Trax is the driving force of the store of the future. The world’s top consumer goods companies and retailers use the Trax cloud platform to gain the power to see what happens at shelf and the agility to delight shoppers in new ways. Armed with Trax data and insights, retailers gain granular, SKU-level visibility to changing store conditions. Trax is a global company with hubs in the United States, Singapore, China, France and Israel, serving customers in more than 90 countries worldwide. The Challenge   Trax operates a complex, multi-cloud platform that currently runs on both AWS and Google Cloud. The company has several accounts on each cloud. The environments themselves are complicated, with Kubernetes clusters and numerous microservices. There are a lot of moving parts that are not easily tracked. The cloud providers’ native tools fall short of Trax Retail’s needs to closely manage cloud costs at the workload level. The Solution   Anodot for Cloud Costs Management provides the ability to measure the different workloads that happen inside a single server in the cloud. This allows Trax to measure a critical KPI, the “cost per image processing,” which shows how effectively the Trax system is operating. Anodot Cloud delivers real-time alerts for unexpected changes in cloud usage and recommendations for cost savings. Key Use Case Drove Requirements for a Cloud Cost Monitoring Tool   A key function of the Trax platform is to take photos of store shelves, upload the images to the cloud, process them through various engines, and deliver information back to the customer. This gives rise to one of Trax Retail’s most important KPIs: the cost to process each of those images. This cost is calculated based on the time it takes to run various applications in the cloud, i.e., cloud usage. With large volumes of images processed every day, this system represents a large source of Trax’s cloud costs. Mark Serdze is Director of Cloud Infrastructure at Trax Retail. Serdze says that when they began looking for a cloud cost management tool, they were very focused on this particular use case. “This KPI gives us a good idea of how effectively our system operates,” he says. “When we moved into Kubernetes, because of the way this platform operates as a cluster with scaling up and down, we lost the ability to break down the billing through the native cloud tools. We used to invest a lot in tracking instances but we never had an ability to measure the different workloads that happen inside a single server in the cloud.” Serdze says this was the main motivation behind looking at Anodot Cloud. “Getting to the level of detail that we need is just part of what this tool offers. In the end, Anodot Cloud won our business,” says Serdze. “In addition to meeting our requirements really well, Anodot Cloud is a SaaS offering, so we didn’t need to install and maintain any kind of infrastructure within our cloud environment. Also, Anodot can keep up with our crazy scale usage patterns. We crashed the other tool with our large volume of images.” Read Full Case Study Here Cloud Cost Monitoring is Critical to the Business   Serdze calls cloud cost monitoring “super critical” to Trax Retail’s business. “One aspect of our business is to replace manual in-store labor at various retailers with our automated solution. Anodot Cloud is an AI-based automated solution. We constantly need to make sure that whatever algorithms we develop can be operated in the most efficient way, cost wise, so it is crucial for us to track them,” says Serdze.  Anodot Cloud allows Trax to monitor cloud costs in a very granular way. “Anodot can detect in real time the anomalies in different services and new deployments that, for example, affect costs of specific microservices,” according to Serdze. “In contrast, the native tools from the cloud providers don’t give as much visibility. We can monitor our services but only retroactively and not with the level of granularity that we want.” The Benefits Are Myriad   There are several teams within Trax Retail that are the main users of Anodot Cloud. The infrastructure team does the day-to-day tracking of cloud costs and resources. The data services team implements the API connections. “In general, Anodot Cloud allows us to be more responsive to trends because the data is more updated and live than what we were previously used to,” says Serdze. “In the past we would pull this data and aggregate it once per month. Now we are doing it continuously. Mostly it reduces the toll on our DevOps team and allows us to focus on optimizing our production environment rather than creating cost tracking systems.” Serdze says there is one key thing they can do with Anodot Cloud that they could not do before. “The most important thing is that we can connect between the usage patterns of our microservices inside the Kubernetes clusters and their actual costs, which is something that’s very hard to do in the native cloud tools.”

Razorpay, India's largest payment solution provider, enables frictionless transactions, revolutionizing money management for online businesses. Founded in 2014, Razorpay offers a fast, affordable, and secure way for merchants, schools, e-commerce, and other companies to accept and disburse payments online. The Challenge Solid anomaly detection is crucial for Razorpay, particularly when serving businesses in payment management. Sudden drops in success rate drops, ticket resolution delays, or fraudulent transactions can impact customer finances and decrease client satisfaction with Razorpay. Other issues Razorpay was facing: - Slow issue detection - Lack of real-time/near real-time alerts - Delayed critical alerts resulting in financial losses - Manual effort for anomaly and fraud detection - Challenges in tracking alerts across dimensions - Lengthy post-anomaly detection root cause analysis (RCA) The Solution Anodot was the partner Razorpay needed to address key issues like ticket resolution time and fraud detection. With a user-friendly UI for non-tech business users and ML forecasting capabilities, Razorpay can enhance the customer experience with automated monitoring and real-time anomaly detection. Main KPIs tracked in Anodot: - Payments SR in different business verticals - Customer success ticket creation and average time to close - Fraudulent transactions in different payment channels - Average payment checkout time - Refunds claimed as fraud Read Full Case Study Here  Anodot: Real-time alert and forecast platform using ML and AI for business monitoring   Real-time communication With Anodot, analytics and engineering teams can receive alerts across multiple channels, including Slack for seamless communication and collaboration for efficient monitoring and problem-solving.   Enhanced customer support  Anodot is open to building customer-requested features and provides a seamless onboarding experience to familiarize users with the tool quickly. Answering all questions and providing optimized, structured solutions.   Removal of manual anomaly detection Anodot's real-time alerts help reduce the financial impact on the company. Ops and analytics can spend less time fixing anomalies and more time on innovation and operational efficiency.   "Anodot is a valuable asset for sending timely alerts and notifications to the right recipients while facilitating quick and easy feedback."  Nishant Thakar BI and Data Strategy, Razorpay

Discover how Aqua Security transformed their cloud cost management with Anodot, achieving real-time visibility and rapid ROI while eliminating manual reporting and optimizing spend across their complex multi-cloud environment.

While it certainly isn't meant to capture the full range of the Anodot platform, it does give you a sense of how you can dig deeper into your metrics to find hidden anomalies you may not have known were there.

“Anodot sets itself apart with automated anomaly detection, rather than manually setting thresholds.”

With Puma, we integrated revenue measures first as this is was their initial goal for using Anodot. However, while working with data, we decided to expand our view to a much broader metrics than just revenue.

With expanded use of microservices, you’ll find yourself confronted with challenges in meeting your service level agreement. These are the service mesh technologies and monitoring tools that will help you better manage service-to-service communication.

Payoneer's payment platform streamlines global commerce for more than 5 million small businesses, marketplaces, and enterprises from 200 countries and territories. Leveraging its robust technology, compliance, operations, and banking infrastructure, Payoneer delivers a suite of services that includes a cross-border payments, working capital, tax solutions, and risk management. Airbnb, Amazon, Google, WalMart and Rakuten are among its many customers. With millions of financial transactions happening on its platform 24x7, Payoneer closely monitors 190,000+ performance metrics in every area across the company. They are watching for any indication that something is off kilter with the business — for example, an unexpected decline in people registering for a new account, or a glitch in an API with third party software — in order to address issues quickly. Anodot helps Payoneer stay on top of its business through timely anomaly detection of various metrics and highly accurate forecasts for currency distributions. Here are a few examples of how Payoneer uses Anodot: Log Analysis to Quickly Spot Issues Yuval Molnar is Senior Director of Production Services at Payoneer. His group created an integration between the Coralogix log analysis platform and Anodot for autonomous anomaly detection. The metadata from every service Payoneer has — now some 1,000+ services — goes into Coralogix and then is fed into Anodot to look for anomalous behaviors within the logs. Using Anodot, Payoneer has been able to increase time to detect critical incidents by 90% and increase visibility into payment operations 3X. "Now we have a monitor in place that checks anomalies in terms of number of errors or number of logs, which is awesome," says Molner. "This is something we didn't have before and it's a game changer because we completely eliminated false positive alerts and vastly accelerated time to detection of real problems." Spotting Trends in Customer Care Payoneer monitors the types of calls coming into its customer care team. Every time a customer calls the care center, the service agent logs the subject and the sub-subject of the reported issue. From time to time, there is a trend where the call center is getting a lot of complaints about a specific subject. With Anodot, service agent reports are fed into the system. If there is a trend where a particular issues is increasing in frequency, Anodot will automatically product an alert in order to get the issue remediated quickly. Forecasting Currency Needs The nature of Payoneer's business is that customers can withdraw funds from their own accounts at any time. Payoneer must have sufficient funds available, in the currency the customers prefer, to meet withdrawal demands. The treasury team at Payoneer must make forecasts for account locations in more than 100 countries in 50 different currencies. The team had been computing the forecasts manually, relying on their own experience and using excel spreadsheets. Payoneer now uses Anodot's forecasting solution to learn patterns in data pertaining to customer withdrawals. Anodot is able to predict which currency could experience a shortfall. With Anodot, payment companies like Payoneer, fintechs, merchants and online sellers can benefit from autonomous payment monitoring. Our AI-based solution learns the behavior of each metric and adapts to seasonality. Anodot can identify payment anomalies even through fluctuating demand, proactively alerting payment teams, and automating resolution via KPI. Monitoring for Cybersecurity Issues Aviv Oren is EMEA Regional Manager of Production Services at Payoneer. He supports internal teams who need to know about anomalous conditions or activities. Oren calls it monitoring-as-a-service. "We are the ones getting alerts from Anodot on technical issues and we notify the appropriate groups about them," he says. "Before Anodot, we tried detecting events using static thresholds. That resulted in a lot of false positives. We got alerts when there was really no issue but had to check to make sure things were okay. That wasted a lot of our time," says Oren. Oren's team also monitors cybersecurity metrics to help detect malicious activity against the company's numerous applications and systems. For example, the production services team gets an alert when there's an unusually large number of successful logins, an increase in unsuccessful logins, or even a drop in successful logins. Anomalies in these metrics could be indicative that an application's login page is being attacked or harvested.

We just can’t comprehend data as AI can – that’s the main theme running through this series. In this, the third and final post of a series on uncovering hidden opportunities using AI analytics, we’ll discuss the shortcomings of dashboard tools, and how traditional business intelligence (BI) tools built upon dashboards can’t keep up with the speed of your business because they provide “too little, too late” when it comes to the information you actually need: real-time, actionable insights. Let’s breakdown the specific shortcomings of these big data dashboards, and demonstrate how an autonomous AI analytics solution fills the need that traditional tools simply cannot meet. The fatal flaws of Big Data dashboards None of the visualizations on the dashboard are actionable. The scatter plots and bar charts can tell you what is going well or not, and even then only in very general terms. More importantly, those visualizations don’t tell you why those metrics are the values they are and knowing why is necessary for formulating and executing the fast reaction needed when a business incident occurs. That reaction, the how of fixing the problem or capitalizing on the opportunity, is where business intelligence meets business strategy. At least it can unless you’re using a tool that provides only general indications. Dashboards inherently gloss over isolated, but significant data anomalies. If you read our previous post on business metrics, you know exactly why important business incidents affecting only one component or segment of your business get lost in the crowd of statistics like totals and averages which combine different individual metrics for one overall KPI. When it comes to anomalies, “isolated” and “significant” are not mutually exclusive. A small blip may represent a large, untapped opportunity. A small spike in online orders from a particular demographic may indicate that a small-scale marketing campaign could be scaled up, and generate even more revenue and new, loyal customers for the brand. The “summarizing” nature of dashboard tools is a serious barrier to real-time insights, especially to the crucial why, as mentioned above. In order to get to the reason, you need to group and correlate multiple anomalies with a variety of event data. However, without analyzing data to the most granular level of detail, there can be no correlation, and thus not reach any real-time actionable insights. For this type of granularity, you need a solution that scales to the millions of individual metrics (time series) you are collecting. Dashboards, however, can’t keep up with the constantly changing and massive amounts of data and thus squander the BI value of this data. A popular quote, attributed to Albert Einstein, gives some relevant advice: “Everything should be made as simple as possible, but not simpler”. Due to their focus on the visualization of only a few metrics, instead of insight from all in real-time, dashboards leave things too simple. Big data dashboards alone don’t detect anything. This is why data analysts have to set (and endlessly re-evaluate and re-adjust) static thresholds, requiring continuous manual monitoring. This is the key reason why they are Small Summary toys, not Big Data tools. Static thresholds produce Alert storms, a sea of alerts that data analysts have to spend way too much time trying to figure out what is at the root of the issue. While doing this, there is a good chance that an important business service is performing poorly, or worse, it is down! The limited insights from dashboards are usually too late. Even if you had the human resources to continuously monitor those dashboards by skilled data analysts, you would still not achieve real-time actionable insights. Traditional monitoring tools, like big data dashboards, suffer from inherent business insight latency: they do not show status in real-time (a key factor for a timely response) – which means that you will really only discover business problems when it’s too late. Take, for example, how for a short period of time on Black Friday, visitors to the Currys PC World website in the UK saw £289 iPads listed for just £4 once a discount code was applied. Thinking they were getting a great deal they jumped on it, only to have their hopes dashed once the company realized the error. Then, there was the Amazon Prime Day glitch that brought the price of a $3,000 telescope lens to less than $100. While some commenters on Reddit said they had received confirmation that their orders had shipped, others were still waiting and wondering whether Amazon would cancel. For isolated events – such as a spike in orders for a particular product due to a pricing glitch – this lag doesn’t just mean missing the event, but losing money and damaging your company’s reputation. [CTA id="5dd0fbf9-e8a1-45b7-a603-75cf69c78502"][/CTA] The Autonomous AI Analytics advantage Grouping and correlating multiple anomalies by design, AI analytics brings your team the most important insights first, eliminating alert storms. An AI analytics solution which can monitor millions of metrics to a granular level, gives you both the detail and scale you need to be able to identify the business incidents that matter, including the most subtle ones, that big data dashboards would overlook and obscure. Automated anomaly detection frees your talented data analysts from the futile task of trying to manually spot critical anomalies while your business is moving forward, sometimes at breakneck speed. Really, you want to save the data analysts for just that… the actual analysis. AI analytics continuously analyzes all of your business data, detecting the anomalies that matter, and identifying why they are happening through correlation across multiple data sources to give you the critical insights that just relying on dashboards can’t. Your analytics solution needs to be intelligent to deliver business intelligence. Unlike dashboards, by using automated machine learning algorithms in an analytics solution, you can eliminate business insight latency, and give your business vital information to strike while the iron’s hot.