Cloud cost anomalies are unpredicted variations (typically increases) in cloud spending that are larger than expected based on historical patterns.  Misconfiguration, unused resources, malicious activity or overambitious projects are some of the reasons for unexpected anomalies in cloud costs. Even the smallest of incidents can add up over time leading to cost overruns and bill shock. As cloud billing data is collected and reviewed periodically, it is often difficult for FinOps teams to detect anomalies related to cloud costs in real or near time.  According to the State of FinOps 2022 report, 53% of organizations indicated that it takes days for their FinOps teams to respond to cost anomalies. It is probably because only 25% of companies have implemented automated workflows to manage anomalies.  Measuring cloud cost anomalies Anomaly management is composed of three distinct phases that should be measured separately: Time to detection (occurrence to discovery/acknowledgement)  Time to root cause (time of investigation) Time to resolution (total duration of the anomaly) Additionally, you should also measure the count of anomalies within a given period (e.g., day, week or month). Automated, machine learning–based anomaly detection systems, such as Anodot, allow the FinOps team to react quickly to avoid unexpected costs.  [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Managing cloud anomalies with Anodot Anodot's fully automated AI detects anomalies in near real time and alerts the appropriate teams only when risk is meaningful, enabling quick response and resolution. 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. Anodot seamlessly combines all of your cloud spend into a single platform. Monitor and optimize your cloud costs and resource utilization across AWS, GCP, and Azure. Deep dive into your data details and get a clear picture of how your infrastructure and economies are changing. With Anodot, you can: Get complete visibility into AWS, Azure, GCP and Kubernetes costs: Understand, divide, track and attribute every dollar spent in context Easily customize reports and dashboards for FinOps stakeholders Manage Kubernetes spending and usage from the same view as your multicloud services Take action and continuously reduce your cloud costs: Pursue the most pertinent cost reduction opportunities with 40+ types of cost savings recommendations CLI and console instructions provided alongside each insight to enable engineers to take action Purchase services efficiently with analysis and customized recommendations Plan your usage effectively and prevent bill shock: Machine-learning based forecasting accurately predicts spend and usage, empowering you to anticipate changing conditions and deliver Detect, alert on, and resolve irregular spending and usage anomalies in near real time Assess enriched Anodot data via our powerful API and leverage within your other tools With Anodot, FinOps practitioners can continuously optimize their cloud investments to drive strategic business initiatives.

Companies are investing heavily in the cloud for the operational and financial benefits. But without a robust cloud cost management strategy in place, the complexity of cloud services and billing can to overspending and unnecessary cloud waste. Being able to accurately predict future cloud spend is one way to more optimize cloud spend and inform budgets. Ideally, finance, engineering and executive leadership agree upon and build allocation and forecast models from which to establish budgets that align with business goals. Once a strategy is in place, cloud cost forecasting accuracy is an important KPI to measure in order to understand cloud efficiency and FinOps success. Measuring forecast accuracy  To measure your forecasting accuracy, you’ll need to calculate the variance between your forecast and actual costs. Once the forecasted spend variance (%) is calculated, it can be compared against the FinOps Community of Practitioners' recommended thresholds: For FinOps practices operating at Crawl maturity, variance from actual spend cannot exceed 20% Variance of 15% for a FinOps practice operating at Walk maturity Variations of 12% for FinOps practices operating at Run maturity [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] In the State of FinOps 2022 report, Run organizations reported 5% variance; Walk organizations reported 10% variance; and Crawl organizations reported 20% variance — a testament to the value of a growing FinOps practice. Accurate cloud spend forecasts require robust FinOps capabilities across the board, including complete multi cloud visibility and the ability to fully categorize and allocate cloud costs. Forecasting cloud costs with Anodot  Anodot’s AI-powered solution analyzes historical data in order to accurately forecast cloud spend and usage by unit of choice, anticipate changing conditions, and get a better read on related costs. This helps organizations to make more informed budgeting decisions and find the right balance between CapEx and OpEx. From a single platform, Anodot provides complete, 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 (with K8 insight down to the pod level) and deliver relevant, customized reporting for each persona in your FinOps organization Easy-to-action savings recommendations - Reduce waste and maximize utilization with 40+ savings recommendations highly personalized to your business and infrastructure Continuous cost monitoring and control - Adaptive, AI-powered forecasting, budgeting and anomaly detection empower you to manage cloud spend with a high degree of accuracy and relevance Immediate value - You'll know how much you can immediately save from day one and rely on pre-configured, customized reports and forecasts to begin eliminating waste  

One of the key advantages of cloud services versus on premise deployments is the wide range of purchasing options and pricing models. While it's an attractive advantage, it can be complicated for organizations to determine the best blend of service pricing models. The ability to define the organization's blend of purchasing strategies and display the target versus actual performance is critical for optimizing cloud cost management efforts. There are several KPIs to help you measure FinOps efficiency and ensure you use the commitment capabilities offered by your cloud provider. Cloud pricing models All major cloud providers offer several pricing models: On-Demand: pay for capacity by the hour or the second depending on which instances you run. No longer-term commitments or upfront payments are needed. Reserved Instances (RIs), Committed Use Discounts (CUDs), and Savings Plans (SPs): flexible pricing models that offer discounts if you commit (based on resource usage or spend) over a fixed period. Spot Instances: purchase spare AWS capacity for up to 90% off the On-Demand price. The downside is that you receive a 2-minute notification that AWS can take the server back and you’ll need to set up a new one. On-demand coverage  Using On-Demand is the most expensive option. One of the metrics that users should measure is the percentage of coverage of On-Demand for each service and determine what can be switched to a different method (Spot / Commitment). Using Spot or workloads that can handle a 2-minute notice before an interruption is a great way to optimize your savings. Generally, they should be stateless applications. It is very common to use Spot on test and development environments. With stable workloads, the preferred method is to buy a commitment that will help you maximize your discount without any interruptions to your workloads. Commitment utilization When it comes to commitments, we want to make sure they're all being used. This is what is called commitment utilization. In this KPI, 2 things are being measured: How much is saved due to the commitments vs. how much waste there is and the ratio between them? The specific percentage of utilization When considering a commitment, you want to give yourself the most coverage while maintaining elasticity — start with no more than 60%, and as you understand your usage, you can increase it. Both of these indicators help make sure that not only are you achieving great coverage but that you are also using your commitment and not wasting money, as the target here is to achieve great coverage. These two KPIs are the first steps in measuring the efficiency of your cloud as well as making sure that you are using the commitment capabilities that the cloud provider offers.  [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Optimize Purchasing with Anodot  Anodot helps companies make smart committed use purchases using personalized recommendations base on historical and forecasted usage. Anodot generates savings recommendations by analyzing all incurred usage eligible to be covered by a SP/RI, and uses proprietary algorithms to simulate possible combinations of RIs that would cover that usage. Anodot helps FinOps teams prioritize recommendations based on impact, and it keeps a complete history of recommendations and actions taken. You can visualize the impact of every change before and after it has been implemented. You can tune recommendations based on organizational preferences, snooze them, or reject them outright. Anodot monitors your cloud metrics together with your revenue and business metrics so you can understand the true unit economics of your SaaS customers, features, engineering teams and more. With Anodot, FinOps practitioners can continuously optimize their cloud investments and drive strategic business initiatives.

Cloud spend — which research shows makes up 51% of IT budgets — is a prime candidate for company cost savings initiatives with the potential to make a huge difference in gross margins.  It’s also an area that has grown dramatically in the last few years due to digital transformation and a rise in cloud demand during the pandemic. The question is, where should finance and IT leaders focus their cost cutting and cloud cost management measures without impacting business operations, performance and availability? An obvious place for companies to start is analyzing and reducing cloud waste. Why? Surveys consistently show an average of 30% of cloud spend is wasted. Cloud providers charge for services provisioned, even if they’re not used. Overprovisioned, unused, and orphaned cloud resources are common in many organizations. Further, On-Demand rates for resources are significantly higher than commitment-based discount rates.  Sources of Cloud Waste Percentage of waste is based on open savings opportunities to optimize cloud billing. To measure this indicator, you have to first define what you consider waste. Waste can be roughly divided into three types: Idle and unattached resources Resources that can be right-sized Commitment purchase opportunity Measuring Cloud Waste Cloud waste is an important KPI for measuring cloud efficiency and FinOps success. The percentage waste KPI is built on a daily/weekly/monthly scan of cloud usage and then finding usage reduction options. Using one of these three groups, you calculate what percentage of the total use is inefficient: Cost of unused cloud resources against total cost (%) Cost of over-provisioned resources against total cost (%) Percent of infrastructure running on-demand vs. covered by discount or Spot, measured by hours (%) Some common examples include the percentage of EBS Unattached out of the entire EBS costs, or the percentage of Old Snapshot out of the entire Snapshot costs, etc.  [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Reduce Cloud Waste with Anodot Systems like Anodot can scan for dozens cloud cost optimization opportunities automatically and calculate a sustained waste percentage. As a result, you can measure whether the overall waste of all cloud costs has been reduced, or if a specific type of waste has been reduced.  With Anodot, you can bring finance, devops, and business stakeholders together to collaboratively control and reduce spend across all your cloud infrastructure. Continuously eliminate waste with easy-to-action savings recommendations that can drive significant savings. See cost causation and allocate spend by service, business unit, team, and app with deep visibility, granular detail, and reporting across AWS, GCP, and pod level Kubernetes. Avoid bill shock and enable FinOps with near real-time anomaly detection and alerts and insightful, machine learning powered cloud cost forecasting. Anodot monitors your cloud metrics together with your revenue and business metrics — so you can understand your true unit economics (revenue, cost, or margin) of your SaaS customers, features, engineering teams, and more. Anodot automatically learns each service usage pattern and alerts relevant teams to irregular spend and usage anomalies, providing the full context of what is happening for the fastest time to resolution. 

Cloud adoption has been on an upward trajectory for over a decade with no signs of slowing down. As widescale migration becomes the norm, organizations are realizing cloud financial management — also referred to as FinOps — is critical to creating long term value in the cloud. Building a culture of financial discipline requires visibility and a strategy for measuring success along the way. Several important KPIs should be measured to understand the effectiveness of your FinOps efforts, including unit metrics. Unit metrics allows businesses to measure the effectiveness of cloud cost management efforts and to plan and predict budgets by making delivery teams financially accountable to business drivers. By using unit economics, FinOps teams can correlate cloud spending growth to overall business growth, surfacing problems when cloud spending grows too quickly. Unit economics explained   A business model’s unit economics describes its revenues and costs in relation to one unit — such as a customer served or unit sold. To understand costs, you must first identify the resources required per customer. A resource is any cloud service that has a direct or shared cost associated with it. If you have dozens of customers on shared resources like databases, storage, and microservices, you would need to model the resources into smaller pieces and understand the time (CPU) and/or memory a specific customer is consuming. Measuring unit costs   According to the FinOps Community of Practitioners: At the Crawl maturity level, FinOps practices measure cloud spend for a particular application against total revenue (e.g., cloud spend as a percentage of revenue). The Walk maturity level requires tying outputs to a unit of activity (e.g., API call cost). The Run maturity level requires measuring how much it costs to do a revenue-generating activity (e.g., cost of a transaction or customer). Unit costs are not a priority for most organizations since they are difficult to understand and measure. According to the State of FinOps 2022 report, only one in four organizations use unit costs, even though organizations operating at the Run maturity level report using them to effectively execute decisions, promote FinOps culture, and drive their FinOps success. Unit costs will never be completely accurate. The cost of these services can, however, be estimated intelligently using several methods of varying accuracy. [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Unit economics with Anodot   Anodot's business monitoring platform analyzes metrics and identifies anomalies. Therefore, metrics are our main KPI and unit of measure. The simplest way for us to measure the costs of our customers is to count the total metrics for all of our customers, then divide the cost of all of our cloud costs and divide it by the total unit metrics. With Anodot’s Business Mapping feature, you can accurately map multi cloud and Kubernetes spending data, assign shared costs equitably, and report cloud spend to drive FinOps collaboration for your organization. Mappings are built from one or more rules that allocate spend to a business dimension using sophisticated and nested evaluation criteria. With each additional mapping, the remaining pool of cloud costs is further divided without overlap. Anodot helps you understand your cloud unit economics by aligning your cloud costs to key business dimensions. Allowing you to track and report on unit costs and get a clear picture of how your infrastructure and economies are changing.  

Achieving cost savings is one of the main drivers for cloud adoption. But for most companies, controlling cloud spend is much more challenging than anticipated. In a recent survey, 94% of IT decision makers report they are overspending in the cloud. Our own survey on cloud costs revealed 90% of executives say better cloud cost management and cost reduction is a top priority. To achieve better cloud financial management, the practice of FinOps — often assigned to a multidisciplinary and cross-functional group — is emerging as the standard for understanding and optimizing cloud computing costs and resources. To ensure FinOps success, organizations should include a practice of benchmarking and measurement to ensure improvement in cloud management. Cloud Instance Costs One of the KPIs most critical for measuring cloud efficiency is hourly cloud service costs. The hourly cost of an instance is affected by a lot of different factors, such as its type, size, and payment plan. An average hourly cost is a way to normalize cloud service costs across projects, teams, use cases and billing methods. This indicator can be used for any service whose cost can be measured in hours or seconds, but users generally measure some or all of these five services: EC2 RDS Redshift Open Search ElasticCache The average hourly cost KPI is mainly affected by the following: Machine size — The smaller the machine you use, the lower the hourly cost will be. Sometimes a powerful machine is necessary, making improvements irrelevant. But often, you can save money and reduce the average hourly computing cost by choosing a less powerful machine that is appropriate for your use case. Form of payment — There are five ways to pay for AWS instances: On-Demand, Savings Plans, Reserved Instances, and Spot Instances. With a cheaper payment option, like Spot Instances (up to 90% off On-Demand prices) or machines covered by a commitment agreement, the hourly cost decreases, improving this metric. A reduction in average hourly cost is indicative of improved cloud efficiency since it considers multiple parameters. [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Anodot for Cloud Cost Management It is possible to overcome visibility and cost challenges by using native cloud service provider tools, building in house solutions, or purchasing FinOps tools, such as Anodot. Anodot is the only platform built to measure and drive success in FinOps, giving teams visibility into KPIs and baselines and recommendations to help control spend. Anodot's 40+ savings recommendations help teams continually eliminate waste and drive savings by optimizing how you purchase AWS, Azure and GCP. Enable FinOps with Anodot Visualize — Understand costs and drive ownership Track, divide and attribute every dollar spent in context with its business role or KPI, team, shared service and application Customize reports and dashboards for each FinOps stakeholder Manager Kubernetes spending and usage from the save view as your multicloud services Optimize — Cut costs with easy-to-action savings recommendations Pursue savings opportunities with personalized cost reduction recommendations CLI and console instructions provided alongside each insight enable engineers to take action fast Purchase services efficiently with analysis and customized recommendations for commitment and discount vehicles Monitor — Operate your clouds efficiently and avoid billing surprises Machine learning-based forecasting accurately predicts spend and usage Detect and resolve irregular spending and usage anomalies Access enriched Anodot data via our powerful API and leverage it within your other tools Anodot's cost allocation feature helps organizations accurately map multicloud and Kubernetes spending data, assign shared costs equitably, and drive FinOps collaboration throughout your organization. Inform and empower business decisions at all levels with Anodot's cloud cost management solution.

Cloud services are the number one source of unexpected overspending for companies today. As a result, cloud financial management is a major focus for most organizations. But how do you track the success of cloud efficiency? Full allocation of multicloud costs is a critical component for understanding your actual cloud services usage, establishing cloud cost management ownership, and creating accurate budgets and forecasts at the line of business, project, application and even team levels. According to the FinOps Community of Practitioners, at least 80% of cloud spend needs to be allocated for a FinOps practice operating at a Crawl maturity level, and 90% for a FinOps practice operating at a Run maturity level. The State of FinOps 2022 report revealed that, on average, only 75% of cloud spending is allocatable, with only 14% of organizations currently achieving 90% cost allocation. Cost allocation challenges Shared costs — Identifying and equitably allocating these costs becomes more challenging as businesses and cloud usage grow, and accurately splitting shared costs impacts ownership strategies like showback and chargeback, as well as budgeting, and forecasting. Tag compliance — To achieve comprehensive cost allocation, it is necessary to enforce tagging and to have retroactive spend tagging capabilities in place. Overall Tagging Compliance must be at least 90%. The ability to map tagged data to financial reporting is a challenge for many organizations. Unallocated spend — Organizations must be able to surface the percentage of cost that cannot be categorized and allocated directly. These costs must be investigated at a granular level to determine if they can either be budgeted directly as a shared cost or can be divided by agreed-upon factors. Methods of spend accountability   Once costs are categorized and allocated, cloud spend reporting and accountability is generally handled through one of two methodologies: Showback — Showback provides full cost transparency and helps drive accountability by providing visibility into spending but keeps the expenses in a centralized budget. Chargeback — Chargeback takes it one step further by sending expenses to a product or department P&L. The ability to allocate spend is what determines FinOps maturity, not whether a company uses a showback or chargeback strategy. According to the FinOps Foundation's State of FinOps 2022 report, 64% of organizations report costs through showback, while 48% report costs through chargeback. 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 waste and spend, and reporting to make sure you improve your cloud efficiency. Assessing your cloud cost allocation strategy   Assessing your allocation strategy is a key step in a successful FinOps journey. Consider the following questions as a starting point: What percentage of your costs do you allocate today? How do you allocate the cost of shared services? Can you map your cloud costs to business units, cost centers, applications, and projects? Is your cloud spending aligned with your business needs? Cloud cost allocation with Anodot   Anodot helps businesses understand cloud unit economics by aligning cloud costs to key business dimensions. This allows users to track and report on unit costs and get a clear picture of how infrastructures and economies are changing.   Anodot's cost allocation feature helps organizations accurately map multicloud and Kubernetes spending data, assign shared costs equitably, and drive FinOps collaboration throughout your organization. With Anodot, FinOps teams can easily classify and divide all of their cloud costs by business structures like apps, teams, and lines of business. Inform and empower business decisions at all levels from Anodot's cloud cost management solution.

Cloud migration efforts continue to grow today as organizations move into a post-pandemic work environment. According to McKinsey & Company, by 2024, most enterprises aspire to have $8 out of every $10 for IT hosting go toward the cloud.   In a survey by Morgan Stanley, CIOs say cloud computing will see the highest rate of IT spending growth in 2022.   Cloud cost complexity is growing  The rapid shift to the cloud combined with the complexity of navigating cloud usage and costs has companies struggling to control cloud spend and reduce waste. The problem is compounded by a souring economy, uncertainty of a recession, and the need for companies to cut costs and shore up revenue growth numbers. Some of the challenging aspects of managing cloud infrastructure include monitoring costs, optimizing resource use, and forecasting future spend. The very nature of cloud computing – and indeed, a reason that companies flock to it – is that compute capabilities can rapidly adjust to accommodate current business demands. Cloud storage capacity and databases fluctuate as well, contributing to the complexity of cloud providers’ billing processes.  Cloud cost visibility crisis    Companies are facing an urgent cloud cost visibility crisis with real implications for the bottom line. As cloud spending becomes  one  of the most expensive resources for a growing number of organizations,   IT, Finance and C-Suite leaders are prioritizing strategies and tools to optimize spend, reduce cloud waste and centralize cloud cost management.  The Anodot 2022 State of Cloud Cost survey, conducted over a two-week span in June and July 2022, helps us understand how organizations of varying sizes and vertical industries across the United States are coping with the challenges of gaining control over cloud costs.  We found the following insights to be most interesting: Nearly 50% of IT executives say it’s difficult to get cloud costs under control.  Respondents say gaining visibility into cloud usage and costs is the top challenge for controlling spend and reducing waste.  88% of those surveyed say optimizing and reducing spend on existing cloud resources is extremely or very important.  Despite these challenges, 60% say migrating more workloads to the cloud is their top cloud initiative in the coming year, making it more important than ever to leverage next generation cloud cost management solutions to understand costs, optimize spend, and reduce waste.    SMBs and enterprises face similar challenges   Anodot surveyed 131 high level information technology executives who have direct line of sight into how much their organizations spend on cloud computing each year. Approximately half the respondents represent SMBs and the other half come from enterprise organizations. As for annual cloud spend, about half of the companies are under a million dollars while the rest spend over a million, with 15% spending more than $5 million each year. Even with this wide range of company size and cloud budgets among the respondents, many agree they face the same challenges: difficulty gaining true visibility into cloud costs, accelerating migration of workloads to the cloud, expectations to spend up to 30% more in 2023, excessive time to notice spikes in cloud costs when they occur, and complex cloud pricing models. These issues seem to be universal regardless of company size.  Cloud visibility is the biggest challenge to controlling costs  The majority (53%) of survey respondents say their biggest challenge to controlling costs is in gaining true visibility into their cloud usage and associated costs. This is followed closely by the complexity of cloud pricing (50%) and the use of complex, multi-cloud environments (49%). In fact, these three issues are closely related. It’s hard to gain visibility into every aspect of cloud usage with an associated cost when the total environment in use is quite complex or even stretched across multiple providers’ platforms. For companies with a multi-cloud strategy, managing and optimizing  costs across providers is nearly impossible due to proprietary and dissimilar pricing models. This challenge of gaining visibility to control cloud costs has given rise to “FinOps,” defined as “an evolving cloud financial management discipline and cultural practice that enables organizations to get maximum business value by helping engineering, finance, technology, and business teams to collaborate on data-driven spending decisions.”  However, even companies that have invested in FinOps still feel they lack the granular visibility needed for optimized cost management. Consider that cloud spend often occurs in siloes spread among different internal teams and with multiple third-party cloud providers, causing a communication and budgeting gap that requires the use of multiple tools to resolve.  Moreover, cloud resource payment schemes often use a complex hierarchy and tagging system to monitor and track cost drivers via dashboards and reports. These systems are often inconsistent within customer accounts and across cloud service providers, making it difficult to reconcile costs. Bring your cloud costs under control with Anodot   Organizations continue to move workloads to the cloud to support their digital transformations and to gain the opportunities for innovation that only the cloud can provide. Even as they do so, half of IT leaders say it’s difficult to get cloud costs under control.  Cloud pricing models are complex and hard to understand. It’s a challenge to gain true visibility into cloud usage and costs, especially as companies invest more heavily in containers and multi-cloud environments. This dearth of visibility makes cost forecasting more difficult. As a result, monthly invoices can sometimes deliver surprises over unexpected costs. Anodot’s cloud cost management solution provides complete, 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, utilization, and performance of their cloud services. With continuous monitoring and deep visibility, businesses gain the power to align FinOps, DevOps, and Finance teams and reduce their total cloud bill.   Eliminate waste -  Anodot’s easy-to-action savings recommendations enable your DevOps team to easily implement spending and service changes that can drive significant savings.  Cost Allocation - See cost causation and allocate spend by service, business unit, team, and app with deep visibility across AWS, Azure, GCP, and pod-level Kubernetes.  Enable FinOps - Avoid bill shock with near real-time alerts and insightful, machine-learning driven forecasting.  Anodot also provides granular insights into Kubernetes that no other cloud optimization platform offers. Businesses can easily track spending and usage across clusters with detailed reports and dashboards. Anodot for Cloud Costs’ powerful algorithms and multi-dimensional filters enable a deep dive into performance and identify under-utilization at the node level.  Anodot’s artificial intelligence-powered forecasting leverages deep learning to automatically optimize cloud cost forecasts, enabling businesses to anticipate changing conditions and usage and get a better read on related costs.  Smart teams use Anodot to avoid daily, weekly, and monthly surprises in their cloud bill. Contact us to learn what autonomous cloud cost monitoring can do for you.

In today’s payments ecosystem, the ability to monitor and use payment data effectively represents a real and essential competitive advantage. Intelligent operations should be a strategic goal for the entire company, and when executed properly, will enable you to build a future-proof payment operations infrastructure.  With the increased proliferation of AI technologies, the payment operations space has been fundamentally changed, and the traditional legacy BI approach relying on dashboards and static thresholds is no longer adequate. AI and ML are critical to accelerating revenue, improving operational efficiency, and providing better customer experiences. Global payments leaders are increasingly relying on AI to monitor and optimize their payment operations resulting in lower costs, higher approval rates, and fewer declined transactions.  In this White Paper: The Journey to Intelligent Payment Operations we define five levels of payment operations maturity, with Level 1 being the least mature and Level 5 employing the most advanced practices: As we advance through levels, we consider 3 key characteristics: monitoring, operations and data; define the key metrics and dimensions we measure; and offer suggestions for how to level up. Additionally, we asked our Chief Data Officer, Dr. Ira Cohen, to share his insights on improving monitoring effectiveness no matter your maturity level or platform. Level 1 : Responders — Analytics and BI At this level, organizations are highly reactive. They operate based on dashboards and static alerts, relying on non-standardized and fragmented manual processes using email and spreadsheets — neglecting advanced tools and leading practices that drive operational performance and resilience.  Level 1 companies are focused only on the most basic aspects of monitoring — using dashboards that are monitored visually and alerts based on manual static thresholds that result in alert noise, false positives and false negatives.  Tips from our CDO When integrating to a vendor - Implement all APIs provided by them — including those not relevant to initiating transactions. Establish your KPIs in simple bullet points (e.g., increase payment acceptance) and understand your most influential dimensions. Eliminate noise by grouping values of dimensions that may affect the results but do not affect the business Measure the impact! Make sure you're monitoring business vs. technical stuff — you might see a glitch, but the impact is minimal. Understand your seasonality and incorporate special events into your monitoring systems Level 2 : Guardians — Automated Anomaly Detection Level 2 organizations have developed more proactive and collaborative processes, incorporating AI- and especially Machine Learning-based technologies to drive operational speed and agility. They implement a robust anomaly detection system which serves as the gatekeeper of their business and the frontline protector of their customers and revenue.  AI-based anomaly detection provides financial institutions with the capabilities needed to detect issues early and take preemptive actions before they turn into crises. They do so by automatically learning the data’s normal behavior, including seasonal and other complex patterns, to identify and alert stakeholders on any combination of metrics that behave abnormally.  Operations teams in Level 2 companies can send any data stream for monitoring, which will be continually analyzed for anomalies. Instead of looking at dashboards, they monitor for anomalies and use AI-based scoring to triage and resolve issues according to their severity and impact. Tip from our CDO Avoid false positives in KPIs that are ratios (e.g., payment success rate) by augmenting with logic that also looks at the volume behind the ratio (e.g., # of payment attempts). Not doing so will alert the team frequently at night, when payment volumes are low and the ratio fluctuates. Level 3 : Analysts — Automated Correlation  Correlation is a marquee trait for Level 3 organizations. They have moved beyond basic single metric/stream anomaly detection and have a comprehensive view of their business and incident impact with event and anomaly correlations.  Applying anomaly detection on all metrics and surfacing correlated anomalous metrics helps draw relationships that not only reduce time to detection (TTD) but also support shortened time to remediation (TTR). This frees up both data professionals and their operations counterparts to collaborate on automation initiatives, simplify complex internal operating structures, and enable support of more complex payment ecosystems. Tips from our CDO Slow drifts are typically detectable only at higher time scales (e.g., daily/weekly). If the payment success rate slowly declines because of a small glitch for a few transactions, it would be hard to notice at lower time scales (e.g., minutes/hours). Monitor high level KPIs (e.g., payments) at lower time scales and more granular breakdowns of the KPI (e.g., payments by country, gateway, provider, merchant, etc.) at a higher timescale. More granularity results in very low volumes of payments for some combinations — making it nearly impossible to detect issues. For example, if the number of payments for a merchant in a certain country using a specific gateway is very low for a time scale (e.g., 1 payment per hour on average), detecting drops in payments and success rates at minute and even hourly time scales will be very hard. But at a daily time scale, the volume will be high enough to detect meaningful drops. Level 4 : Masters — Augmented Root Cause Analysis  Organizations at this level are masters of payment operations. Payment operations are automated across the board using AI. They have sophisticated practices and tools in place for detection, correlation, incident triage, and remediation. In these companies, data professionals and operations are focusing on judgment-based work and are empowered to deal with high value activities such as business strategy, product development, and advanced analytics. For Level 4 companies to reach the upper echelon of Level 5, they have to make remediation core to their payment operations strategy. The good news is they have all the pieces in place: a centralized monitoring platform; autonomous learning of data at scale; accurate learning models; event and anomaly correlation; noise reduction mechanisms; and support for quick root cause analysis.  Tip from our CDO For KPIs with low volumes, monitor the time between transactions and detect when it becomes too long compared to normal. This provides the ability to detect failures in very low volume KPIs. Level 5 : Visionaries — Automated Remediation  Level 5 companies are robust in all dimensions of payment operations maturity, but what really separates them from others is their journey to automated remediation. They codified their remediation strategies using webhooks and triage workflows to enable automated remediation. As a result, teams at Level 5 companies can identify and fix challenges more efficiently, resulting in improved customer experience and much lower incident costs.  With the competitive edge and agility offered by their intelligent operations, Level 5 companies outperform and outpace their less mature counterparts. ​​The journey to level 5 isn’t straightforward, nor is it the same for everyone. So regardless of your current level, you need to pay attention to the Level 5s to understand the challenges coming your way. Tip from our CDO ML-based monitoring is paving the way for autonomous remediation. Soon, ML-based systems will recommend a remediation action based on previous incidents; execute the action through the remediation engine, and fine tune its operations through a closed feedback loop, increasingly improving its reactions. The promise is exciting, but the reality is complicated. Learn more about the route to automated remediation and the eight essential components that automated remediation will depend on to (hopefully) operate successfully. Leapfrog maturity levels with Anodot Anodot's autonomous business monitoring platform helps merchants and payment providers to identify and resolve payment issues faster, route payments intelligently, and optimize approval rates. Anodot customers report 80% faster detection times, 90% drop in alert noise, and 30% reduction in incident costs. By optimizing the payment transaction process, their operations teams can focus their efforts on digital transformation and value-added initiatives.  Payments Operations Business Package Anodot, working with major fintech clients, has developed a turn-key monitoring solution for payment operations teams, utilizing industry best practices and ML domain expertise. Anodot’s payment monitoring business package is built to deliver value fast: Easy integration to any payment data source.  Out-of-the-box alerts and dashboards. Completely autonomous learning, monitoring, and correlations.  Slick and simple UI makes root cause investigation a breeze.  Triage workflows and automated actions. Payment companies and financial institutions can quickly expand their monitoring coverage with additional business packages offered by Anodot: Customer Experience & Support — Monitoring users impressions, funnel, Customer support events, calls, chats and emails.  Treasury & FX — Monitoring deposits, accounts balances, fluctuations,  Fraud — Monitoring trends, anomalies and suspicious behavior