Although user experience is very important and issues around UX and application performance sometimes relay to revenue loss, not all revenue loss can be seen when exploring user performance and not all user performance issues affect revenue.

In today's payment ecosystem, the ability to monitor and use payment data effectively represents a real competitive advantage. Intelligent payment operations enables organizations to build a future-proof operations infrastructure. In a recent webinar hosted by Anodot, we talked to a panel of experts in payments operations to discuss how to leverage data to optimize payment processes. Experts from Thunes, Payoneer, 888 Holdings and Anodot joined in the roundtable. Liron Diamant, Anodot's Global Payment expert set the stage discussing today's environment in which payment data is becoming a commodity - a digital product. She said payment companies and financial institutions are realizing that smart operations aren't necessarily related to performance but also to the company's ability to learn and adapt using automation and complex data analysis. The panel started the webinar discussing the process of collecting data, specifically which data they find most useful in analyzing. Collecting useful data for payment operations Elie Bertha, Product Director at Thunes, said it's most useful to collect and monitor payment data that enables users to detect issues as fast as possible and communicate it properly. He also said it's important to link all data sources together for a 360 degree view of the business and the customer. Ari Kohn, the Risk Team Leader at Payoneer, said data that is managed and measured properly is the foundational layer of a successful payments business. He said Payoneer's approach to using data for analysis is constantly evolving. He says the company has multiple sources of data stored in multiple formats. His teams have to wrangle all of that to get a 360 degree view of what's going on in order to identify risk. . Anodot's Chief Data Scientist, Ira Cohen,  discussed what happens on the other side of data collection - machine learning. Ira agreed it's important to be notified as soon as possible when something is happening. He said the speed of incident detection has a lot to do with the volume and velocity of data. Cohen says the challenge in data collection that feeds into AI and machine learning is to understand what level of granularity to go by. Cohen says the two options of granularity are by time and space. For example, you can break down transactions by location - down to a particular user. You can also aggregate transactions in time as well - in windows of one minute, five minutes, one hour, etc. Cohen says a good monitoring system allows you to play with both of these attributes, but the dimensionality of the data and the timescale resolution of the data. [CTA id="7bfafd13-eb8f-4542-a736-1a6b27f79f68"][/CTA] Payment use cases  Elie Bertha from Thunes says one of the company's interesting use cases is to segment customers and compare them which helps detect anomalies from a business perspective. Amit Levy at 888 holdings says they strive for end-to-end monitoring that correlates technical issues with business KPIs such as revenue, and how they are related. Ari Kohn from Payoneer discussed use cases in risk management. He says different products carry different risks. For example, when Payoneer is issuing a debit card, the primary concern is fraud. In order to protect customers from card theft, they have to look for signals that indicate that kind of behavior. However, when issuing capital for a seller that needs an advance, they are worried more about delinquency. Kohn says both of those use cases rely heavily on the availability of data - data that is specific to the types of risk they monitoring. The panel also discussed how they prioritize payment incident alerts and how they democratize data across the company for self service analytics. You can watch the roundtable discussion in its entirety here.

Running machine learning anomaly detection on streaming data can play a significant role in your overall revenue. Here’s why.

After putting the initial tools and processes in place for a cloud management strategy, many organizations struggle to scale their FinOps to fit their growing cloud needs. To ensure that the scalability of cloud computing is actually boosting your company’s financial performance, delivering continuous insight and value from cloud investments is critical. Cyberark, an identity security company, uses Anodot's cloud cost management solution for achieving ongoing value and savings in their FinOps practice. In a recent Anodot webinar, Cyberark's FinOps expert, Uri Eliyahu, discussed solutions to creating an all-encompassing cloud culture and tips for driving organizational alignment around FinOps.   [CTA id="794d2af8-d992-4ed1-9bfb-c286e1d3e3c8"][/CTA]   Uri compares cloud computing to the game of chess. Sometimes, you don't know what you don't know but you can plan your moves ahead of time. Uri shared how Cyberark established cloud operations and a Cloud Center of Excellence using tools like Anodot to increase influence across the organization and reduce what is not known about cloud spend and usage. Cloud Operation Magic Triangle Traditionally, organizations relied on on-premise data centers which required a CapEx expenditure ahead to purchase hardware and software. With cloud computing, developers or engineers can spin up an instance in one click without oversight or approval from IT or Finance. To reduce this risk, Uri says companies need to provide tools and processes to make sure cloud engineers can do their work across the following domains: security, FinOps and operations. Uri says companies should have a vision for building a cloud operations culture, from the top down. Direct and Indirect Costs  It's important to take into consideration direct and indirect costs when budgeting for FinOps. For example, typical direct costs would include services like Amazon EC2, Amazon S3 and Amazon RDS. But according to Uri, direct costs account for only about 55% of total cloud spend. Indirect costs such as AWS KMS, AWS CloudTrail or data transfer must be considered as well. Using Anodot to Scale FinOps Anodot is the only FinOps platform built to measure and drive success in FinOps, giving you complete visibility into your KPIs and baselines, recommendations to help you control cloud waste and spend, and reporting to make sure you improve your cloud efficiency. Anodot  is built to offer cloud teams a contextual understanding of cloud costs and the impact of business decisions on cloud spend, helping companies achieve unit economics and understand how specific units and/or customers impact cloud metrics including cost, utilization and performance. From a single platform, Anodot provides complete, end-to-end visibility into your entire cloud infrastructure and related billing costs. By monitoring your cloud metrics together with your revenue and business metrics, Anodot enables cloud teams to understand the true cost of their SaaS customers and features. Anodot automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage anomalies, providing the full context of what is happening for the fastest time to resolution. With continuous monitoring and deep visibility, you gain the power to align FinOps, DevOps, and Finance teams and cut your cloud bill.

A jumping-off point for improving your business monitoring capabilities and the way you measure its effectiveness.

The on-demand infrastructure of the cloud has its benefits and challenges. While it allows flexibility and immediate availability, the rapid fluctuations of cloud use makes it difficult to forecast and budget. The goal of forecasting is to help businesses anticipate results and create budgets. It's typically based on a combination of historical spending and an evaluation of future infrastructure and application plans. Anodot's cloud and data science experts recently recently led a webinar discussing strategies for forecasting future multicloud spend across AWS, Azure, GCP and Kubernetes. 4 types of FinOps forecasting Ira Cohen, Anodot's Chief Data Scientist and Jeff Haines, Anodot's Director of Marketing explained that in order to help control spending, business should leverage four types of FinOps forecasting: Planning: long-term - Foresee the long term evolution of your cloud costs based on past usage and inputs about what might happen in the next year or two Budgeting: mid-term - Analyze budgets that were allocated to different teams or business units every few months or quarter to ensure they are on track Monitoring: short-term - Forecast through the next month looking at forecast vs. actual vs. budgeted, track progress and take action if over budget Insight generation for proactive FinOps - Forecasting to generate insights and cost saving recommendations [CTA id="7456a2b2-c05a-421c-96d0-0a4a44a6d249"][/CTA] Capabilities required for forecasting models Granularity - Forecasting for different clouds, services teams and products Accuracy - Use the forecast at any granularity to get accurate budgets Flexibility - Should be flexible enough to adapt to changes Forecasting cloud spend with Anodot Cohen and Haines discussed the example of of short term ML-powered forecasting with Anodot. In this graph, the teal line tracks the previous calendar month actual spend by day and the blue area is showing the actual current month spend in September. The filled blue area represents the actual spend.In this example, month to date costs were about $3.2 million dollars. The dotted orange line represents Anodot's AI-generated forecast for the remainder of the month which is estimated to be a little over $5 million. You’re able to configure budgets for business objects like linked accounts, services, teams, and projects. This overview shows current versus budgeted consumption for each budget, as well as forecasted versus budgeted consumption. You can set budgets monthly, monthly through the quarter, and monthly for the next calendar or fiscal year. In addition to forecasting capabilities, Anodot also provides end-to-end visibility into an organization's entire cloud infrastructure and related billing costs. By monitoring cloud metrics together with revenue and business metrics, Anodot enables cloud teams to understand the true cost of their cloud resources, with benefits such as: Deep visibility and insights - Report on and allocate 100% of your multicloud costs and deliver relevant reporting for each persona in your FinOps organization. Easy-to-action savings recommendations - Reduce waste and maximize utilization with 40+ savings recommendations personalized to your business Immediate value - You'll know how much you can immediately save from day one and rely on pre-configured, customized reports to begin eliminating waste. With Anodot's continuous monitoring and deep visibility, engineers gain the power to eliminate unpredictable spending. Anodot automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage anomalies, providing the full context of what is happening for the fastest time to resolution.

Traditional Analytics Tools for eCommerce can’t include Each and Every Metric Number of sessions, total sales, number of transactions, competitor pricing, clicks by search query, cart abandonment rate, total cart value…the analytics tools commonly used by eCommerce companies for performance monitoring can’t include every metric, and even if they did the analysts using them wouldn’t be able to keep up with the amounts of changing data. This of course, inevitably leads to overlooked business incidents and lost revenue whenever these tools are used in the fast-paced world of eCommerce. In eCommerce, minutes matter. Your infrastructure and your competitors’ ad bidding strategies can change in an instant. Any metric can signal an important business incident. When these tools are the foundation of your performance monitoring and business, incident detection doesn’t occur anywhere near the speed of business, so your analysts can spend less time analyzing and more time head-scratching. The need to go granular with performance monitoring Traditional analytics tools like KPI dashboards and lists fall flat on their face when it comes to performance monitoring in the fast-paced, multi-faceted world of eCommerce. These tools take a high-level approach that tries to simplify the complex through generalization, causing BI teams to overlook plenty of metrics for eCommerce analytics. This is a design flaw since even though those tools may automate reporting and visualization, they still require humans to manually monitor the visualized data and spot the anomalies which point to business incidents. Many interesting things can happen in the metrics you’re not monitoring, leading you to miss things completely or discover them too late after the financial and reputation damage is already done. Also, missing just one of a metric’s many dimensions can cause you to miss significant business incidents. Think of metrics as the general kind of quantity and dimensions as the specific slices of that data (e.g. daily sales per brand, daily sales per browser). In effect, monitoring each dimension multiplies the number of metrics that could be monitored, easily resulting in far too many ecommerce analytics metrics for a single person, or even a team, to constantly monitor. A performance monitoring horror story To illustrate why etailers need to take this granular approach to performance monitoring, consider an eCommerce company that sells physical goods in the US. Like many online retailers, this one accepts a wide variety of payment options, from PayPal and credit cards to e-wallets like Google Wallet and Apple Pay. The etailer’s BI team notices on their dashboard that the total daily revenue dropped very slightly. The almost imperceptible dip in this high-level KPI gets passed over by the analysts because they have about five other dashboards to monitor anyway, so they attribute it to statistical noise. Meanwhile, a crucial payment processor has changed their API, breaking the etailer’s ability to process orders made with American Express cards, resulting in those customers abandoning their carts. Since orders with AMEX cards make up such a small portion of the total order volume for this merchant, the total daily revenue barely budges, glossing over the frustration of those AMEX cardholders. Had this company been monitoring daily revenue, not as a single KPI, but broken out across each payment option (daily revenue from AMEX orders, daily revenue from Apple Pay orders, etc.), the sudden drastic drop in successful AMEX orders would have been obvious. Even if this team was using a reasonable static threshold on this metric (an approach which doesn’t scale, as we’ve discussed before), they would have been alerted and the team could contact the payment provider to fix their broken API or implement a workaround in their own code. Problems like these, which impact a small subset of your target market or existing customer base occur quite often in eCommerce, and can paralyze a company’s growth. And what if the company in our hypothetical scenario had just launched a line of premium smartphone accessories for international business travelers – the exact demographic most likely to shop with an American Express card? Good luck recovering from that misstep. The value of real-time monitoring of every eCommerce metric With every passing day that the problem goes undetected, lost revenue piles up and this merchant’s success in breaking into that wealthier clientele is less and less likely. Missed problems lurking in overlooked eCommerce analytics metrics can stop growth in its tracks. The only performance monitoring solution which is adequate for eCommerce is one that can monitor all the dimensions of a given metric in real-time. By missing the crucial business incidents that can make or break eCommerce success, analytics tools that overlook many vertical-specific metrics imperil the merchants who use them. As we’ll see in the next article of this series, this is just as true in fintech as it is in eCommerce.

Optimizing Kubernetes Costs   Gartner predicts by 2022, more than 75% of global organizations will be running containerized applications in production, a huge jump from the mere 30% in 2019. Kubernetes remains the most popular container orchestration in the cloud. According to the Cloud Native Computing Foundation (CNCF) 96% of organizations are already using or evaluating Kubernetes in 2022 Kubernetes has crossed the adoption chasm to become a mainstream global technology With more organizations adopting Kubernetes, the reality is setting in that there is tremendous potential cost impact due to lack of visibility into the cost of operating Kubernetes in the cloud. According to CNCF, inefficient or nonexistent Kubernetes cost monitoring is causing overspend. Cloud experts at Anodot and Komodor recently hosted a webinar to discuss the challenges of optimizing cloud costs and how to empower teams to control Kubernetes costs and health. [CTA id="03a6f09d-945f-4144-863f-39866f305afb"][/CTA] The rise of FinOps Historically, engineers and architects did not have to worry too much about operational costs. Now, engineers are on the hook for the financial impact of:  Code resource utilization Node selections Pod and container configurations Meanwhile, finance has dealing with the transition from the CapEx world of on-premises IT to OpEx-driven cloud as well as comprehending cloud cost drivers and the complexity of the cloud bill.  That's why more organizations have cross-functional Kubernetes value realization team, often called FinOps or Cloud Center of Excellence. The goal of this team is to strategically bring engineering and finance together and remove barriers to maximizing the revenue return on your business’ investment in Kubernetes. Visibility into Kubernetes is critical Getting control of Kubernetes costs depends primarily on gaining better visibility. CNCF combines all aspects of visibility together with monitoring, but, when asked what level of Kubernetes monitoring they have in place: Nearly 45% of industry respondents were simply estimating costs Almost 25% had no cost monitoring in place  With 75% of organizations running Kubernetes workloads in production, now is the time to eliminate cloud cost blindspots by understanding K8s cost drivers. Kubernetes cost drivers In order to build better visibility, organizations need to understand the seven primary Kubernetes cost drivers: Underlying nodes Pod CPU/memory requests/limits Persistent volumes K8s scheduler Data transfer  Networking  App architecture In the webinar, experts outline specific strategies that will empower your team gain visibility into and optimize each of the Kubernetes cost drivers. Anodot for Kubernetes cost optimization To enable Finops that covers  all of  Kubernetes, enterprise organizations are choosing Anodot for continuous visibility into K8s costs and drivers so you can understand what elements are contributing to your costs and tie them to your business objectives. With Anodot, you can visualize your entire Kubernetes and multicloud infrastructures from macro, infrastructure-wide views, all the way down to the specifics of each container. Anodot empowers finance teams to allocate and track every dollar of spend to business objects and owners, revealing where costs are originating. We help you monitor your cloud spend so can respond to anomalous activity immediately and are never surprised by your cloud bill.   Our team of scientists has delivered AI-powered cost forecasting that helps you accurately predict costs and negotiate enterprise discounts. With Anodot, you'll realize a culture of FinOps that solves the Kubernetes cost visibility problem.