Qin Yu, Director of Engineering at Xignite.com, was faced with the pressures of delivering a technology architecture that could handle the massive volume of financial market data with sub-millisecond latencies. A thorough understanding of databases and their best use cases was required to curate the best technology solution for Xignite. Open Source Redis was an obvious choice for any data that required fast access, as well as complex computations via in-memory analytics. Qin chose Redis for its versatile data structures, commands and Lua scripting support, which made it the simplest, fastest way to crunch the data delivered by Xignite. But outages had the potential to wreak havoc and could cost the company dearly. As Xignite’s client base grew, it was more important than ever to reduce downtime. Qin had to find a way to reliably scale the business’ usage of Redis while mitigating the risk of downtime or data loss.

Andrew Spencer, Stance’s Director of Technology, was tasked with delivering a retail website that was engaging, refreshing, and massively responsive. The site had to handle huge bursts of traffic during celebrity events and endorsements while maintaining its responsiveness. These performance directives needed to be achieved with a very small team and limited resources. For instance, when Rihanna, one of Stance’s creative directors, tweeted about her limited edition product to her 60+ million followers, the site had to withstand huge bursts of traffic and maintain an extremely fast checkout process for the limited edition products.

HealthStream needed a high-performance datastore to enhance user responsiveness with low operational overhead. The company required a system that was blazing fast, highly available, and reliable. HealthStream's workforce development platform, used by approximately 4.5 million healthcare professionals in the U.S., demanded low latency and high performance caching. Additionally, operational simplicity, high availability, and high reliability were critical requirements for their applications.

Scopely, a rapidly growing mobile gaming company, faced several challenges in maintaining stable, top performance for their games. Initially, they used Amazon’s Elasticache for Redis management but found it required unrealistic levels of self-monitoring. High availability and sub-millisecond response times were crucial for their business, as any performance issues could lead to users switching to other applications. Additionally, Scopely needed to consolidate efforts, focusing on technology innovation while outsourcing non-core services to save time and money. With a modest-sized operations team, they did not want to burden their developers with managing Redis. Lastly, scalability was a significant concern. Scopely's games could rapidly grow from thousands to hundreds of thousands of users within 24 hours, necessitating efficient and quick scaling of their Redis databases.

Cirrus Insight was experiencing rapid growth, becoming the #1 app on Salesforce and ranking #41 on the Inc. 5,000 list of fastest-growing companies in the United States. With plans to expand both domestically and internationally, they faced the challenge of scaling their database to handle an increasing number of users without sacrificing performance. They evaluated Redis Enterprise for its ability to provide seamless scaling, 24×7 support for mission-critical Redis layers, and stable, high performance.

Drivy is a small but quickly-growing business. Drivy plans on expanding to other European countries, starting with the U.K. Drivy has had over 1.4 million Rental days logged and nearly 1 million users ever since the platform was created on 2010. With this uptick in growth and a relatively small operational team, Drivy needed to scale its mobile and web app seamlessly without sacrificing performance. Here are a few other challenges that led Drivy to evaluate and ultimately select Redis Enterprise: Values Redis Enterprise’s ability to provide: High availability persistence, auto-failover, cross-zone / multi-region / multi-datacenter in-memory replication Seamless scaling & clustering Stable, high performance

The business challenges that led the profiled company to evaluate and ultimately select Redis Enterprise included the need to address growing application usage and user count, as well as the desire to scale to multiple locations/sites. BrikL faced no direct comparison challenges before choosing Redis Enterprise, indicating that their primary focus was on finding a solution that could meet their specific needs for high performance and scalability. The company needed a robust caching solution to support their eCommerce platform, which is critical for improving speed-to-market and increasing sales for their apparel company clients.

PubPlus faced significant challenges with high latency and slow response times from other databases. As their application usage and user count grew, they needed a solution that could handle real-time analytics, job and queue management, and time-series data efficiently. The company sought a database solution that could offer high availability, stability, and high performance while seamlessly scaling to meet their growing needs.

The business challenges that led the profiled company to evaluate and ultimately select Redis Enterprise included difficulty operating, scaling, and administering other databases. Swello, a small French marketing platform, faced significant hurdles in managing their existing database infrastructure. The complexity and inefficiency of their previous database solutions hindered their ability to scale and maintain high performance, which was crucial for their social media engagement services. As their application usage and user count grew, these challenges became more pronounced, necessitating a more robust and scalable solution.

The business challenges that led the profiled company to evaluate and ultimately select Redis Enterprise included data-loss and downtime with other databases, a lack of security control with other databases, and pricing issues. Tarife.at needed a reliable and high-performance database solution to support its e-commerce platform, which serves 350,000 visitors per month. The company faced significant challenges with data integrity and availability, which were critical for maintaining customer trust and operational efficiency.

Zego faced significant challenges with high latency and slow response times from other databases. Additionally, they experienced difficulty in operating, scaling, and administering these databases. As their application usage and user count grew, they needed a more efficient solution to ensure faster service delivery and better performance.

The business challenges that led the profiled company to evaluate and ultimately select Redis Enterprise included difficulty operating, scaling, and administering other databases. Seller Snap needed a solution that could handle the growing application usage and user count without compromising on performance or stability.

Betserv faced significant challenges with their existing database, primarily high latency and slow response times. These issues were compounded by difficulties in operating, scaling, and administering the database, which hindered their ability to provide a seamless experience for their clients. The latency problems were particularly problematic for their eCommerce platform, where quick response times are crucial for maintaining customer satisfaction and operational efficiency. As a result, Betserv began evaluating alternative solutions that could offer better performance and reliability without requiring additional specialized personnel.

Zefo faced significant challenges with data-loss and downtime using other databases. Additionally, they experienced difficulties in operating, scaling, and administering these databases. The company needed a small cluster solution to handle their growing user base and ensure a stable, high-performance experience for their customers.

Jelly Button Games faced significant challenges with their viral hit game, Pirate Kings, which saw a massive increase in daily active users from 1,000 to 3.5 million in less than a month. This surge translated to 1 million requests per minute, overwhelming the operations staff. The game's real-time interactions required fast read and write capabilities to maintain an engaging and interactive experience. Additionally, Jelly Button needed to address high availability, seamless scaling, monitoring and management, and 24×7 support for their mission-critical Redis layer.

Vodafone faced the challenge of managing a complex operating environment with diverse levels of organizational maturity and processes that needed to be aligned across its global operations. The company needed a flexible, scalable supply chain solution to accommodate a growing network of global trading partners. Vodafone aimed to leverage economies of scale and scope across multiple operating units by obtaining visibility and control across its distributed trading network. The company also sought to resolve issues around data and process standardization to capture benefits associated with price transparency and strategic sourcing.

Unilever faced significant challenges in managing its supply chain due to increasing volatility in the market. The company identified five key global trends impacting its operations: multiple channels, sustainability, economic volatility, customer intimacy, and digital savviness. To address these challenges, Unilever needed a more agile supply chain that could handle the growing volatility without resorting to expensive inventory increases. The company aimed to improve its short-term forecast accuracy and reduce working capital tied up in inventory.

Procter & Gamble (P&G) faced significant challenges in accurately forecasting short-term demand for their consumer products. Their existing 24-month forecast provided a good overview for monthly or weekly production, but it was insufficient for the immediate needs of supply chain planning and manufacturing teams. These teams required a short-term forecast to plan production effectively and avoid 'fire-fighting' practices. P&G needed a solution that could provide accurate short-term demand forecasts to ensure agility and flexibility in manufacturing, especially for products with very short production and order lead times. The company explored various solutions but found that most big software companies lacked the agility to meet their specific demand sensing needs. Terra Technology's Real-Time Forecasting, later known as Demand Sensing (DS), emerged as a promising solution due to its specialized focus on consumer packaged goods (CPG) demand planning and forecasting.

As Avon expanded globally, the company faced significant challenges in managing its supply chain due to a lack of centralized information across its global locations. This was exacerbated by the use of varied MRP and ERP systems in different regions, leading to inefficiencies in inventory and distribution management. The manual, ad-hoc planning processes were not supported by enterprise systems, resulting in varying service levels and a surplus of stock. Avon needed better engagement across multiple geographies and disparate platforms to achieve real-time visibility into supplier production availability and move towards collaborative planning and execution.

Seagate needed to create a more responsive supply chain to support business practices such as vendor-managed inventory, JIT hub replenishment, and build-to-demand manufacturing. The company faced challenges due to short product life cycles and highly volatile demand, which required fast time-to-market and fast time-to-volume strategies. The combination of these factors drove the need to integrate business processes across divisions, geographies, and trading partners. Seagate aimed to move from a build-to-forecast model to a true build-to-demand model, requiring better synchronization with trading partners and a robust system providing visibility to the supply chain process for all suppliers and customers.

In 2005, Lenovo acquired IBM’s Personal Computing Division, which led to significant growth and revenue potential. However, the integration posed several operational challenges. Lenovo had to operate its global supply chain on legacy systems, which were expensive and lacked flexibility. The company needed to design and build a worldwide IT platform to transition from these legacy systems quickly and with minimal business disruption. The new platform required improved visibility, efficiency, and responsiveness to manage its growing trading network. Additionally, Lenovo aimed to drive business innovation, operational excellence, and unparalleled customer experience.

The Boeing 787 Dreamliner represents a fundamental shift in manufacturing philosophy and approach for The Boeing Company. With 135 structural and systems partner locations around the world involved in the manufacturing and fabrication of the airplane, coordinating the end-to-end supply chain across these partners is paramount to the program’s success. One of the biggest challenges in this production model is ensuring that all partners have access and visibility to the latest demand information from Boeing and that Boeing has visibility to the supplier’s ability to meet the delivery schedule. It is crucial that all of the major subassemblies arrive in Washington at the same time for final assembly. If a subassembly is late or missing, there is very little time or space for Boeing to store the other large components. If a partner cannot meet an expected delivery date then Boeing must adjust the schedule and potentially delay the arrival of the other assemblies. Another critical success factor is to ensure continuity of supply of component parts being consumed by the Tier 1, volume partners. Due to the critical nature and dollar value of some of these component parts, Boeing retained contractual relationships directly with the Tier 2, providing partners. One of the benefits to the Tier 2 suppliers is shorter payment cycles. The challenge was implementing a pull-based replenishment model between the Tier 1 and Tier 2 partners that could be supported by the commercial relationship and payment process between Boeing and the Tier 2 partners. Above all else, Boeing and their partners needed a way to quickly identify potential problems across the supply chain and immediately assess their impacts on other partners or aspects of the manufacturing process. As an example, if a component part replenishment shipment was late, will it cause a potential stock out at a Tier 1 provider? If it will, then which delivery schedule will be impacted and against which finished airplane? What are the other subassembly production schedules that must be adjusted as a result? Having global visibility to process exceptions across the supply chain is critical to delivery performance.

When investing in partners to drive program performance, iB2B channel suppliers like GE Automation rely heavily on point-of-sale (POS) data from partners to power everything from customer intelligence to sales compensation. The ability to collect and use this information is crucial to GE’s ability to operate with transparency and enable effective sales processes. With its data in disarray, GE struggled to calculate accurate sales commissions, spent weeks matching POS data to opportunities and lacked any information on sell-through for all but the largest partners. GE realized that to achieve their goals of increased visibility and better sell-through, they would have to perform an entire overhaul of their data processes. This overhaul would require cleaning up the customer data stored in GE’s Salesforce database to eradicate issues like duplicate data and inconsistent naming conventions, amongst many others.

The 160 branches of Leggett & Platt (L&P) operated autonomously with disparate systems and a decentralized IT infrastructure. Data sharing was minimal among purchasing centers, accounting processors, and inventory management. Importing and purchasing were highly manual, error-prone, and time-consuming processes. Much of the process was handled outside L&P and could not be effectively monitored by the global compliance team. A comprehensive review revealed that the existing import compliance system was outdated and not coordinated, oriented more towards domestic purchasing. Upgrading the legacy ERP systems to minimal compliance standards was difficult and costly, making it clear that the company needed to consolidate its import management systems and automate its import processes.

The global semiconductor manufacturer faced several challenges as it transitioned from in-house to fabless manufacturing. The primary issues included maintaining manufacturing visibility, inaccurate and slow inventory reporting, increased scrap, and missed customer commitments due to yield issues. Additionally, the company struggled with overpayments for external services due to complex, manual contract reconciliation processes. These challenges were exacerbated by siloed systems that hindered timely and accurate reporting, raising the risk of regulatory non-compliance and impacting profits.

Bose faced significant challenges in understanding its reseller channel, tracking only 50% of channel data. The company lacked detailed information on how new products were received and needed centralized reports beyond internal, ad-hoc reports. With 5,000 stores and 2,000 resellers in Europe alone, Bose recognized the importance of collecting and sharing channel intelligence to overcome data analysis challenges and better support its partner network from both an inventory and marketing perspective. The company aimed to capture 80-90% of channel data to make informed business decisions and improve communications with partners.

With its growing network and global security concerns, Etihad Cargo heightened its cargo rules and policies to strengthen its compliance with global regulations. However, the company realized its risk of compliance violations was heightened because of its manual and ad hoc screening processes. Companies like Etihad Cargo must ensure that no parties in a shipment transaction are on a sanctioned or restricted list and that cargo doesn’t violate any laws — such as those governing dual-use or regulated goods — for the countries of export and import. “Aside from being labor-intensive, navigating the constantly changing regulations and export and import requirements often requires coordination across multiple parties and compliance checks,” said Andre Blech, Director of Operations and Delivery at Etihad Cargo. Etihad Cargo’s goal was to reduce risk exposure and streamline regulatory compliance by automating cargo screening through a single platform to reduce manual efforts. Most importantly, the company needed a solution and partner that could grow with them, adapt to changes in business processes, perform systems upgrades and quickly integrate new compliance regulations as they emerge.

When investing in partners to drive program performance, internet business-to-business (iB2B) channel suppliers like GE Automation rely heavily on point-of-sale (POS) data from partners to power everything from customer intelligence to sales compensation. The ability to collect and use this information is crucial to GE’s ability to operate with transparency and enable effective sales processes. With its data in disarray, GE struggled to calculate accurate sales commissions, spent weeks matching POS data to opportunities and lacked any information on sell-through for all but the largest partners. GE realized that to achieve their goals of increased visibility and better sell-through, they would have to perform an entire overhaul of their data processes. This overhaul would require cleaning up the customer data stored in GE’s Salesforce database to eradicate issues like duplicate data and inconsistent naming conventions, amongst many others.

Maintaining control of its containers is a costly and challenging concern. Besides the cost of the container itself, simply moving a container costs money — whether it is full or empty. After being unloaded, the best practice is to reuse import containers for export shipments as quickly as possible to save time and money on two empty runs. When immediate reuse cannot be coordinated, the carrier must coordinate with a third-party trucker or rely on the shipper to schedule a trucker to return the container so it is available for outbound moves. This is a manually-intensive task that cuts into profit margins. The carrier network’s massive inventory of containers and the expansion into new regions made its leaders realize they needed better control over this expensive equipment. With teams all around the world managing empty equipment, it became essential for the organization to reduce inefficiencies caused by the inability to efficiently pair empty import containers with nearby shippers for exports.

Historically, the company relied on its sales and operations teams to get information about customers and routes to market. However, with the substantial global growth of the company’s distributor and reseller network and rapid changes in customer preferences, capturing timely channel data from every country of operation became challenging. As one of the company’s leaders remarked, “Our network of distributors and resellers has grown over time. As a result, we recognized that we needed more frequent, reliable and accurate channel sales data in order to track the success of our products around the world.” Instead, the company only received distributor and reseller data monthly, and only from some of its Tier-1 partners. Often, the company’s operations teams struggled to convince partners to share any data at all. The data they did receive was in different formats with varying degrees of completeness and accuracy, and there was simply too much to manually scrub in a timely manner. Given the ever-increasing volumes of partner data the company expected in the future, manual data management was out of the question. The business simply did not have the right specialized tools and skills—much less time—to consistently collect and process actionable data. In addition, the company’s leaders desired to expand outside the consumer space into the business-to-business (B2B) sector and was looking for ways to identify suitable partners that could take them there. To address the data issues and achieve stable, consistent growth and expansion into B2B markets, they knew they would have to engage experts and find a reliable software solution.