OLYMP, a global apparel company, was faced with the challenge of improving the environmental performance of their packaging while maintaining functional requirements. They needed to calculate the environmental impact of different packaging types and identify a reliable and easy-to-use life cycle assessment (LCA) tool for packaging. They also required external results verification for transparent communication. The company aimed to meet the criteria for their sustainability flag GREEN CHOICE by 2025, which involved increasing the proportion of sustainably produced raw materials, reducing the use of critical chemicals, and establishing environmentally friendly processes in production.

Covestro, a leading manufacturer of high-tech polymer materials, was facing challenges in standardizing its Process Hazard Analyses (PHAs) documentation. The company wanted to ensure that all its plants could access and share templates, reduce the effort spent on maintaining software and keeping hardware up to date, and have the flexibility to modify templates in accordance with changes in business and industry requirements. Additionally, Covestro wanted to improve the visibility of process safety information to enable solution evaluation and tracking.

YPF, a leading energy company, was looking to enhance transparency and provide stakeholders with a clearer view of their environmental and carbon data. Their existing collection system was technologically obsolete and Excel-based, which made it difficult to aggregate environmental emissions related to waste & water management and biodiversity data into a single repository. They wanted to transition from a manual process with separated data streams to an automated and unified reporting solution. The goal was to improve key performance indicators (KPIs) in sustainability, increase transparency for audit trails and provide more reliable data for stakeholders and investors.

The Co-op, a large consumer co-operative with interests across various sectors, was facing challenges in ensuring the safety of its workforce. The retail sector is vulnerable to a wide range of incidents, from workplace accidents to antisocial behavior, as well as the verbal abuse and physical assault of colleagues. To improve the safety culture, Co-op decided to establish a new incident reporting and management system, accessible to all colleagues and contractors. The safety strategy went beyond the collection and reporting of incidents and near misses. An important success factor for establishing a broad and efficient safety culture and protecting the workforce was to evaluate the collected data with advanced data analytics capabilities to spot underlying trends and risk profile sites, ensure compliance and enable better decision-making. Due to Co-op’s complex safety requirements and the need to cover all its stores, future-ready mobile technology was needed.

Bayer, a global leader in healthcare and nutrition, was using various documentation tools for their Process Hazard Analysis (PHA), including MS Word, Excel, PHA Works, Leader and PHA-Pro. This disparate approach did not allow for shared data analysis, and Bayer had no way of identifying the common risks shared by their sites. They were in need of a risk management documentation tool that would allow for consistent and accurate data recording and analytics. The tool needed to be capable of recording PHA safety reviews across all sites and sharing them globally throughout Bayer’s network.

The multinational footwear and apparel company was struggling with a manual material approval process and difficulty managing safety data sheets and inventory. The lack of documentation led to the repetition of tasks and confusing communication to internal stakeholders. The company's processes were spread across multiple software solutions, which were cumbersome and often created redundant work.

DEUTZ, a leading manufacturer of innovative drive systems, faced challenges in securing its supply chain and being risk aware. The company had integrated Sphera Supply Chain Risk Management into its global risk management within procurement, but realized that limited visibility into sub-tiers represented a significant risk of supply disruptions or shortfalls. In 2022, the focus was on meeting strong demand, fulfilling delivery obligations, and keeping supply chains up and running. At the same time, the German Supply Chain Act with special focus on human rights and environmental protection along the supply chain, drove companies such as DEUTZ to perform risk management and reporting for all suppliers. DEUTZ’s attempt at manual identification of sub-tier suppliers failed due to two significant hurdles: it did not meet the requirements of the European General Data Protection Regulation (GDPR) and it involved a high expenditure of time, yet did not provide value to neither DEUTZ nor their customers.

Clariant, a leading specialty chemical company, recognized supply chain risk management as a megatrend that they would have to manage if they wanted to stay ahead of the competition. They wanted to secure their market position by cooperating with the right suppliers who didn’t pose a threat to their reputation, whether because of noncompliance with regulations, sustainability issues or other reasons. As a chemical company, Clariant can’t produce their products without all the supplies they need—even if that supply is only one tiny component of a much larger product.

Joyson Safety Systems (JSS), a global leader in mobility safety, faced several challenges in managing its supply chain risk efficiently. The automotive industry is governed by strict regulations and standards to ensure product safety, quality, reliability, and business integrity. As a tier-1 supplier of safety systems to automotive manufacturers, JSS had to ensure compliance with these laws and rules. The company also had to monitor the financial stability of its suppliers, many of whom were small to mid-sized businesses. The merger of two global automotive suppliers to form JSS in 2018 further complicated matters. The company had to deal with multiple interfaces of systems and databases, and the lack of a systematic approach to supply chain risk management. The company's risk reporting was based mostly on downloads from various regional systems, which were then consolidated manually. This led to inefficiencies and increased the likelihood of human errors.

Vimeo, a leading professional video platform, was facing the challenge of identifying critical signals in their decade's worth of data that could be used to improve operations, monetize services, and advance the business. The company's existing rule-based monitoring system was not able to understand each KPI's context or dig deeper into its permutations to find hard-to-detect anomalies. The company's growth was a big factor in the decision to adopt Anodot to quickly identify anomalies and trends in the data. The existing monitoring and alerting tools were based on hard-coded thresholds that couldn't cope with Vimeo's hyper growth. The threshold approach wasn't scalable because the numbers changed all the time.

Browsi, a startup providing large-scale publishers with AI tools to gauge the visibility and impact of their online ad inventory, was struggling with handling the enormous amounts of data it collects daily. The company needed an autonomous monitoring system to assess Key Performance Indicators (KPIs) such as page views, ad impressions, and other metrics for online movement and ad interaction. Browsi required real-time capabilities to respond to business or technical issues as they arise and to alert their customers of potential problems. Before integrating Anodot, Browsi had a limited view of its data systems and was alerted of technical or business problems only a day, and in some cases only several days, after they occurred. These delays and faults translated into a loss of revenue that could easily reach thousands of dollars.

The digital printing company sells commercial digital presses for digital printing of items such as pictures, labels, large format prints, etc. Its customers are print houses around the world. The business model is that the customers purchase the printing press up front and also pay per page printed. The company provides the ink and spare parts and most of the support. Whenever a press is down, the company loses both in revenue (due to fewer prints) and in support costs. Customer satisfaction also suffers as a result. Typically, whenever there was a problem, the customers’ first instinct would be to start replacing spare parts that the company provides, and only afterwards they might call support. The initial support call costs the company several hundred dollars, and still may not resolve the problem. If an issue persisted or recurred, an expert would be sent (at the cost to the company of a few thousands of dollars per call). In this process, the company lost revenue from presses that were malfunctioning, paid a lot in support and parts, and also eroded its customer satisfaction.

Before using Anodot, Wix's data analysts manually measured and analyzed vast amounts of data. This included activity related to customers' actions on Wix, such as success and failure rates while opening the Wix website editors, checkouts at e-commerce sites hosted by Wix, logins by premium customers, and other important events. The analysts spent a great deal of time scrutinizing reports and graphs to try to detect issues, but important issues were sometimes identified hours to days after they had occurred. Wix needed a real-time alert system that would indicate issues without manual threshold settings in the key metrics.

As Minute Media’s business scaled, it became increasingly difficult to keep tabs on incidents that impacted user experience, revenue, and costs. The company needed a solution that could help identify underlying issues in the platform to prevent penalties with Google and other supply-side platforms, understand issues with the integrity of data aggregated from a wide variety of sources, improve the ad profit margins, especially during consistently changing patterns such as the pandemic, and prevent revenue loss by quickly notifying of fraudulent bot clicks on video ads. Prior to Minute Media adopting Anodot, data analysts extracted data and worked with it manually to try to spot anomalies or trends. This manual process was untenable, especially as the company grew, leading Minute Media to begin looking for an automated solution to identify anomalies in the business data.

Telecommunications companies are facing the challenge of managing and monitoring an increasing number of products, campaigns, retail channels, prepaid and roaming services, billing, customer experience and support, and order and fraud management operations. The complexity and dynamic nature of business data make it difficult for static monitoring approaches to effectively track these metrics. Despite redundancies in data centers and telecom networks, outages and incidents still occur, impacting network, business, and customer experience management operations. The cost of these incidents can be significant, with a 2016 survey indicating that the average cost of a data center outage rose 7% from 2013 to 2016. For a telco operator with annual revenues of $1B, annual incident costs can range between $11.6M-$41.1M, depending on the types of systems used for monitoring.

The company, a mobile gaming giant, relies heavily on its in-house developed cross-promotional system for revenue. Any bug or change in the system could lead to more than 15% loss in in-app purchases. The company used to monitor impressions, clicks, and conversions of their cross promotions on a weekly basis using Tableau dashboards. However, this manual process was slow and inefficient, often leading to delayed insights on glitches. For instance, a new promotion caused major crashes across several platforms, but the issue was not logged until the next day. It took almost four days for the company to realize the problem, and it was only discovered when checking another unrelated system.

Redis Labs, a company in a high-growth phase, was acquiring many enterprise customers in the Fortune 500 and Global 1000. It needed to scale its customer service while maximizing efficiency and minimizing time and resources. As Redis Labs scaled, it became responsible for managing tens of thousands of databases and could no longer manually monitor their usage patterns individually. The company wanted their monitoring to operate on a more granular level, picking up incidents that might otherwise go unnoticed. With the growing volume of databases also came a wider variety of usage patterns, which couldn’t be properly tracked with the fixed alerting that had proved sufficient in the company’s early days.

Xandr’s marketplace operates at a scale and complexity that are hard to fathom. The company serves multiple billions of ads every single day, handles 45 million transactions per second, and processes more than 175 terabytes of data. Xandr’s platforms make a lot of complex business decisions to reach the right customers for the marketers. When glitches occur and blank ads are served, all parties lose money. This has to be detected and resolved quickly before losses mount. The extensive nature of Xandr’s partnerships meant that issues could take a week or more to detect and resolve. Xandr’s infrastructure includes thousands of servers and hundreds of applications across its global data centers. The company used a variety of disparate tools to monitor the performance of the infrastructure itself as well as the delivery of the ads.

GetYourGuide, a global booking platform for travelers, was facing challenges in spotting issues in their business data in real-time. They were taking too long to identify problems, which led to revenue losses in their cloud services and marketing budgets, and negatively impacted user experience. The company needed an automated solution to control cloud costs, track product usage for changing revenue, and monitor marketing activity and ad spend. The global pandemic further complicated matters, as the travel industry was severely impacted, and GetYourGuide had to adjust its operations and spending accordingly.

Rubicon Project, one of the largest ad exchanges in the world, processes trillions of transactions each month in real-time auctions. The company receives more than 13 trillion bid requests per month, handled in its seven global data centers, housing more than 55,000 CPUs. However, the Tech Ops team could not monitor more complex aspects of business and trends, especially not in real time. For instance, Rubicon needed real-time insight if a large institutional buyer deviated from its normal transaction trend by any percentage in one of the global data centers at any hour of the day or night. Such deviations could have a devastating effect on the exchange if there was a delay in addressing it with the customer. Along the bid stream, there were many potential areas for communication or technical breakdown, which would prevent the bid from going into the auction, and negatively affect overall bid health.

With the rollout of 3G and 4G technologies, telecommunication service offerings have grown. Cell phone usage has skyrocketed. Voice, video, and data have converged to offer rich new services that customers rely upon. High-definition video consumption and other services are consuming more network bandwidth, making it more important than ever to accurately manage and maintain network performance. In this case study, a leading provider of telecommunications services needed to ensure end-customer satisfaction and quickly mitigate any network performance issues, where any incident could easily cost them millions of dollars. They had to be able to monitor service assurance, as well as analyze data at detailed levels to track the underlying quality of network performance and to avoid unexpected outages. Managing multiple communication applications and platforms required advanced network monitoring and orchestration to ensure optimal network performance. The company didn’t have clear visibility into how network resources were used. Their available network management tools were static and only addressed specific needs, unable to provide reliable, transparent data for insights in real-time.

Payoneer, a global cross-border payments platform, was facing several challenges. They needed to replace their traditional monitoring system with one that could scale with their rapidly growing business. They also wanted to provide mission-critical monitoring-as-a-service to internal groups. Another challenge was to eliminate wasted engineering effort by preventing false positives on operational metrics. Lastly, they wanted to prevent revenue loss by accurately forecasting demand for funds. Given that the company’s operations span so many countries and involve a massive number of partners and their APIs, Payoneer continuously monitors nearly 200,000 metrics to ensure it meets SLAs and general reliability and performance targets.

NetSeer, a leading adtech company, was facing challenges with its business and operational KPI tracking. The company was using several tools such as Graphite and alerting systems, but they were not accurately alerted on key business problems. The standard static thresholds were causing either too many false positives, or not enough alerts. For instance, the company tracks the number of ad calls to their front end and back end throughout the day and night. Daytime requests are typically 20 times more than nighttime requests, and with a static threshold, even a significant drop in daytime requests would not trigger any notification. Additionally, performance issues would crop up from time to time when new services were implemented and the NetSeer team had no way to identify them quickly.

Mayvenn, a company that provides high-quality beauty products and aims to connect customers with the right stylists, relies heavily on data for its operations. The company moves a variety of data, including ad and marketing spend, email, text, and customer service data, from Amazon S3 to Amazon Redshift using Python for analysis and into Looker for reporting. However, the company faced challenges with its previous data orchestration tool, Alooma, which hindered fast iteration of ETLT. The data team at Mayvenn often found themselves blocked on projects due to dependency on the engineering team, which often had a full queue.

HNI Corporation, a global leader in workplace furnishings and residential building products, was in the midst of a planned five-year transformation from seasonal bulk orders by big distributors to customized orders by dealers, individuals, and enterprises. This required a refactoring of the management of the supply chain by taking control of the data from ordering systems, ERP, and fulfillment systems. The COVID-19 pandemic and disrupted office and work-from-home environments forced HNI to speed up changes to how it does business, requiring a solution with flexibility and speed as a cornerstone for transformation. The data science and analytics team at HNI needed a platform that could scale with them, minimize cross-functional dependencies, reduce time-to-pipeline production, and refocus on the logic versus the infrastructure.

The New York Post, a highly data-driven publisher, was faced with the challenge of accelerating time-to-market for internal reporting, financial, and other data initiatives. The upcoming crackdown from Google on third-party cookie data in the Chrome browser accelerated the need to drive more data-driven personalization and engagement across the New York Post sites. The team at the New York Post required a faster way to ingest, aggregate, transform, and write out a variety of critical new data feeds in order to meet various business demands and requirements.

TE21, an education company, was facing challenges due to its growth and the development of more complex business processes. They were outgrowing their manual QuickBooks solution and needed a more robust and automated process. The company also needed a CRM system that could enable a true “quote–to–cash” process starting with sales, which could be easily connected to their financial billing, collection, and reporting system. The challenge was to find a solution that could handle the growing business's challenges.

Netwrix Corporation, a market-leading visibility and governance platform for IT environments, was facing several issues due to their archaic data management system of copying and pasting data between datasets. This process did not scale as the company grew to a $20 million company and it quickly became clear that the company needed a better solution. The company faced three main challenges: eliminating errors in their copy and paste method, scaling their processes, and improving data integrity. In order to solve these systematic problems, Netwrix needed an efficient and effective enterprise planning software that could allow them to smoothly integrate with Salesforce.

Lufthansa Systems, a leading IT service provider for the airline and transportation markets, needed a robust and stable database platform to underpin its airline planning and routing software. This software is sold to hundreds of airlines globally to ensure every flight is a safe one. The company offers a modular range of products and services to manage all aspects of airline operations and planning. The challenge was to provide real-time availability of data, high levels of uptime, reliability and stability, cost-effective licensing, and vendor support.

Lechler GmbH, a German mid-sized company specializing in the manufacture of nozzles and custom spray technology solutions, needed to revamp its company-wide manufacturing technology solution. The company aimed to improve its business processes, enhance and extend its reporting options, and ensure flexibility in its database administration. The ultimate goal was to continuously improve customer service and minimize the time from order to delivery. Prior to the implementation of the new system, Lechler was working with a large data processing center which was very inflexible.