improving the customer experience. Additionally, operators can begin experimenting with machine learning by using real-time analytics to identify network events that could impact performance.

Finally, with its self-serve analytics platform, everyone in the organization can now access the data they need to make faster data-driven decisions. With real-time analytics, Ciena’s customers no longer have to wait to see their updated data because it is displayed instantly after any changes are made in the source platforms.

“Because of Striim, we have so much customer and operational data at our fingertips. We can build all kinds of solutions without worrying about how we’ll provide them with timely data,” Rajesh Raju, director of data engineering at Ciena, explains.

2. Macy’s: Improving Digital and Mobile Shopping Experiences 

Macy’s, Inc. is one of America’s largest retailers, delivering quality fashion to customers in more than 100 international destinations through the leading e-commerce site macys.com. Macy’s, Inc. sells a wide range of products, including men’s, women’s, and children’s clothes and accessories, cosmetics, home furnishings, and more. 

Use cases

Macy’s real-time analytics use cases were to:

Achieve real-time visibility into customer and inventory orders to maximize operational cost, especially during the peak holiday events like Black Friday and Cyber Monday
Leverage artificial intelligence and machine learning to personalize customer shopping experiences.
Quickly turn data into actionable insights that help Macy’s deliver quality digital customer experiences and improve operational efficiencies.

Macy’s migrated its on-premise inventory and order data to Google Cloud storage to reach its objectives. The company decided to move to the cloud based on the benefits of cost efficiency, flexibility, and improved data management. To facilitate the data integration process, it used Striim, which allowed it to:

Import historical and real-time on-premise data from its Oracle and DB2 mainframe databases.
Process the data in flight, including detecting and transforming mismatched timestamp fields.
Continuously deliver data to its Big Query data warehouse for scalable analysis of petabytes of information.

Real-time data analytics has been a critical factor in Macy’s ability to understand customer behaviors and improve the shopping experience for its customers. Data analytics has enabled the company to increase customer purchases and loyalty and optimize its operations to minimize costs. As a result, Macy’s has been able to offer its customers a seamless and personalized shopping experience.

3. Inspyrus: Facilitating Real-time Customer Invoice Reporting

Inspyrus (acquired by MineralTree) is a fintech SaaS company specializing in automating the accounts payable (AP) process of invoice capture, invoice approval, payment authorization, and payment completion. To do this, the company connects with hundreds of different ERP and accounting systems companies and streamlines the entire AP process into a unified system.

Use cases

Inspyrus wanted to build a real-time data analytics system to:

Provide customers with a real-time view of all their invoicing reports as they occur. 
Help customers visualize their data using a business intelligence tool.

Inspyrus used Striim to seamlessly integrate customer data from various ERP and accounting systems into its Snowflake cloud data warehouse. Striim’s data integration connector enabled the company to generate real-time operational data from Snowflake and use it to power the business intelligence reports it provides to customers through Looker.

Inspyrus’s updated data stack, consisting of Striim, Snowflake, dbt, and Looker, has enhanced the invoicing operations of its customers through rich, value-added reports. 

According to Prashant Soral, CTO at Inspyrus, the real-time data integration provided by Striim from operational systems to Snowflake has been particularly beneficial in generating detailed, live reports for its customers.

4. HomeServe: Accelerating Business Intelligence and Machine Learning Initiatives 

HomeServe is a home repair and improvement company that provides emergency repair, maintenance, and installation services for customers in the UK, the US, and other countries. HomeServe’s innovative water leak solution, LeakBot device, detects hidden leaks before they cause significant damage. The device continuously monitors the property’s plumbing system and issues real-time alerts to enable timely repair service.

Use cases

HomeServe sought to utilize real-time data analytics to:

Gain better operational intelligence and provide its insurer partners detailed reports on leaks detected and repaired, along with cost savings.
Use granular streaming data to analyze how the LeakBot device performs in the field, enabling the company to optimize the service to homeowners and insurance partners.

HomeServe used Striim’s streaming data integration platform to deliver rich operational data to its Google BigQuery analytics environment. Striim captures every insert, update, and delete operation in the LeakBot system and streams it to BigQuery.

Using Striim to transfer operational data to BigQuery continuously, HomeServe conducts a post-operational analysis and gains insights into individual leaks. HomeServe then uses this data to create reports demonstrating the value of HomeServe to insurance partners in terms of performance and cost savings. Insurance companies also use these reports to accurately assess the escape of water risk and pricing at renewal.

HomeServe’s data scientists also leverage the Google BigQuery environment to analyze device performance and continuously optimize the machine learning model used in the LeakBot solution. This helps ensure that the algorithm used by the device is as accurate as possible when operating in real-life situations.

5. Blume: Optimizing Real-time Supply-chain Visibility

Blume Global is a technology company that provides supply chain and logistics solutions to businesses worldwide. The company offers a range of software and services that help businesses manage the movement of goods, track shipments, and optimize their supply chain operations. 

Use cases

Blume aimed to leverage real-time data analytics to: 

Create an open, centralized platform that provides accurate, up-to-date information to all trading partners. 
Optimize assets, achieve end-to-end traceability, and increase visibility across the supply chain.

To achieve its data analytics goals and increase the resilience and scalability of CarrierGo (one of Blume’s key SaaS applications), the company decided to move data from its Oracle database to the cloud. Striim Migration Service for Google Cloud enabled Blume to set up continuous unidirectional streaming from its on-premise Oracle Database to a MySQL target running on a Google Compute Engine VM.

After migrating to Google Cloud, Blume now easily provisions a new MySQL environment and scales up the existing platform by adding more virtual resources to its analytics services to deliver seamless visibility of global freight movements.

Transform How Your Company Operates Using Real-time Analytics With Striim

Real-time analytics transforms how your business operates by providing accurate, up-to-date information that can help you make better decisions and optimize your operations. 

Striim offers an enterprise-grade platform that allows you to easily build continuous, streaming data pipelines to support real-time cloud integration, log correlation, edge processing, and analytics. Request a demo today.

Read MoreStriim

RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Most Popular

Recent Comments