Imagine lining up before sunrise on a cold November morning the day after thanksgiving, waiting in line to buy up to hundreds of dollars in discounted goods – and then being informed that the retailer couldn’t accept your credit card. Would you shop there again? This nightmare scenario is what happened to Macy’s shoppers on November 24th, 2017, aka Black Friday. Around noon, overcapacity issues shut down the retail giant’s payment processing systems, turning them into a cash-only enterprise for up to six hours on one of the busiest shopping days of the year. The blow to company revenue may be calculable, but the damage to the company’s reputation is not. How can companies avoid these expensive and embarrassing disasters? Glitches Are More Common Than You Think Retailers are plagued by glitches, big and small. Even Amazon, probably the most technologically sophisticated ecommerce platform on Earth, is not immune to the occasional error (Free Echo Dot’s anyone?). So how are smaller and less tech-savvy retailers expected to cope? The glitches they encounter could include: High-volume traffic overloading payment processing servers Orders in online shopping carts failing to complete Incorrect pricing for goods sold online or in stores Mistargeted online advertising Missing opportunities to upsell or cross-sell customers based on faulty analytics Did you know that customers are up to 3x as likely to post a negative review of your company after a bad experience? And that 80% of potential prospects will desert your company if they read negative reviews of your products and services? Though it’s hard to put a price on the loss of reputation, the lost sales do stack up, averaging $250,000 per incident during Black Friday and Cyber Monday, and $40,000 per incident during the rest of the year. Smart Error Handling Demands a Proactive Approach Until recently, retailers had few options when it comes to preventing failures in their ecommerce platforms or their brick-and-mortar stores. The traditional approach was to wait for something to break, and then fix it as quickly as possible. In Macy’s case, “quickly” was about six hours. That’s not an acceptable speed. Another option is to try to track everything that is happening with dashboards and alerts, which quickly grows out of hand – how do you know where to look on your hundreds of beautiful visualizations? Which alert is meaningful when you get hundreds every day? One of the difficulties in these scenarios is that similar-looking errors might have diverse causes. Your cash registers might not be able to process credit cards, but that could be due to a failure in any number of separate applications. We refer to these errors as “micro-glitches” – multiple, nearly imperceptible failures in multiple locations which accumulate to cause spectacular outages. Going back to Macy’s, according to news reports they suffered an overcapacity outage of their credit card systems. Even this kind of outage comes with its own subsidiary failures and forewarnings, however. Systems as theoretically robust as the payment processing system for a major retailer don’t fail all at once. There are cumulative warning signs, such as an increasing trickle of transaction failures, or additional latency during card transactions. The ability to detect and interpret these warning signs could have meant averting a system failure at 10:00 AM, as opposed to trying to recover from a crash at noon. Maybe I’m stating the obvious here, but it’s much better and easier to prevent a failure that you identify early than to try to recuperate after the fact. How Anodot Can Help Where does Anodot fit in? Our AI-powered analytics gives companies the ability to collect and interpret real-time time-series metrics from across their payment processing systems and every other internal and external system, letting them detect and mitigate failures before they begin to drain their revenue and reputations. This can protect companies from the types of disastrous events that Macy’s experienced on Black Friday. To learn more about Anodot, and how our technology can give early warning capabilities to your ecommerce platform, sign up for a free demo today.

Find out how Anodot’s business incident detection system automatically alerts the mobile gaming company to any changes in their business data streams.

Few things have propelled the Internet of Things’ dizzying growth in recent years as much as machine learning and the innovators who are pushing it. Independent, intelligent machines that can comb through data to make their own decisions are, to some, the only reason such a phenomenon as the IoT can exist in the first place. When it comes to IoT, the connected devices are expected to work with little human intervention. However, no matter how intelligent machines become, human beings still need a way to monitor them, to check that everything is working as planned. Adding machine learning to IoT monitoring tools helps detect problems and anomalies and enhance the analysis for human operators. Monitoring and management systems can not only check the performance, but can also provide real-time visualizations of device activity, irrespective of their locations: robots on factory floors, sensors in shipping fleets or medical equipment in a hospital. IoT needs a system to identify unusual situations and alert when attention is needed, before equipment failure disrupts operations. Industrial IoT Will Transform Many Industries While much of hype around IoT focuses on consumer applications, like smart homes, connected cars and consumer wearables like wristband activity trackers, it is the IoT’s industrial applications which may ultimately dwarf the consumer side in potential business and socioeconomic impacts. The Industrial IoT stands to transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy. IoT Interoperability is critical for maximizing the value of the Internet of Things. According to a McKinsey report “On average, 40 percent of the total value that can be unlocked requires different IoT systems to work together.” With its open APIs, Samsung ARTIK Cloud breaks down data siloes between devices and enables a new class of IoT applications and services. By connecting directly to ARTIK Cloud, Anodot provides a layer of analytics and real-time detection of incidents to the collected data. AI-powered Analytics Automatically Monitors and Turns IoT data into Insights Anodot analyzes the millions of data points that stream into ARTIK Cloud from various IoT sensors in homes, factories or other IoT implementations. Anodot is disrupting the traditional business intelligence industry with its AI-powered Analytics solution. Anodot’s proprietary machine learning algorithms learn the normal pattern of behavior from the real time event data streaming into ARTIK Cloud, and detects anomalies from the spikes and dips in IOT real time data, sending alerts about any metrics that deserve greater attention. Anodot scores each anomaly based on how far “off” it is from the normal, correlating multiple related anomalies to avoid alert storms and aid in determining the root cause of any issue encountered. Disrupting the static nature of Business Intelligence (BI) tools BI tools are generally designed to help highly analytical individuals make very specific decisions, they are backward-looking, and they lack the ability to provide actionable information to front-line analytics teams. Anodot is disrupting the static nature of the Business Intelligence (BI) market, differing in several key areas: Traditional analytics and BI solutions deal with historical data, not this minute, not showing a real-time status. Due to these limitations, they typically look at only a subset of all the available data, yielding at best delayed and at worst incomplete results. Traditional BI tools that monitor data cope with just part of a problem, only focusing on the data they think they might need, while specific signals could get overlooked. Traditional BI tools struggle to get an integrated view of all business metrics, focusing on just a few key metrics Anodot analyzes streaming data in real time and predicts the future behavior of each metric. Anodot automatically identifies what is happening and can ingest all metrics, focusing on just the important ones. Anodot applies algorithms to large volumes of data in a more efficient manner to discover patterns or trends in the data — a task that BI tools were not designed to accomplish. Predictive Maintenance Prevents Breakdowns The Internet of Things can create value through improved maintenance. With sensors and connectivity, it is possible to monitor production equipment in real time, which enables new approaches to maintenance that can be far more cost-effective, improving both capacity utilization and factory productivity by avoiding breakdowns. Predictive maintenance and remote asset management, can reduce equipment failures or unexpected downtime based on current operational data. Vastly improved operational efficiency (e.g., improved uptime, asset utilization) through predictive maintenance and remote management improve operational efficiency through predictive maintenance, and achieving results such as savings on scheduled repairs (12%), reduced maintenance costs (nearly 30%) and fewer breakdowns (almost 70%). In the screenshot below, Anodot monitors factory data generated by IoT sensors. All machine parameters are tracked and learned in real time, correlating metrics temperature, vibrations and noise. Anodot identifies several anomalies, possibly indicating a problem.   Outlier Detection Makes Proactive When Maintenance Possible Anodot can also be used to compare the performance of similar things, and detect outliers. As the sensors and components become more prevalent in industrial environments, it is possible to collect data from multiple industrial IoT components and correlate a particular component behavior with similar components. Anodot can pinpoint outliers not just within the data for one machine, but for multiple machines (input changes like increased temperature). This will significantly improve predicting the likelihood of equipment failure or a need for unscheduled maintenance to maintain equipment efficiency. Remove Seasonal Fluctuations and Expose Real Trend In Underlying Data Machine data has a seasonal component, cyclic patterns in observed data over time. For example lights that are turned on in the evening, turned off at night, and then on again in the early morning. On the weekends the behavior may be different. These and other patterns make it difficult for a human user to identify static thresholds to set for manual alerts, and in fact make most static thresholds irrelevant. Anodot, however, automatically learns how the data behaves, including all of its patterns and seemingly random behavior, correlating between external events (like holidays or weather changes) and the collected data metrics. Making Sense of Your ARTIK Cloud Data Diverse devices and “things” continuously send and receive data to ARTIK Cloud. Anodot helps make sense of what is happening all in real time. Without having to set any thresholds or even understand how the data is supposed to behave, Anodot can provide automated, pre-emptive alerts. Learn how Anodot is revolutionizing IoT data in our new partnership with Samsung Artik Cloud

The global advertising market is growing and forecasted to exceed $700 billion soon. Much of that growth is attributed to digital advertising aimed at people who are spending more time online, looking at screens, streaming ad-supported music and entertainment, and connecting through social networks. Companies’ spending on digital advertising experienced double digit growth in 2020, despite the pandemic.  Adtech companies are responding to this growing demand with fast-paced programmatic advertising, which utilizes data insights and algorithms to automatically serve ads to the right user, at the right time, on the right platform, and at the right price. The Importance of Data in Programmatic Advertising As the world of digital advertising becomes more dependent on this type of programmatic media buying, data is transforming how the adtech industry operates. The data – cost per impression, cost per click, page views, bid response times, number of timeouts, number of transactions per client, etc. –is as important as the money spent on those impressions. The data shows how effective the ad buys really are, thus proving whether or not they are worth the money spent on them. This is one reason that data must be continuously monitored. The vast array of moving parts in online advertising means that adtech companies need to collect, analyze, interpret, and act upon immense datasets instantaneously, every single day. The insights that come from this massive onslaught of data can create a competitive advantage for those who are prepared to act upon those observations quickly. Traditional business intelligence tools can’t scale to fully support adtech needs Addressing the current data analytics needs in adtech can be challenging. With billions of daily transactions, the sheer volume, velocity, and complexity of the data can easily overwhelm conventional business intelligence tools. While traditional BI tools such as dashboards and email alerts offer some support, in general, their capacity in the context of adtech analytics is severely limited. Among the most common problems are: Lack of data correlation – Traditional tools may show only one problem, like server latency, but will not show or correlate multiple issues in the same alert, for example, server latency and a dip in conversions due to time-out issues. This can make it difficult to uncover technical anomalies that can dramatically affect revenues. Alert fatigue – An overly sensitive monitoring solution can generate large volumes of alerts for even small incidents. The more alerts, the greater likelihood of false positives and the chance that staff will ignore alerts due to lack of time to investigate them all. Seasonality issues – Traditional BI tools based on thresholds don’t consider seasonal patterns in data and often end up capturing too many samples that are falsely identified as anomalies.  BI tools work from hindsight – It can take hours, days or even weeks to find issues and apply remediation when using traditional BI and monitoring tools, making them unsuitable for the fast pace of programmatic advertising. Minor undetected issues can cause major losses – Adtech companies can lose hundreds of thousands, if not millions, of dollars due to the passage of time between a business incident and its discovery. If undetected for too long, even minor issues can cause a detrimental disruption to service. AI in adtech delivers actionable insights Real-time analysis is the only way for adtech companies to determine whether key indicators are under or over performing. To ensure these companies always have their finger on the pulse of every consequential metric or data anomaly, executives, data scientists, and analysts are turning to real-time machine learning, artificial intelligence (AI/ML), and predictive analytics to help them identify and resolve issues immediately. With data accumulating at an exponential rate, it's simply impossible for data analysts to extract relevant and timely business insights without autonomous AI analytics. Adtech companies need a scalable, real-time BI and analytics solution like Anodot, which can handle any number of data variables, intelligently correlating related anomalies that may not be apparent to a human observer.  Best results are achieved with machine learning, which does not require any manual configuration, data selection, or threshold settings, along with algorithms that can handle complex data such as click rates, impressions, and bid duration for every combination of campaign, publisher, advertiser, and ad exchange. Anodot’s AI-powered business monitoring for adtech  Where traditional BI tools fail due to time delays, data constraints, and complexity, Anodot’s predictive analysis learns data patterns for a variety of KPIs and dimensions and delivers actionable insight through automated anomaly detection.  Anodot’s big data ML algorithms are specifically designed to detect outliers, preemptively identifying trends as well as issues before they become problems, and facilitating optimization and operational maintenance. In real time, Anodot can detect anomalous behavior, correlate multiple anomalies, and then alert the proper teams in order to get a fix in place. Anodot Helps Xandr Resolve Issues Quickly Xandr is a massive-scale marketplace that connects the demand side to the supply side in the advertising ecosystem. Ben John, CTO at Xandr, describes what his company went through in trying to solve their data monitoring challenges before engaging Anodot.  “We had to install these agents and run hundreds if not thousands of servers and applications across our global data centers. When a business-critical incident happened, people had to look at the logs, at some of the monitors, and at alerts. They would try to correlate it all to understand the business incident or business impact. That is really hard, and every minute we were losing revenue, and also our customers were losing revenue, so time is of the essence.” Xandr needed an automated solution that could scale to the company’s rigorous demands, and yet could detect anomalies happening for a single customer in a single region of a global business. They chose Anodot’s cloud-based solution to identify and resolve incidents before they can impact business. “We reduced the time to detection of root causes from up to a week to less than a day. The complexity of our platform makes manual detection incredibly difficult,” says John. “Before Anodot, it could take up to a week because our platform integrates with so many partners. Now, this data helps us find so many incidents within a few hours or within a day, compared to multiple days and weeks.” Anodot caught events that resulted in savings of thousands of dollars per event. “Each campaign going through the Xandr platform configures hundreds of thousands, if not millions of ads, and if things go wrong, it can have a significant financial impact. We were able to save lots of money for both Xandr and our customers,” according to John. Anodot helps keep the Magnite ad exchange working smoothly With 2.5 times more transactions than NASDAQ, Magnite (formerly Rubicon Project) is one of the largest ad exchanges in the world. More than 90% of people browsing the Internet will see an ad that goes through the Magnite exchange. Using this service, the world’s leading publishers and advertising applications can reach more than a billion consumers. With 13 trillion monthly bid requests, 55,000 CPUs, and 7 data centers, Magnite’s BI needs were well beyond the scope of what humans could monitor, analyze, and control. Magnite turned to Anodot to track their data in real time to aid in the creation of a fair and healthy ad marketplace. Anodot’s advanced machine learning-based BI and analytics solution allows the Magnite team to identify trends and correlations in real time. Recently, Magnite was able to instantly correlate a drop in one customer’s bidding activity to system time-outs. Magnite immediately contacted the client and alerted them. The customer identified a bug in a recent software release as the culprit for the time-outs and resolved it quickly to get back in the game. Magnite also benefited from the added ability to pull existing business intelligence solutions into the Anodot system. Magnite used an open source monitoring tool, so Anodot simply extracted data from the tool, allowing Magnite to streamline and automate data analytics.

Explore how this ride-share leader uses Anodot to identify business risks in real time

Immediately address data quality problems and save weeks of dealing with inaccurately reported data. AI Analytics and Anomaly Detection puts renewed trust in the quality of the data that directly impacts business priorities.

Fintech Metrics: Critical Transaction KPIs to Monitor In a previous post about payment transaction monitoring, we learned how AI-based payment monitoring can protect revenue and improve customer experience for merchants, acquirers and payment service providers. In this post, we’ll highlight the critical transaction metrics that should be monitored in order to achieve these goals. When most organizations think about ‘transaction metrics’ they probably think the KPIs are only relevant to BI or analytics teams. Measuring and monitoring payment metrics and other data doesn’t take priority in running the daily affairs of Fintech operations. Or does it? What if we told you that the opposite is true. If Fintech companies want to protect revenue, payment operations teams must be able to find and fix transaction issues as they’re happening. In an increasingly digitized and competitive environment, no one can afford to wait for periodic reports to provide the necessary insights to run and optimize their daily operations. It’s time for data to be approachable and understandable to all business units, and we’ll explain why in this post. Read on to discover how to improve transaction metrics monitoring to meet the challenges that lie ahead - or on your table right now. Using transaction data proactively Transaction processing metrics are significantly more complex to monitor than most digital metrics like web traffic or engagement. On top of the financial responsibility and risks, teams are dealing with heightened operational complexity. Just think how many steps are necessary for a single transaction on your site and how many parties are entangled. Many stages require verification, authentication, and approval. It’s never just a click. With so many intersections and points of friction, there’s a lot that can potentially go wrong. A glitch in any of the related algorithms, APIs, or other functionalities can cause chain reactions in a whole series of processes and immediately lead to reduced customer satisfaction and eventually to a loss of revenue. It also means there are many opportunities to optimize processes and increase efficiency. At each link in the chain, there’s something to improve. To make both possible - detect failures and opportunities - it’s critical to monitor the entire set of digital payment metrics. Currently, that’s in the hands of the BI or IT teams. Operational teams depend on standardized reports of historical data after it passes through the relevance filters of the data analysts. You may be missing specific transaction metrics that could provide a valuable understanding of how consumers behave or point towards weaknesses in the operational processes. You are definitely losing time when it comes to identifying failures. Why organizations need more granularity for payment metrics The amount of data and metrics to monitor has become overwhelming even for the dedicated business units. There are only so many dashboards a human being can observe. To remain efficient, they currently focus on critical business metrics and generalized data. Alert systems notify about irregularities based on manually set static thresholds, causing alert storms when there are natural fluctuations. Let’s imagine transaction performance metrics show a decrease, and the data you receive helps you identify a reduced payment approval rate. That’s still a pretty general observation that creates more questions than answers. A more granular view of the data, such as by location, vendor, payment method, device, and so on, could deliver insights that point you towards the cause. The same is true for optimization efforts. With a deeper level of granularity, companies can pinpoint weaknesses and strengths more precisely and act upon them with a higher chance of success. You can easily identify your highest-performing affiliates or discover the geographical locations you are most popular in. [CTA id="7a3befd2-2d16-4944-af32-574e0a11e90d"][/CTA] Revenue-critical KPIs to monitor Because there are so many metrics and dimensions to measure across the payment ecosystem, it’s important to focus on the most critical KPIs. Fintech operations teams should make sure they have accurate and timely insight into the following metrics: Payment approval - compare payment requests vs. payments approved. With Anodot you can identify discrepancies on the spot and reduce the time to identify and fix issues. Merchant behavior - measure the number of transactions, financial amounts, and more. Anodot lets you analyze merchant behavior and uncover ways to optimize marketing and business. Vendor routing - evaluate your payment providers. Anodot helps you focus your efforts on the strongest vendors. APIs - nothing goes without functioning APIs in fintech. With Anodot you can easily monitor the functionality and ensure smooth processes. Deposits and settlements - monitor the two layers for payment. Use Anodot to stay on top of the entire payment process and increase efficiency. Processing intervals - keep an eye on the time it takes for payments to go through. With Anodot you’ll know right away when there’s a delay somewhere in the system and can avoid customers being disappointed and abandoning your site. The benefits of real-time payment metrics The problem with the current method of analyzing transaction metrics analysis is that data is historical, too generalized, and not effectively prioritized. In other words, by the time the information reaches you, it already belongs to the past. Strictly speaking, decisions are based on outdated information. Real-time data enables you to see and react to what’s happening right now. That may not sound all that beneficial at first. Some people even find the thought of having to respond in real-time stressful. But monitoring real-time data doesn’t mean you sit around watching your data monitor like a flight supervisor. Back to the payment approval issue; The tool correlates out-of-the-ordinary data behavior and finds related incidents in real-time. Instead of you - or a data person - digging up possible related metrics and creating reports to see what caused the drop, the tool points you towards the cause and the solution. How AI makes data accessible to more business units Anodot’s AI-driven business monitoring tool learns normal data behavior patterns, taking seasonal changes and fluctuations into consideration to identify anomalies that impact business. Anodot monitors all your business data and learns the behavioral patterns of every single metric. The monitoring goes on even when you are not looking, distilling billions of data events into single relevant alerts. Anodot also correlates irregularities in transaction metrics with other data and notifies the relevant business units. This means, when you receive an alert, it contains maximum information to help you get to the bottom of what’s happening and how things are connected. Let’s say you detect a drop in deposits. Anodot correlates all related metrics and identifies that all activities with a specific vendor are down, so the failure is with that particular vendor. You are a huge step closer to the next phase of problem-solving. Anodot also prioritizes and scores the severity of an alert based on its financial impact. You only get notified about the metrics that are relevant and need immediate attention. Autonomous payment metrics monitoring for higher efficiency Only an AI/ML-based solution that autonomously monitors all metrics, correlating and prioritizing data, can ensure that each business units receive the insights they need when they need it. The days where data was the sole domain of a chosen few are over. In today’s digitalized business environments, data is everywhere and needs to be accessible to those who need it most. Monitoring data is part of a daily routine, just like keeping an eye on the fuel gauge in your car to know when you need to refill.

Companies produce, collect and manage massive amounts of data Recently in TechBullion, Anodot’s CEO, David Drai, addressed the question, ‘Why Every Company Needs DataOps’ With DevOps, IT was finally recognized as the strategic advantage that the business needed to beat the pants off their competition. Companies now deploy code tens, hundreds or even thousands of times per day, while still delivering unsurpassed stability, reliability and security. DevOps isn’t foolproof Drai expands by saying: “I can cite hundreds of major and expensive incidents that even DevOps couldn’t protect businesses from facing.” More and more, organizations have come to the realization that DevOps is just a part of the solution for maintaining reliable business performance. Where does DevOps Fall Short? While DevOps plays a key role in minimizing the friction between development and production, BI teams see a similar struggle with between backroom and front room developers. The challenge is in closing the gap between these two areas. Drai wrote, “Devops understands monitoring without a holistic understanding of the business and its granular data.  On the other end of the spectrum are BI and data teams that do have a nuanced understanding of business data, but are lacking in tools for around-the-clock monitoring and alerting to abnormal behavior of the data.” What is DataOps? Companies rely on data from a variety of different sources, helping them to gain a better understanding of customers, products, and markets. Explained Drai, “an entirely new role is needed: DataOps.  Because of the dynamic nature of data and the constant new services, partnerships, and products entering the market every quarter, the DataOps role is ongoing and should comprehensively understand and use the proper tools to monitor the ebb and flow of company data including business anomalies, trend changes, changes in predictions, etc.” Why not traditional BI role? How does DataOps differ? The skills gap will not be found in traditional BI strategies. The DataOps role will fill growing gap by working with data across the organization and uncovered a better ways to develop and deliver analytics. “As the focus of DataOps is to monitor and understand all company data, there is a strong existing link between this role and existing company roles like BI analysts and data engineers,” Drai emphasized, “Each role is unique enough to stand on its own, and all three should be reporting to a Chief Data Officer, a position that is becoming increasingly prevalent in data-driven companies.” Next Step See the full article on TechBullion: From DevOps to DataOps : Why Every Company Needs DataOps  

This new report from Blue Hill Research takes a closer look at how enterprises deploy DataOps models to establish the free flow of data within their organization. It includes real-world case studies which demonstrate how organizations in various industries from retail and ecommerce to education are leveraging new technologies to break down silos.