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.

“Anodot allows us to capture incidents an hour or two before they create a customer experience impact. This also helps take complexities away from our operations people”

Anodot Chief Data Scientist Ira Cohen covers the five alert settings you should adjust to get more relevant and actionable alerts for your business metrics.

The State of KPI Dashboards for Business Intelligence When they were first introduced, business intelligence (BI) KPI dashboards were intended to help provide continuous visibility into an enterprise’s performance, and thus optimize the analysis process of BI. Continuous visibility, however, is not the same as real-time intelligence. This has become increasingly and painfully clear to businesses over the years. In our latest series of articles we’ve shown how dashboards have failed to deliver on their hype for many reasons: Real actionable insights come from intelligently correlating many (often subtle) individual clues, which KPI dashboards are unable to do because they are designed to give a general overall picture. In their goal to simplify the complex, business intelligence KPI dashboards are unable to present the granular data required to quickly get to the root cause of a KPI’s behavior, thus requiring analysis to occur in a separate tool. So much for the “single pane of glass”. KPI dashboards are tools for visualizing the behavior of a metric, not for monitoring them. Since they require human eyeballs continuously glued to the screen, KPI dashboards are unable to automate the real-time discovery of the clues which can help you earn more revenue or decrease losses. That’s just the tip of the iceberg. KPI Dashboards for Business Intelligence: Delays, Costs, and More Costs Traditional BI products which usually feature these dashboards are complicated platforms that require a heavy investment of time and money to implement, resulting in a long time to value. When you buy one of these tools, you’re not getting a turnkey solution, but rather a down payment on a large IT project which frequently blows past deadlines and budgets. Even if you’re lucky and actually get one of these solutions up and running, the costs – and delays – keep coming. That’s because those KPI dashboards for business intelligence don’t continuously feed on a stream of real-time data, but on reports and tables generated by IT. This increases business latency and kills real-time decision-making. Without real-time data, profit-damaging business incidents come and go days before you’re even aware of them. Do you want to avoid the lag due to waiting for the reports? Be ready to pay more for additional custom integration. These integrations add a whole new layer of labor costs, further pushing out the already long time to value. The additional expenses include the time of your CDO, CTO, data scientists with enough experience to do this integration completely, and often additional programmers, data analysts, and decision-makers. Your only alternative to these hefty one-time integration costs is often a continuous subscription with a middle man like Zapier for the connectors you need. Even for the business events those dashboards are able to catch, only skilled analysis can deliver an explanation for why a particular KPI changed the way it did. Not everyone who relies on the BI dashboard is a skilled data scientists capable of performing that sleuthing. Truth be told, those KPI dashboards require sleuthing even before they’re created. This is because every dashboard first needs to be designed, and that design begins with knowing what questions you want to be answered. This is of course completely useless when you are looking for unknown unknowns. Since, by design, business intelligence KPI dashboards visualize only a small subset of a company’s important metrics, some KPIs will inevitably be left out if they don’t match all the criteria you currently think are important. Since the dashboard, and the analysts using them, can’t see every metric, picking the wrong KPIs is a very real risk. No choice of chart types, colors or text options will be able to highlight signals in data you’re not collecting. Correcting for this early mistake in the dashboard design is time-consuming and costly since any new KPIs need new data to be gathered, new reports run, a chart type chosen, etc. With all of these built-in delays and latencies inherent in KPI dashboards, is it any surprise that the BI adoption rate among employees was only 30% in Gartner’s 2017 survey? Unsurprisingly, a solution that is unable to deliver high-velocity business intelligence is also unable to drive rapid adoption. AI Analytics Drives Past Business Intelligence KPI Dashboards  If KPI dashboards for business intelligence have so many problems, why are companies still using them? Slick visualizations hypnotize users to the point where they miss the big picture. When staring at data they become blinded by information and miss the obvious. These tools give the illusion of transparency into business performance, but that transparency, if it comes at all, provides no value if it is not provided at the speed of your business in a way that makes the data insights truly actionable. AI analytics, however, can deliver actionable insights in real time. With built-in data science, this new breed of solutions uses powerful machine learning algorithms to accurately and automatically detect anomalies in every time series metric—not just a few KPIs. AI analytics also automatically correlates and combines related anomalies, giving your analysts all the clues they need to respond to business incidents like third-party API breakage, a pricing glitch, or a surge in product orders. These are the types of rapid-fire events every modern company needs to manage and those which do so successfully have long left cumbersome KPI dashboards behind.