Understanding Kubernetes Cost Drivers Optimizing Kubernetes costs isn’t an easy task. Kubernetes is as deep a topic as cloud (and even more complex), containing subtopics like: Scheduler and kernel processes Resource allocation and monitoring of utilization (at each level of K8s infrastructure architecture) Node configuration (vCPU, RAM, and the ratio between those) Differences between architectures (like x86 and Arm64) Scaling configuration (up and down) Associating billable components with business key performance indicators (KPIs) and much more! That’s a lot for a busy DevOps team to understand and manage, and doesn’t even consider that line-of-business stakeholders and finance team members should have some understanding of each cost driver’s function and importance to contribute to a successful FinOps Strategy. Following is a description of the seven major drivers of Kubernetes costs, the importance and function of each, and how each contributes to your cloud bill. These descriptions should be suitable for the consumption of all business stakeholders, and can be used to drive cross-functional understanding of the importance of each cost driver to Kubernetes FinOps. The Underlying Nodes Most likely, the cost of the nodes you select will drive a large portion of your Kubernetes costs. A node is the actual server, instance, or VM your Kubernetes cluster uses to run your pods and their containers. The resources (compute, memory, etc.) that you make available to each node drive the price you pay when it is running. For example, in Amazon Web Services (AWS), a set of three c6i.large instances running across three availability zones (AZs) in the US East (Northern Virginia) region can serve as a cluster of nodes. In this case, you will pay $62.05 per node, per month ($0.085 per hour). Selecting larger instance sizes, such as c6i.xlarge, will double your costs to $124.1 per node per month. Parameters that impact a node's price include the operating system (OS), processor vendor (Intel, AMD, or AWS), processor architecture (x86, Arm64), instance generation, CPU and memory capacity and ratio, and the pricing model (On-Demand, Reserved Instances, Savings Plans, or Spot Instances). You pay for the compute capacity of the node you have purchased whether your pods and their containers fully utilize it or not. Maximizing utilization without negatively impacting workload performance can be quite challenging, and as a result, most organizations find that they are heavily overprovisioned with generally low utilization across their Kubernetes nodes. [CTA id="03a6f09d-945f-4144-863f-39866f305afb"][/CTA] Request and Limit Specifications for Pod CPU and Memory Resources Your pods are not a billable component, but their configurations and resource specifications drive the number of nodes required to run your applications, and the performance of the workloads within. Assume you are using a c6i.large instance (powered with 2 vCPUs and 4 GiB RAM) as a cluster node, and that 2 GiB of RAM and 0.2 vCPUs are used by the OS, Kubernetes agents, and eviction threshold. In such a case, the remaining 1.8 vCPU and 2 GiB of RAM are available for running your pods. If you request 0.5 GiB of memory per pod, you will be able to run up to four pods on this node. Once a fifth pod is required, a new node will be added to the cluster, adding to your costs. If you request 0.25 GiB of memory per pod, you will be able to run eight pods on each node instance.  Another example of how resource requests impact the number of nodes within a cluster is a case where you specify a container memory limit, but do not specify a memory request. Kubernetes automatically assigns a memory request that matches the limit. Similarly, if you specify a CPU limit, but do not specify a CPU request, Kubernetes will automatically assign a CPU request that matches the limit. As a result, more resources will be assigned to each container than necessarily required, consuming node resources and increasing the number of nodes. In practice, many request and limit values are not properly configured, are set to defaults, or are even totally unspecified, resulting in significant costs for organizations. Persistent Volumes Kubernetes volumes are directories (possibly containing data), which are accessible to the containers within a pod, providing a mechanism to connect ephemeral containers with persistent external data stores. You can configure volumes as ephemeral or persistent. Unlike ephemeral volumes, which are destroyed when a pod ceases to exist, persistent volumes are not affected by the shutdown of pods. Both ephemeral nor persistent are preserved across individual container restarts. Volumes are a billable component (similar to nodes). Each volume attached to a pod has costs that are driven by the size (in GB) and the type of the storage volume attached — solid-state drive (SSD) or hard disk drive (HDD). For example, a 200 GB gp3 AWS EBS SSD volume will cost $16 per month. Affinity and The K8s Scheduler The Kubernetes scheduler is not a billable component, but it is the primary authority for how pods are placed on each node, and as a result, has a great impact on the number of nodes needed to run your pods. Within Kubernetes, you can define node and pod affinity (and pod anti-affinity), which constrains where pods can be placed. You can define affinities to precisely control pod placement, for use cases such as: Dictating the maximum number of pods per node Controlling which pods can be placed on nodes within a specific availability zone or on a particular instance type Defining which types of pods can be placed together and powering countless other scenarios Such rules impact the number of nodes attached to your cluster, and as a result, impact your Kubernetes costs.  Consider a scenario where an affinity is set to limit pods to one per node and you suddenly need to scale to ten pods. Such a rule would force-increase the number of nodes to ten, even if all ten pods could performantly run within a single node.  Data Transfer Costs Your Kubernetes clusters are deployed across availability zones (AZs) and regions to strengthen application resiliency for disaster recovery (DR) purposes, however data transfer costs are incurred anytime pods deployed across availability zones communicate in the following ways: When pods communicate with each other across AZs When pods communicate with the control plane When pods communicate with load balancers, in addition to regular load balancer charges When pods communicate with external services, such as databases When data is replicated across regions to support disaster recovery Network Costs When running on cloud infrastructure, the number of IP addresses that can be attached to an instance or a VM is driven by the size of the instance. For example, an AWS c6i.large instance can be associated with up to three network interfaces, each with up to ten private IPv4 addresses (for a total of 30). A c6i.xlarge instance can be associated with up to four network interfaces, each with up to 15 private IPv4 addresses (for a total of 60).  Now, imagine using a c6i.large instance as your cluster node, while you require over 30 private IPv4 addresses. In such cases, many Kubernetes admins will pick the c6i.xlarge instance to gain the additional IP addresses, but it will cost them double, and the node’s CPU and memory resources will likely go underutilized. Application Architecture Applications are another example of non-billable drivers that have a major impact on your realized Kubernetes costs. Often, engineering and DevOps teams will not thoroughly model and tune the resource usage of their applications. In these cases, developers may specify the amount of resources needed to run each container, but pay less attention to optimizations that can take place at the code and application level to improve performance and reduce resource requirements.  Examples of application-level optimizations include using multithreading versus single-threading or vice versa, upgrading to newer, more efficient versions of Java, selecting the right OS (Windows, which requires licenses, versus Linux), and building containers to take advantage of multiprocessor architectures like x86 and Arm64. Optimizing Kubernetes Costs As the complexity of Kubernetes environments grow, costs can quickly spiral out of control if an effective strategy for optimization is not in place. The key components to running cost-optimized workloads in Kubernetes include: Gaining complete visibility - Visibility is critical at each level of your Kubernetes deployment, including the cluster, node, pod and container levels. Detecting Kubernetes cost anomalies - Intelligent anomaly detection solutions continuously monitor your usage and cost data and immediately alert relevant stakeholders on your team so they can take corrective action. Optimizing pod resource requests - Once your containers are running, you gain visibility into the utilization and cost of each portion of your cluster. This is the time to tune your resource requests and limit values based on actual utilization metrics. Node configuration - Node cost is driven by various factors which can be addressed at the configuration level. These include the CPU and memory resources powering each node, OS choice, processor type and vendor, disk space and type, network cards, and more. Autoscaling rules - Set up scaling rules using a combination of horizontal pod autoscaling (HPA), vertical pod autoscaling (VPA), the cluster autoscaler (CA), and cloud provider tools such as the Cluster Autoscaler on AWS or Karpenter to meet changes in demand for applications. Kubernetes scheduler configuration - Use scheduler rules to achieve high utilization of node resources and avoid node over provisioning. In cases such as where affinity rules are set, the number of nodes may scale up quickly. Anodot for Kubernetes Cost Management Anodot’s cloud cost management solution gives organizations visibility into their Kubernetes costs, down to the node and pod level. Easily track your spending and usage across your clusters with detailed reports and dashboards. Anodot provides granular insights about your Kubernetes deployment that no other cloud cost optimization platform offers.  By combining Kubernetes costs with non-containerized costs and business metrics, businesses get an accurate view of how much it costs to run a microservice, feature, or application. Anodot’s powerful algorithms and multi-dimensional filters also enable you to deep dive into your performance and identify under-utilization at the node level.  To keep things simple, the solution seamlessly combines all of your cloud spend into a single platform so you can optimize your cloud cost and resource utilization across AWS, GCP, and Azure.

The high volume and high rate of transactions in the adtech market pushes vast amounts of data through the entire ecosystem, 24x7. Regardless of its place in the market – advertiser, ad exchange, ad network, or publisher – each has thousands or even hundreds of thousands of metrics that measure every aspect of the company’s business.  Monitoring these metrics can prevent incidents from impacting the business. Even a short-term outage of some aspect of selling or serving ads can result in significant revenue loss.  The Need for Real-Time, Automated Monitoring Today, adtech businesses need to go beyond simply monitoring KPIs, they need to be able to react to the story the data is telling, the moment it shows up. The best way to do that is using machine learning (ML) and artificial intelligence (AI) to automate the process of monitoring critical metrics and spotting issues as soon as they appear.  Some metrics are more important than others, mainly because they have an outsized impact on revenue. In such cases, analysts want to know as soon as possible if the metric is deviating from the normal baseline in a negative way.  Some of these metrics are enormously complex, with multiple dimensions that make them quite impossible to monitor without a sophisticated ML/AI system. Let’s look at a few examples of metrics that adtech firms have told us that, in their experience, are absolutely critical to monitor closely.  Publishers need to closely watch fill rates for their placements Fill rate is a critical metric for publishers on the supply side of advertising. This metric refers to the rate at which a specific ad placement area is utilized. The more time that the space is populated with ads that are seen by visitors to the webpage, the higher the fill rate. Optimally, a company would like to have a fill rate of 100% or as close to that as possible.  Take the example of a news website that provides free access to content. Since there is no paywall, advertising revenue is vitally important, making fill rate a critical KPI to monitor.   If the fill rate suddenly drops for a particular region, browser, advertiser, or reader profile, the company is going to miss out on some revenue. And this isn’t the only placement area the company offers up; there may be hundreds of placements.  One reason it’s difficult to measure fill rate is that the metric experiences seasonality. Different placements may have different fill rates at different times of the day for different locations. Only a machine learning model that accounts for seasonality can keep up with the variations in the data patterns for this very important metric. If the drop in fill rate is associated with a specific advertiser, the publisher can reach out to let them know there’s a problem where ads aren’t appearing as they should. This is bad for both the advertiser and the publisher, so the sooner the issue is resolved, the better. Advertisers must keep an eye on ad spend to optimize their budgets Another crucial KPI for advertisers on the demand side to pay close attention to is paid impressions (ad spend). An advertiser wants to reach as many eyeballs as possible and typically pays to place ads where they are most likely to be seen (and hopefully clicked on). This is another metric that can get complicated very quickly, making the ad spend hard to manage. The first assumption is that the advertiser wants to spend the entire budget. There is little value in not spending the full amount because that means ads aren’t being served, people aren’t seeing them, and sales may not occur due to prospects’ lack of awareness of the product or service.  The next question is how to allocate the funds to maximize impressions. Even a very simple example shows how this can get complicated quickly. Suppose an advertiser has a monthly budget of $30,000 for online ads. A simple plan might be to spend $1,000 each day on placements. But traffic isn’t equal across every day of the week and there may be seasonal events that cause spikes or drops in traffic.  Now imagine a company with a very large ad spend budget, many different campaigns, and an array of target content platforms. It’s easy to see how planning and tracking ad spend can get complicated. This is where ML and AI is critical to understanding the business context and impact of seasonality of your key metrics that impact ad spend.  The example alert below shows an unexpected drop-off in ad spend that would certainly warrant investigation. There’s one more thing that can throw a monkey wrench in the ad spend monitoring process. What if the company knowingly spends all the money set aside for a campaign in the first 10 days of the month, i.e., the campaign is capped? On the 11th day, and every day of the month after that, a machine learning system might flag the day’s $0 spend as an anomaly, flooding the advertiser with false positive alerts.    Anodot has solved this issue by having the advertiser send a metric indicating the campaign is capped. Then the ML system ignores the ensuing $0 daily spends, thus preventing false positive alerts. Learn more about how Anodot is reducing false positives in ad campaigns. Proactive monitoring of ad requests and bid requests There are other important KPIs that should be monitored closely, including ad requests and bid requests. Ad requests are calls from a publisher into an exchange to sell their placement inventory. It’s important to monitor this metric in real time because, if it decreases dramatically, it would indicate that there is low inventory in order to sell. Therefore the revenue to the publisher and/or exchange would decrease. Conversely, if ad requests spike up too much, it could cause capacity issues in an exchange’s data center volume. Bid requests are calls to bid on inventory in an exchange from the demand side. This metric should be monitored in real time because decreases in the number would affect ad spend and potentially create unsold inventory for the publisher. The graphic below illustrates an alert on this metric showing an unexpected drop in bid requests. Anodot can autonomously monitor your critical KPIs to maintain your revenues Buying and selling ads at scale triggers exponential complexities. Anodot’s AdTech analytics monitors 100% of data and metrics, including the backend process, data quality, continuity and ad load time, to ensure smooth platform performance and to protect the user experience. Anodot helps adtech companies monitor changes in traffic volume, quality and conversion rates. See which campaigns have “gone silent” or are at risk of churn. Use these insights to reach out to customers and resolve issues before they escalate.

It’s hard to believe enterprise BI platforms have been around for three decades. In that time, they have served the purpose of collecting and analyzing large amounts of data to help businesses make more informed decisions.  But in today’s data-driven economy, analysts struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret data in real-time. The fact is, traditional BI tools were not designed for the massive volume and speed of data in today’s organizations.  To address these shortcomings, Forrester recently published The Future of Business Intelligence report. The report gives technology executives recommendations on how to get more value from their BI applications. The recommendations include infusing by platforms with the power of augmented AI and names Anodot as a vendor with these capabilities.  According to Forrester, many organizations are struggling to get mileage from their current BI applications because they are: Not actionable and, therefore, not impactful Delivered in silos without context Primarily used by data and analytics professionals  Forrester’s report focuses on the emerging technologies and techniques that deliver business insights in a more efficient and effective way – beyond data visualizations and dashboards. We’ve captured the top recommendations for technology executives and data leaders. [CTA id="f62f355f-0ce8-49d8-88fb-bfef37af5c56"][/CTA] Close the gap between insights and outcomes with impactful BI Forrester says technology and data leaders must never lose sight of the ultimate BI objective: to effect tangible business outcomes with insight’s driven actions.  To start closing the gaps between insights and actions, technology and data professionals should:  Integrate metadata using advanced tooling Leverage digital worker analytics platforms to find links between insights and actions Automated operational decisions Procure decision intelligence solutions Effect tangible business outcomes with actionable BI  An insight by itself, no matter how informative or revealing, is not actionable. To make BI actionable, Forrester recommends technology and data leaders should:  Infuse actions into BI apps Embed workflows into analytical applications Upgrade business applications with embedded analytics Combine analytical and transaction applications via a translytical BI platform Become more effective with augmented capabilities Applying actionable insights is one way to influence business outcomes. To make BI more effective with augmented capabilities, technology and data leaders should:  Adopt an augmented BI platform for AI-infused BI  Use the augmented BI platform for anticipatory capabilities Forrester recommends Anodot’s AI-driven business monitoring platform for organizations looking for augmented capabilities. Anodot uses AI and machine learning to analyze 100% of business data in real time, autonomously learning the normal behavior of business metrics.  Rather than developing new views, models, and dashboards, teams leveraging AI analytics gain real-time actionable insights to react to change and predict the future.  Anodot delivers the full context needed for BI teams and business users to make impactful decisions by featuring a robust correlation engine that groups anomalies and identifies contributing factors.  BI should be unified and personalized Analytics silos continue to plague companies as they implement different platforms for strategic vs. operational insights, structured vs. unstructured data analysis, and basic vs. advanced analytics. Data leaders should invest in options to unify BI as follows:  Start by unifying all insights-driven decisions under one umbrella Proceed to unify analytics on structured and unstructured data Integrate multiple BI platforms via a BI fabric Emphasize, prioritize, and invest in BI personalization for different users Remove the remaining silos by unifying analytical and operational reporting platforms BI based on adaptive, future fit technology Forrester predicts that firms that prepare for systemic risk events with a future fit technology strategy will outpace competition by growing at 2.7x industry averages. Technology leaders should make future BI adaptive by: Architecting to bring BI to data. Investing in decoupled, headless BI Opportunistically deploying full-stack platforms  Adding investigative intelligence to your future BI tech portfolio mix BI is embedded in all systems of work According to Forrester, 80% of enterprise decision makers are not using BI applications hands on, rather they are relying on data analytics teams to “bring BI to them”.  To seamlessly embed relevant insights into all digital workspaces, technology and data leaders must prioritize investments in BI functionality embedded in: Business applications Enterprise collaboration platforms Enterprise productivity platforms Browsers  In today’s competitive environment, your analytics solution needs to be intelligent in order to deliver business intelligence. Unlike dashboards, by using automated machine learning algorithms in an analytics solution like Anodot, you can eliminate business insight latency, and give your business vital information to detect and solve incidents before losing revenue or customers.   

Few organizations remain today without some of their business operating in the cloud. According to a study from 451 Research, part of S&P Global Market Intelligence, 96 percent reported enterprises using or planning to use at least two cloud application providers (Software-as-a-Service), with 45 percent using cloud applications from five or more providers. In 2024, global spending on public cloud services is expected to reach $679 billion, surpassing $1 trillion by 2027. Most companies move to the cloud to take advantage of cloud computing solutions' speed, innovation, and flexibility. Cloud operations can also provide cost savings and improved productivity.  However, controlling cloud costs has become increasingly difficult and complex as cloud adoption grows. That is why cloud cost management has become a priority for CIOs to understand the true ROI for cloud operations.  When cloud assets are fragmented across multiple teams, vendors, and containerized environments, it is easy to lose sight of the budget. As a result, cloud financial management is a must-have for understanding cloud cost and usage data and making more informed cloud-related decisions.  Plus, it's an opportunity for more savings! According to McKinsey, businesses using CFM can reduce their cloud costs by 20% to 30%. But what exactly is Cloud Financial Management (CFM)? Is it merely about cutting costs? What kind of tools are best for multiple cloud environments? If you have these and other questions, we have the answers. Let’s jump in!   Table of Contents: What’s Cloud Financial Management? Cloud Financial Management Benefits  Cloud Financial Management Challenges Building a Cloud Center of Excellence Anodot for Cloud Financial Management  Anodot’s 7 Core Features for Cloud Success   [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] <h2id="toc-what">What's Cloud Financial Management? Cloud Financial Management is a system that enables companies to identify, measure, monitor, and optimize finances to maximize return on their cloud computing investments.  CFM also enhances staff productivity, workflow efficiency, and other aspects of cloud management. However, it is important to remember that while cost is a major focus, it’s not the only one.  A subset of CFM is FinOps, which is essentially a combination of Finance and DevOps. The idea behind FinOps is to foster collaboration and communication between the engineering and business teams to align the cost and budget to their technical, business, and financial goals.   Cloud Financial Management Benefits  Better Track Cloud Spend Cloud Financial Management helps companies oversee operations, tasks, and resources that drive usage billing. This insight can be used to identify projects, apps, or teams that are driving your cloud costs. Optimize Cloud Costs With visibility into cloud resources and spend, your organization can identify and remove unutilized resources, redundant integrations, and wasteful processes. Financial Accountability   Instead of reacting to unexpected cost spend and spikes, cloud financial management allows businesses to plan and predict budgets by making delivery teams financially accountable. By aligning cloud financial data to business metrics, organizations can establish common goals and outcomes.  Cloud Financial Management Challenges Budgeting Migrating from on-premise to the cloud often means transitioning from a CapEx to an OpEx model. On the surface, switching to a predictable OpEx-based strategy seems attractive. However, the change can create more issues than it solves.  Optimizing costs is the biggest driver for moving to OpEx. However, cloud spend is vulnerable to waste and overspend if not carefully managed. Many companies haven't reaped the expected cloud benefits due to poor visibility and control. Some have taken the dramatic step of ‘repatriating’ workloads while others have adopted a hybrid approach.  Visibility Into Cloud Assets and Usage Monitoring cloud assets makes or breaks FinOps. But employees often find it challenging to track asset performance, resource needs, and storage requirements. Tagging offers a simple solution, allowing easy categorization of cloud assets by department, performance, usage, costs, and more. Even when you look at the infrastructure, there are numerous departments in an organization, and there are different purposes for them to use the cloud. So, unless and until there is a proper tagging system for these departments, operations, and costs, it is very difficult to monitor cloud assets.  Calculating Unit Costs The unit cost calculation becomes a tedious job, considering the complexity of the cloud infrastructure and the sheer number of assets. In addition, calculating and comparing the investment and the revenue being generated becomes difficult when there are so many multiple interdependencies.  Identifying Inefficiencies Companies that lack full visibility into cloud spend find it difficult to identify where there are inefficiencies, waste, or overuse of resources. The result is that decisions can’t be made regarding the efficient allocation of resources, and companies are in the dark regarding questions such as whether an increase in spend results from business growth or from sheer inefficiencies. Building a Cloud Center of Excellence A Cloud Center of Excellence (CCoE), or FinOps practice, is an important next step for companies using ad hoc methods for cloud cost management. A CCoE provides a roadmap to execute the organization’s cloud strategy and governs cloud adoption across the enterprise. It is meant to establish repeatable standards and processes for all organizational stakeholders to follow in a cloud-first approach. The CCoE has three core pillars: Governance - The team creates policies with cross-functional business units and selects governance tools for financial and risk management. Brokerage - Members of the CCoE help users select cloud providers and architect the cloud solution. Community - It's the responsibility of the CCoE to improve cloud knowledge in the organization and establish best practices through a knowledge base. With those pillars as a foundation, CCoEs are generally responsible for the following activities: Optimizing cloud costs - Managing and optimizing cloud spend is a key task of the CCoE. They are also accountable for tying the strategic goals of the company with the cost of delivery value in the cloud. Managing cloud transformation - In the initial phase of transformation, the CCoE should assess cloud readiness and be responsible for identifying cloud providers. During migration, the team should provide guidance and accurate reports on progress. Enforce cloud policies - Security and regulatory requirements can change frequently in complex and changing cloud ecosystems. It's important that CCoE members enforce security standards and provide operational support across the business. Anodot for Cloud Financial Management  Anodot’s Cloud Cost Management solution helps organizations get a handle on their true cloud costs by focusing on FinOps to drive better revenue and profitability. From a single platform, Anodot provides complete, end-to-end visibility into your entire cloud infrastructure and related billing costs. By tracking cloud metrics alongside revenue and business metrics, Anodot helps cloud teams grasp the actual cost of their resources. Anodot's 7 Core Features for Cloud Success   Forecasting and Budgeting with 98.5% Accuracy Use historical data to predict cloud spending and usage based on selected metrics and changing conditions to make necessary adjustments to avoid going into the red. Cost Visibility Manage multi-cloud expenses on AWS, Azure, Google Cloud, and Kubernetes with customizable dashboards, multi-cloud cost tagging, and anomaly detection. Real-Time Cost Monitoring  Monitoring cloud spend is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time. Cloud activity that isn’t tracked in real-time opens the door to potentially preventable runaway costs. Anodot enables companies to detect cost incidents in real-time and get engineers to take immediate action.  Saving Recommendations Get 80+ CTA recommendations throughout all major cloud providers and enjoy a 40% reduction in annual cloud spending. Real-time Alerts & Detection Eliminate uncertainty surrounding anomalies through precise, targeted notifications and machine learning (ML) models. Stay consistent with cloud activity by analyzing data to accurately differentiate normal fluctuations from actual risks, thereby minimizing false positives. 360° View of the Multicloud Never waste time searching for a spending transaction again. Simplify cost management with an all-in-one platform offering billing flexibility and cost allocation for enterprise and MSP models. AI Tool for Cloud Spending With a simple search, cloud cost management can be automated with CostGPT. Get instant answers to address common cost challenges, including complex pricing models, hidden costs, and inadequate monitoring and reporting. Automatic Savings Trackers Track the effects of applied recommendations using automated savings reports and a savings tracker.   CFM just got a lot easier with Anodot. Try it out and see the difference.  

Ad Campaigns: How to Reduce False Positive Alerts for Ad Budget Caps   The massive scale and speed of online advertising means that adtech companies need to collect, analyze, and act upon immense datasets instantaneously, 24 hours a 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 monitoring tools such as business intelligence systems and executive dashboards don’t scale to the large number of metrics adtech generates, creating blind spots where problems can lurk. Moreover, these tools lead to latency in detecting issues because they act on historical data rather than real-time information. Anodot’s AI-powered business monitoring platform addresses the challenges and scale of the adtech industry. By monitoring the most granular digital ad metrics in real time and identifying their correlation to each other, Anodot enables marketing and PPC teams to optimize their campaigns for conversion, targeting, creative, bidding, placement, and scale. False positive alerts steal time and money from your organization   In any business monitoring solution, alerts for false positive anomalies or incidents are troubling in several ways. First of all, they divert attention from investigating or following up on positive anomalies detected. The fact is, no one knows the difference between a false positive and a true positive until at least some investigative work is done to determine the real situation. In the case of false positives, this is time (and money) wasted while true positives are on the back burner waiting for the resources to investigate them. Time lost = money lost in the adtech industry. Too many false positive notifications create alert fatigue and eat away at confidence in the monitoring solution. Analysts may begin to doubt what is found and ignore the alerts—thus real problems are not being found and mitigated. When excessive false positive alerts are issued, the monitoring solution needs to be tuned in terms of the detection logic in order to reduce the noise and improve accuracy. This is precisely what happened in a recent case with an Anodot adtech client, and the resulting fix will help anyone in adtech and marketing roles.  Capped campaigns create false positives in business monitoring   In this scenario, an adtech company’s account managers are responsible for helping their customers manage campaign budgets and allocate resources in order to attain optimal results. Working closely with Anodot, this company has set alerts for approximately 7,000 metrics to monitor for changes in patterns and to detect any technical issues that might result in unexpected drops in their impressions, conversions, and other critical KPIs. It’s all very standard for any adtech company. So what’s the issue? Capped campaigns create false positives.  Many of this company’s customers have a predetermined budget for each campaign that is used to pay for the various paid ads, clicks, impressions, conversions, and so on. When the budget is exhausted, or nearly so, the account manager is notified by an internal system. At the same time, there’s a rather large drop for the relevant KPIs that are being measured and monitored, which makes sense given that no additional money is being put toward the purchase of ads. This usually happens without a relevant detectable pattern. While the account manager expects the drop in KPIs, the business monitoring system does not—and thus the detected drop in KPIs appears to be an anomaly. The system often sends a corresponding alert, which in this case is a false positive because the drop was expected by the account manager. Capped campaigns are not unusual in this industry, so the monitoring system needs to be tuned to accommodate these occurrences to reduce the number of false positive alerts. Anodot’s unique approach eliminates false positives in capped campaigns   Anodot’s first attempt to resolve the issue was to add the capped events as an influencing event. This failed to fix the issue because the influencing event did not correlate to a specific metric, only to an alert. The result was still false positive alerts which often went to many people, resulting in redundant “noise.” A successful resolution came when Anodot suggested sending notice of the capped event as an influencing metric in its own right so that it can be correlated on the account dimension level or on a campaign ID. So, the adtech company sends a metric – “1” for a capped event, “0” for uncapped – via an API to Anodot. The API call is triggered on each significant KPI change. In response, a watermark is sent respectively to close the bucket, ensuring the metric’s new value to be registered in the quickest way possible. When a KPI drop occurs, Anodot looks for the corresponding business metric of the capped event on an account level. If the latter metric contains a “1” then no alert is triggered because the system now knows this is a capped campaign. The influencing metric will go back up to the last 10 data points, looking for the last one reported, meaning that if Anodot gets the capped event before the drop is reported, Anodot is still able to detect it. The illustrations below show how this technique prevents false positive alerts. The anomaly of the dropping KPI is detected in the orange line. The corresponding capped campaign metric is reported in the image below. When the metrics are correlated and placed side by side, the resulting image looks like this: Note the lack of the orange line indicative of an anomaly that will trigger an alert. Anodot’s approach works for any company with capped campaign budgets   While Anodot designed this approach for a specific client’s needs, it has application for other companies in adtech that have capped campaigns. The goal is to eliminate the false positives that arise from campaigns reaching their end budget, causing a drop in KPIs like CTR, impressions, revenue, and so on.  The adtech company must have granular campaigns data, registering both capped and uncapped events to be sent to Anodot via API. Seasonality is recommended on the campaigns being monitored, meaning that capped and uncapped events are to be sent to Anodot at the same intervals as the campaigns; for example, every 5 minutes, hourly, etc. The process is easy to set up, with campaign monitoring as an existing condition. The first step is to send the capping events as metrics (0 or 1) with the relevant dimension property, such as the account ID, campaign name, or campaign ID. Next, Anodot will use the capped metrics as influencing metrics within the alert. If this sounds like a scenario that will help your company reduce false positive alerts while monitoring campaign performance, talk to us about how to get it set up. By eliminating false positives, your people can concentrate on what’s really important in monitoring performance.   

Big Data: How AI Analytics Drives Better Business   “Data-driven” is the latest buzzword in organizations in which data-based decision making is directly connected to business success. According to Gartner's Hype Cycle, more than 77% of the C-suite now say data science is critical to their organization meeting strategic objectives.  For top organizations looking to adopt a data-driven culture to stay competitive, what does that mean? The term evokes images of data analysts huddled in a dimly lit office, watching numbers and visualizations pass on a dashboard as their observant eyes search for anomalies.  But as data scientists and analysts become increasingly expensive and in high demand, many organizations are questioning why these highly skilled knowledge workers should be relegated to a role where they observe dashboards and react to changes?  Companies that lead in data-driven organizational analytics know that for knowledge workers to deliver value, they need tools that free them from laborious tasks to spend more time on meaningful strategic initiatives and less time wrangling data for insights. Growth of Big Data    Industry analysts predict that digital data creation will increase by 23% per year through 2025. The global market for Big Data is expected to exceed $448 billion by 2027. So what's driving this growth? Businesses across the globe now recognize the force multiplier that data-driven business intelligence represents to improve business outcomes.  The only legitimate restraining forces for the development of Big Data are the costs associated with staffing data science and business intelligence competencies and the time-intensive nature of analytics work.  With over 81% of companies planning to expand their Big Data capabilities and data science departments in the next few years, the competition for resources will only increase.  Traditional BI Dashboards Can't Keep Up With Big Data   In today’s data-driven economy, managers struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret the data in real-time. The fact is, conventional BI approaches and tools were not designed for and are not suited for the growth of Big Data. While most of the existing BI solutions can process and store a vast amount of data with many dimensions, they don't offer analysts a manageable way to get real-time business insights, and they certainly don't help data science teams predict the future.  Traditional BI tools lack detailed analysis, offer little correlation, and don’t provide real-time actionable insights. That leaves data science teams and business analysts spending hours with data stores instead of working on delivering value with predictive analytics.  Gain Business Value With Big Data Empowered by AI Analytics   Many companies overextend their BI tools and teams on use cases they were never built to handle. That leaves knowledge workers trying to extract insights from traditional solutions. To put that in perspective, it’s like tying an anchor around their waist and asking them to swim.  The answer is extending business intelligence capabilities with analytics capabilities empowered by AI and machine learning. Rather than developing new views, models, and dashboards, teams leveraging AI analytics gain real-time actionable insights to react to change and predict the future.    Big Data AI Analytics With Anodot   Regardless of the industry or how far along your business might be in its data analytics journey, Anodot's AI-powered analytics can empower your knowledge workers to focus on leveraging business insights to deliver value. Instead of digging into dashboards for answers, Anodot delivers the answers to them, automatically.  Anodot monitors 100% of business data in real time, autonomously learning the normal behavior of business metrics. Our patented anomaly detection technology distills billions of data events into a single, unified source of truth without the extra noise that can leave teams flatfooted.  Anodot delivers the full context needed for BI teams to make impactful decisions by featuring a robust correlation engine that groups anomalies and identifies contributing factors. This helps teams know first, before incidents impact customers or revenue.  Data-driven companies use Anodot's machine learning platform to detect business incidents in real-time, helping slash time to detect by 80 percent and reduce false-positive alert noise by as much as 95 percent.

Everyone from managers to C-suite executives wants information from analytics in order to make better decisions. Business analytics gives leaders the tools to transform a wealth of customer, operational, and product data into valuable insights that lead to agile decision-making and financial success. Traditional business intelligence and KPI dashboards have been popular solutions but they have their limitations. Creating dashboards and management reports is labor-intensive, plus someone has to know what to look for in order to present the information in graphical or report format.  The Limitations of Traditional Dashboards The information that is surfaced tends to be high-level summary data which only pertains to some of the company’s key metrics. This is largely because BI systems and KPI dashboards can’t scale to handle a significant number of metrics. As a result, managers are making decisions based on incomplete information. In addition to lacking depth and breadth of data, these systems present historical rather than real-time data. While this is sufficient for observing trends over time – e.g., whether sales are increasing over time, what cloud costs are incurred monthly, etc. – using historical data takes away the ability to make decisions and act on something that is happening right now. Moreover, the high-level reports and dashboards aren’t helpful when needing to find the source of an issue because of the lack of context and data relationships. In short, business dashboards have their purpose for providing high-level summary information but they fall far short of being able to present in-depth, real-time information to support decisions and actions that must be taken now. Beyond Dashboards: AI-Powered Analytics Scale Up to Go In-Depth   AI-powered analytics is an enhancement over dashboards that enables the scalability to address all relevant business data. This allows a company to monitor everything and detect anything—especially events they didn’t know they had to look for — the unknown unknowns.  AI-powered analytics use autonomous machine learning to ingest and analyze vast numbers of metrics in real-time.  Anodot’s Autonomous Business Monitoring platform is just such a system, and provides organizations with “a data analyst in the cloud.” Let’s explore the numerous benefits to using AI-powered analytics to closely monitor subtle changes in the business as they occur. Work with Data in Real-Time to Accelerate Decision-Making and Action   AI-powered analytics is able to work with data in real-time, as it is coming into the system from multiple data sources across the business. Machine learning algorithms process the data and look for outliers in order to discover issues as they are happening rather than long after the fact.  It allows organizations to make timely corrections in their processes, if needed, to minimize the impact of negative anomalous activity. Of course, not all anomalies are problematic; some may indicate that something good is happening or spiking and it would be helpful to know sooner rather than later. Take, for example, the case where a celebrity is endorsing a product on Instagram. The positive buzz generated by this external mention can really drive up sales of that product, but only if the business can respond in time to capitalize on the free attention.  A large apparel conglomerate learned this lesson the hard way when their BI team discovered a celebrity endorsement days after it occurred. If they had discovered the sharp uptick in sales for that product and the rapidly dwindling inventory of that product in one of their regional warehouses in real-time, they could have capitalized on the opportunity by increasing the price or replenishing the inventory to keep the customer demand fed. The apparel company now works with Anodot to detect sudden spikes in sales of their various products. This information is detectable within minutes, and with immediate alerting to the spikes, the company can respond accordingly to ensure sufficient inventory to cover the unexpected (but very welcome) demand.  Work with Metrics on a Vast Scale   While a KPI dashboard might be able to track and present information on dozens of metrics, an AI-powered analytics solution can work with millions or even billions of metrics at once. More metrics means being able to get more granularity as well as more coverage – i.e., depth and breadth – into what is happening within the business.    The ad tech company Minute Media tracks more than 700,000 metrics in order to monitor the business from every angle. The company uses Anodot Autonomous Detection to detect anomalies in that data that could be indicative of invalid traffic, video player performance issues, or other conditions that lead to loss of revenue on ads. Since implementing the AI-powered analytics solution, Minute Media has been able to increase its margins on ad revenues to improve the company’s bottom line. (Read more about this success story here.) Correlate Metrics from Multiple Sources   With thousands of metrics (or more) now in play, some of these metrics will have relationships with each other that may not be obvious on the surface. For example, a DNS server failure halfway around the world could be impacting a company’s web traffic that results in fewer visitors and lower revenue. The only way to identify this cause-and-effect relationship is through AI-powered analytics.  Solutions such as Anodot automatically correlate metrics from numerous sources across the business to uncover previously unknown relationships among metrics. Correlation analysis is incredibly valuable when used for root cause analysis and reducing time to detection (TTD) and time to remediation (TTR).  Two unusual events or anomalies happening at the same time/rate can help to pinpoint an underlying cause of a problem. The organization will incur a lower cost of experiencing a problem if it can be understood and fixed sooner rather than later. Consider this example use case from the ad tech world. Both Microsoft and Google rely on advances in deep learning to increase their revenue from serving ads. AI-powered analytics allow these companies to identify trends and correlations in real-time, like instantly correlating a drop in a customer’s ad bidding activity to server latency. With the root cause quickly identified, the ad tech company can resolve the latency issue to help the bidding activity return to normal levels. Let the Data Tell the Story Instead of Attempting to Predefine the Outcome   A clear benefit of using AI-powered analytics is that it can uncover insights in the data that weren’t expected or anticipated. No one has to predefine what they want the data to reveal. This is well illustrated with an example from another Anodot customer. Media giant PMC was having difficulty discovering important incidents in their active, online business. The company had been relying on Google Analytics’ alert function to tell them about important issues. However, they had to know what they were looking for in order to set the alerts in Google Analytics. This was time-consuming and some things were missed, especially with millions of users across dozens of professional publications. PMC engaged with Anodot to track their Google Analytics activity, identifying anomalous behavior in impressions and click-through rates for advertising units. Analyzing the Google Analytics data, Anodot identified a new trend where a portion of the traffic to one of PMC’s media properties came from a bad actor—referral spam that was artificially inflating visitor statistics.  For PMC’s analytics team, spotting this issue would have required that they already know what they were looking for in advance. After discovering this activity by using Anodot, PMC was able to block the spam traffic and free up critical resources for legitimate visitors. PMC could then accurately track the traffic that mattered the most, enabling PMC executives to make more informed decisions. Monitor for conditions that could indicate a cyberattack or data breach in progress   Cyberattacks don’t happen in a vacuum; they need to use the underlying infrastructure of an organization’s network and other systems to establish their foothold and make their attack moves. By monitoring the operational metrics of these systems, companies can get alerts of early indicators of something being amiss. Consider the massive Equifax data breach of 2017. Equifax confirmed that a web server vulnerability in Apache Struts that it failed to patch promptly was to blame for the data breach. DZone explained how this framework functions. “The Struts framework is typically used to implement HTTP APIs, either supporting RESTful-type APIs or supporting dynamic server-side HTML UI generation. The flaw occurred in the way Struts maps submitted HTML forms to Struts-based, server-side actions/endpoint controllers. These key/value string pairs are mapped Java objects using the OGNL Jakarta framework, which is a dependent library used by the Struts framework. OGNL is a reflection-based library that allows Java objects to be manipulated using string commands.” Had Anodot’s AI-powered analytics been in place at Equifax, it could have tracked the number of API Get Requests for user data and noticed an anomalous spike in requests, thus catching the breach instantly, regardless of the existing vulnerabilities. While Anodot Autonomous Detection is not a cybersecurity solution per se, it can complement an organization’s regular security stack by monitoring for unusual activity on the company’s systems and infrastructure. Monitor the Performance of Telecom Systems   One area where this is becoming increasingly important is telecom services and 5G cellular networks. As 5G deployment scales up, an explosion of devices and new services will require proactive monitoring to help ensure guaranteed performance for mission-critical applications. With the complexity of 4G/5G hybrid networks and a host of new challenges for 5G networks, and as the network of interconnected devices grows, monitoring and maintenance become a greater challenge for operational teams. By correlating across metrics and performing root cause analysis, AI-powered analytics significantly decreases detection and resolution time while eliminating noise and false positives. Get instant insights on cloud costs with Anodot's CostGBT Speaking of innovative AI-powered tools, we recently released a new feature to visualize your cloud spending clearly. With just one simple cost-related question, our bot generates the answers needed to start reducing cloud waste and saving on costs. Still not sold? Maybe the benefits will convince you: Simplicity: Users can ask questions about their cloud costs in chat, receiving accurate and relevant insights. Actionable Insights: CostGPT provides strategic optimization suggestions, along with further inquiries and commands, to help customers thoroughly understand their cloud expenditure. Proactive Decision-Making: By leveraging search data, CostGPT enables organizations to make informed decisions on cloud resource allocation, preventing unnecessary costs and optimizing resource utilization. Real-Time Data Visualizations: CostGPT offers intuitive visualizations for exploring and analyzing cloud costs, allowing users to plan and make informed expenditure decisions. Ready to experience it yourself? Talk to us to get started. In Summary   There are many uses cases for and benefits of AI-powered analytics. Anodot’s Autonomous Detection business monitoring solution and CostGBT allows companies to automatically find hidden signals in multitudes of metrics, in real-time, so that action can be taken immediately to minimize the negative impacts of issues in the underlying systems. This can preserve revenues and reduce the cost of lost opportunities. 

While both platforms offer cloud cost management, Anodot’s continuous monitoring and unit economics empower teams to proactively identify, understand and eliminate waste to significantly reduce cloud spend.

Last month Gartner published its first ever “Market Guide for AI Offerings in CSP Network Operations,” and we’re excited to share that Anodot has been identified as a Representative Vendor in the report. According to the Gartner report, “CSPs are focusing on automation of their network operations to improve efficiency and customer experience, and mitigate security concerns.” The market guide presents many new and actionable insights. We’re taking a closer look at a couple of them and sharing our perspective on their significance for operators. The strategic role of data correlation The first insight we’d like to discuss is the reason behind why it’s so challenging for operators to meet their top objectives for AI implementation, i.e.: Network monitoring Network optimization Root cause analysis According to Gartner this is due to the fact that: “Despite CSPs having multiple data sources, most of it is uncorrelated and is thus processed separately. Vendors are working on ways to unify these data silos in order to create a large dataset for their ML algorithms.” The great news for operators is that they don’t have to wait to overcome the correlation hurdle. This is because collecting and correlating all data types from 100% of the network’s data sources (however siloed) in real time, is exactly what Anodot’s autonomous network monitoring platform does. Say goodbye to silos  Anodot’s unique, off-the-shelf data collectors and agents, collects data from every network domain and layer, and service and app, aggregating inputs from sources that include network functions and logic such as fault management KPIs, xDRs, OSS/BSS tools, performance management KPIs, probe feeds, counters, alerts, and more. So, the days of monitoring the network in silos, with separate tools for each domain and layer, are over. Kudos to correlations As for correlations, Anodot correlates anomalies across the entire telco stack (including the physical, network, and data layers), and between KPIs, alerts, and network types (i.e., mobile, fixed/broadband, and wholesale carriers/transmission). Root cause at the speed of AI Through this combination of eliminating siloes and correlating data, Anodot also provides early detection of service degradation, outages, and system failures across the entire telco ecosystem, sending alerts in real time with the relevant anomaly and event correlation for the fastest root cause detection and resolution. Benefits for CSPs include: Time to detect incidents accelerated by up to 80% Time-to-resolve incidents improved by 30%  Root cause analysis improved by 90% And this is how operators can address the top three objectives for AI in their network operations. A quick & important look at KPIs Another important insight presented by Gartner in the report includes the following: “CSP CIOs who are looking to leverage automation and AI offerings to support their organizations with evolving business requirements, network operations, and continuing digital transformation and innovation should: Prioritize the critical success factors of your network operations through a structured analysis of your network operations center (NOC) and other related operations that can benefit from AI. Identify your service-level objectives and select AI vendors that contribute to your business-driven key performance indicators (KPIs).” 3 Let’s focus on KPIs. Operators have millions to billions of network-centric KPIs that help uncover performance issues. But without autonomous anomaly detection on 100% of the data, it can be impossible to deal with the volume and velocity of data, identify the anomalies within network big data, and resolve issues before they impact business-driven KPIs. The impact of impact scoring Here too, Anodot comes with an innovative approach. Not only does the platform continuously and autonomously monitor and analyze millions (and billions) of performance and customer experience KPIs by leveraging patented algorithms. It takes this even further. Once it detects an anomaly, it assigns a score based on its impact on what’s important to the operator. In fact, every single KPI that comes in goes through a classification phase and is matched with the optimal model from a library of model types. Then, Anodot’s statistical models group and correlate different KPIs in order to analyze them based on the use case.  Moreover, it automatically groups related anomalies and events across the network into one alert, reducing alert noise by 90%. Indeed, at Anodot, we believe in the importance of aggregating and correlating data from multiple data sources to address the key AI objectives of CSPs. We are also committed to assuring that AI brings a strategic contribution to business-driven KPIs. And, being identified as a Representative Vendor in Gartner’s report serves as a great validation of this approach.  The  Market Guide for AI Offerings in CSP Network Operations is available to Gartner subscribers.