Amazon EC2 Explained Amazon Elastic Compute Cloud (EC2) is one of the core services of AWS, designed to help users reduce the cost of acquiring and reserving hardware.  EC2 represents the compute infrastructure of Amazon's cloud service offerings, providing organizations a customizable selection of processors, storage, networking, operating systems, and purchasing models.  It is known for assisting organizations to simplify and speed up their deployments for less cost and enabling them to increase or decrease capacity as requirements change quickly.  However, the costs associated with instances and features in EC2 can soon get out of control if not properly managed and optimized. The first cost consideration is usually selecting an instance type.  EC2 Instance Types Even for experienced cloud engineers and FinOps practitioners, EC2 pricing is extraordinarily complex. Many options impact cost, with instances optimized for workload categories like compute, memory, accelerated computing, and storage.  The default option for purchasing is on-demand instances, which bills based on seconds or hours of usage but require no long-term commitments. EC2 instances are grouped together into families. Each EC2 family is designed to meet a target application profile in one of these buckets: General Purpose Instances General-purpose instances provide a balance of computing power, memory, and networking resources and can be used for everyday workloads like web servers and code repositories.  Compute Optimized Compute-optimized instances are best suited for applications that benefit from high-performance processors. Memory-Optimized  Memory-Optimized instances deliver faster performance for workloads that process large data sets in memory.  Accelerated Computing Accelerated Computing instances leverage hardware acceleration and co-processors to perform complex calculations and graphics processing tasks. Storage Optimized Storage optimized instances are designed for workloads requiring high performance, sequential read and write access to large-scale datasets.  When considering the cost, each instance type above can vary by region or operating system selections. The Hidden Cost of EC2 While AWS documents the cost of each instance type by region in their EC2 Pricing, getting to the actual price of using these services requires much more consideration. The first thing to consider is the status of the EC2 instance. Customers pay for computing time, disk space, and data traffic if in a running state.  Customers may still incur charges for unattached IPs and any active (not deleted) storage when in a stopped state. Unfortunately, many users mistakenly believe that stopping their servers will stop further costs from accruing, and this is not the case.  Another potential hidden cost of using EC2 is data traffic. AWS calculates data traffic costs by tier, based on a pre-defined volume with traffic falling below the volume incurring less cost and anything above paying more.  Because AWS charges for data traffic at the account level, many manual monitoring processes fall short in projecting actual costs. Considering how many AWS services comprise the AWS account of a large-scale program or company, it's easy to imagine how difficult it would be to monitor and control cloud spending in AWS. How to reduce AWS EC2 Spending Here are some of the best practices to reduce EC2 spending in AWS: EC2 Right-Sizing Many developers fail to consider right-sizing when spinning up AWS resources, but it's a critical component of optimizing AWS costs. AWS also defaults to many flexible but pricey options like On-Demand instances. Choosing a suitable instance type and service tier can significantly reduce cost without impacting performance.  EC2 Generation Upgrade AWS offers different instances tuned specifically for various workloads, as discussed above. When selecting an instance type, look for the latest generation options because they often provide the best performance and pricing.  Unnecessary Data Transfers AWS charges for inter-Availability Zone data transfer between EC2 instances even if they are located in the same region. Whenever possible, co-locate all instances within a single Availability Zone to avoid unnecessary data transfer charges.  Stopped Instances Stopping EC2 instances does not eliminate the potential for charges. Resources attached to stopped instances like EBS volumes, S3 storage, and public IPs continue to accrue costs. Consider terminating attached resources or the instance if it is no longer in use. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Optimize EC2 Cost with Anodot Anodot’s Cloud Cost Management solution makes optimization easy. It can easily connect to AWS, Azure and GCP to monitor and manage your spending. Even with multi-cloud environments, Anodot seamlessly combines all cloud spending into a single platform allowing for a holistic approach to optimization measures.  What makes Anodot for Cloud unique is how it learns each service usage pattern, considering essential factors like seasonality to establish a baseline of expected behavior. That allows it to identify irregular cloud spend and usage anomalies in real-time, providing contextualized alerts to relevant teams so they can resolve issues immediately.  Proprietary ML-based algorithms offer deep root cause analysis and clear guidance on the steps for remediation. Customers are already using Anodot to align FinOps, DevOps, and finance teams' efforts to optimize cloud spending.  Accurate forecasting is one of the central pillars of FinOps and cloud cost optimization. Anodot leverages AI-powered forecasting with deep learning to automatically optimize cloud cost forecasts and enable businesses to react to changing conditions before impacting cost. Rather than manually watching cloud resources and billing, your analysis teams will view cloud metrics with a business context in the same place as revenue and business metrics. That allows FinOps practitioners to optimize cloud investments to drive strategic business initiatives continually.

Amazon S3 Explained Amazon Simple Storage Service (S3) is an essential cornerstone of AWS and among its most popular service offerings. S3 allows tenants to store, secure, and retrieve data from S3 buckets on demand. It is widely used for its high availability, scalability, and performance. It supports six storage classes and several use cases, including website hosting, backups, application data storage, and data lake storage. There are two primary components of Amazon S3: Buckets and Objects. Users create and configure S3 buckets according to their needs, and the buckets store the objects they upload in the cloud. The six storage classes of Amazon S3 and the price differentiation While S3 prides itself on its simplicity of use, choosing the correct storage class isn't always as easy and can have a tremendous impact on costs. The free tier limits storage to 5GB in the standard class, but it's only available for new customers. AWS has six S3 storage classes above the free tier: Standard, Intelligent Tiering, Infrequent Access, One-Zone Infrequent Access, Glacier, and Glacier Deep Archive. Each one offers different features, access availability, and performance. Here is an overview of each class: Standard S3 standard storage is best suited for frequently accessed data. It's elastic in that you only pay for what you use, and customers typically use it for data-intensive content that they want access to at all times, from anywhere. Infrequent Access Storage S3 Infrequent Access Storage is best suited for use cases where data access requirements are ad hoc or infrequent and available quickly when needed. An example could be backup and recovery images for a web or application server. The cost model for infrequent storage is cheaper than standard storage but scales more each time you access the data. One-Zone Infrequent Access The "regular" Infrequent Access Storage ensures the highest availability by distributing data between at least three availability zones within a region. For use cases where data access is infrequent, lower availability is acceptable, but that still need quick retrieval times, One-Zone Infrequent Access Storage is the best option. S3 will store the data in one availability zone, but the cost will be 20% less than Infrequent Access Storage. Intelligent Tiering Amazon offers a premium S3 service called Intelligent Tiering. It analyzes usage patterns and automatically transfers data between Standard and Infrequent tiers based on access requirements. The selling point of this tier is it saves operators the labor of monitoring and transferring the data themselves. That said, it comes with a charge of $.0025 for every thousand items monitored. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Glacier Most customers use S3 Glacier for record retention and compliance purposes. Retrieval requests take hours to complete, making Glacier unsuitable for any use case requiring fast access. That said, the lower cost makes it ideal when access speed isn't a concern. Glacier Deep Archive S3 Glacier Deep Archive offers additional cost savings but carries further data access limitations. Deep archive is best suited for data that customers only need to access 1-2 times per year and when they can tolerate retrieval times upwards of 12 hours. How to Reduce AWS S3 Spending AWS S3 owes its popularity to its simplicity and versatility. It helps companies and customers across the globe store personal files, host websites and blogs, and empower data lakes for analytics. The only downside is the price tag, which can become pretty hefty in a hurry depending on how much data is stored and how frequently it's accessed. Here are some helpful tips for reducing AWS S3 Spend: Use Compression AWS bases so much S3 cost on the amount of data stored, so compressing data before uploading into S3 can reap significant savings. When users need to access the file, they can download it compressed and decompress it on their local machines. Continuously monitor S3 objects and access patterns to catch anomalies and right-size storage class selections Each storage class features different costs, strengths, and weaknesses. Active monitoring to ensure S3 buckets and objects are right-sized into the correct storage class can drastically reduce costs. Remember that you can leverage multiple tiers within the same bucket, so make sure all files have the right tier selected. Remove or downgrade unused or seldom-used S3 buckets One common mistake in managing S3 storage is users will delete the contents of an S3 bucket, leaving it empty and unused. It's best to remove these buckets entirely to reduce costs and eliminate unnecessary system vulnerabilities. Use a dedicated cloud cost optimization service rather than relying only on cloud provider tools The most important recommendation we can make to keep cloud costs under control is to use a dedicated, third-party cost optimization tool instead of relying strictly on the cloud provider. The native cost management tools cloud providers offer do not go far enough in helping customers understand and optimize their cloud cost decisions. - Disable versioning if not required. - Leverage endpoint technologies to reduce data transfer costs. Cloud Cost Management with Anodot Organizations seeking to understand and control their cloud costs need a dedicated tool. Anodot's Cloud Cost solutions easily connect to cloud providers like AWS to monitor and manage cloud spending in real-time and alert teams to critical cost-savings recommendations. Here are some of the key features: Anodot makes lifecycle recommendations in real-time, based on actual usage patterns and data needs. Rather than teams manually monitoring S3 buckets and trying to figure out if and when to switch tiers, Anodot provides a detailed, staged plan for each object considering patterns of seasonality. Versioning can significantly impact S3 costs because each new version is another file to maintain. Anodot continuously monitors object versions and provides tailored, actionable recommendations on which versions to keep. Many customers don't realize how uploading files into S3 can significantly impact costs. In particular, large uploads that get interrupted reserve space until completed, resulting in higher charges. Anodot provides comprehensive recommendations for uploading files and which files to delete in which bucket.  

AI-driven business monitoring could have prevented expensive glitch for Santander Bank As most people were preparing to celebrate the new year, the UK's Santander Bank was dealing with a crisis. On Christmas day, roughly 75,000 people who received payments from companies with accounts at Santander Bank received a duplicate payment transaction. The total damage amounted to £130m, and recovery in these situations is a painful process for both the bank and its customers. Making things even more complicated is that many of those who received the erroneous funds are customers of different banks. It's a big mess, but could it have been prevented? Preventing revenue critical incidents with AI analytics The right monitoring approach could have prevented or at the very least mitigated the incident at Santander Bank. It appears that most of the transactions happened in a short time, on the same day and perhaps in the same hour. Even if the bank was monitoring this type of use case, manual processes and traditional monitoring don't cope well when disasters quickly progress. AI models are far more adept at catching anomalies in real-time and alerting intervention teams before the damage gets out of hand. To respond to costly anomalies in real-time, organizations need systems that thoroughly understand the expected behavior of all components and transactions. Integrating machine learning and AI-empowered monitoring tools can aid this process by establishing a clear baseline of anticipated behavior across any business metric. Things like seasonality, customer behavior, and routine transactions help these monitoring solutions identify anomalies as soon as they occur. None of this is to say that it's a simple problem to solve. On the contrary, modern banking systems are incredibly complex, with integrated systems and transactions fragmented into multiple streams and sophisticated interactions with external partners (and competitors). Human observation of traditional dashboards can't keep up with all this complexity. How Anodot helps banking and payment companies detect revenue-critical incidents As the Santander incident demonstrates, glitches within the complex banking and payments ecosystem can lead to a significant incident costing companies millions and eroding customer confidence. In cases like these, timing is everything. AI and Machine Learning empowered monitoring is simply essential to oversee it all and ensure that incident responders receive real-time alerts so they can intervene before it's too late. Anodot is an AI-driven business monitoring solution that helps companies protect their revenue with a platform that constantly analyzes and correlates every business parameter. It catches revenue and customer impacting anomalies across all segments in real-time, cutting time to detect revenue-critical issues by as much as 80 percent. Anodot helps global banks immediately identify issues such as failed and declined transaction rates, login attempts, device usage and the transaction amount per type – all of which are valuable in detecting potential revenue and customer experience issues. For example, Anodot can immediately identify an unusual drop in transactions, which could mean a potential issue with a bank card or payment gateway. Real-time alerts ensure the issues can be resolved before impacting customers or revenue. Real-time financial protection for the world's most complex businesses Anodot's AI models provide proactive protection by catching potential issues immediately. It doesn't matter how complex the system is or how diverse the customer base might be. Problems are remediated quickly before minor nuisances turn into massive incidents. With Anodot, integrations are simple Managing financial systems is complicated enough. Anodot adds robust, automated monitoring capability with no painful integrations and no learning curve for your operators. Within minutes, Anodot's built-in connectors learn the expected behavior of every single business metric in a system and start monitoring all streams for abnormal behavior. Actionable alerts that suit your processes Not only does Anodot autonomously monitor billions of events for every metric in a company's revenue streams, but it distills them into singular alerts scored by impact and immediacy. Anodot notifies your teams via existing comms systems like email, Slack, PagerDuty, or even Webhook with minimal false positives. The rise of digital solutions, payment channels and transaction volumes has caused an exponential surge in the amount of data that must be monitored and managed. Leaders in the financial services and payments industries are using AI-driven technologies to monitor high volumes of data, from multiple sources, in an efficient manner.

The fintech market grows larger and more diverse each day. The financial news website Market Screener says the global fintech market will be worth $26.5 trillion by 2022, with an average annual growth rate of 6%. In Europe alone, the use of financial technology increased by 72% during 2020. Competition in this market segment is also on the rise. In the first eleven months of 2021, over 26,300 startup companies joined the fray—more than double the number of new entrants just three years earlier. As competition for customers’ engagement and loyalty heats up, players need to address much larger audiences, spread across ever-growing geographic regions. Monitoring and managing business operations becomes more challenging as the number of customer accounts and financial transactions continue to grow. Consequently, more solutions that solve fintech-related problems are needed. There is a focus on solutions that help fintech companies optimize all phases of their operations, from customer acquisition to payments processing and forecasting of payouts. In all aspects of the business, there is little margin for error or unexpected disruptions or downtime. Optimizing performance is key to succeeding in this industry. The explosion of activity spawned by all these companies is generating massive amounts of customer and payment data as well as information about the underlying business processes. Deep insights hidden within this data can help companies optimize their payment approval rates, transaction costs, and fraud mitigation, as well as retain customers and give revenue growth a boost. Time is Critical When Issues Arise in Payment Processes The fintech industry comprises many different sectors and industries, including retail banking, acquiring banks, payments facilitators, trading platforms, crypto-currencies, P2P payments, and more. While the industry is diverse, all players have at least one thing in common: a sophisticated technology platform that processes upwards of millions of transactions daily, often with calls to third party partners in the value chain. Throughout these platforms are points where data can be collected, measured, and monitored for changes, anomalies, and trends that can be indicative of an issue in operations or the business outlook. For example, the payment services company Payoneer closely monitors 190,000+ performance metrics in every area across the company. They are watching for any indication that something is even slightly off kilter with the business – such as, an unexpected decline in people registering for a new account, or a glitch in an API with third party software – in order to address issues quickly. Payoneer also monitors customer withdrawals in order to accurately forecast the funds that must be available for withdrawal, in the currencies that customers prefer, without over-allocating funds and losing the opportunity to use that money elsewhere in the business. Time is critical when it comes to identifying and resolving issues in payment processes. Consider what happens if there is a glitch in an API to a backend banking system that is crucial for payment approvals. If transactions can’t access this API, the payment acceptance rate will plummet and the fintech company is going to lose revenue during this unexpected downtime. Not only could the monetary losses be quite significant but the company could suffer reputational damage as well if customers can’t complete their payment activities. To make sure every payment transaction is completed as expected, operations teams must be able to find and fix payment issues as they’re happening anywhere along the end-to-end transaction path. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] Traditional Dashboards Can’t Keep Up Traditional dashboards and analytics solutions cannot keep up with the complexity and volume of payment data and channels. Operations staff and analysts have to manually dig into multiple dashboards to uncover (if possible) the root cause of a payment incident and then remediate the problem. Given that dashboards typically present historical rather than real-time data, analysts lose the ability to make decisions and act on something that is happening right now. This further delays mitigation and drives up revenue losses resulting from low success rates. To optimize the payment process from end-to-end, fintech companies need the benefits of AI-powered insights. Payment transaction monitoring is the practice of observing customer transactions and payment data (payment approval/failure, payment fees and rates, payment behavior, etc.) in production to ensure performance and availability. AI-based real time monitoring ensures that networks, applications, and third party service providers perform as expected. When transactions fail, it often means that a business’s most critical, time-sensitive applications fail as well. And even if a payment transaction doesn’t fail outright, performance degradation and data anomalies can wreak havoc on the user experience and signal problems in essential system functions. That’s why monitoring transaction behavior is just as crucial as monitoring critical servers and infrastructure. Get Real Time Insight into the Behavior of Payment Data Anodot’s highly scalable automated payment monitoring helps companies gain real-time insight into the behavior of their payment data. Using sophisticated autonomous machine learning, Anodot learns the patterns and behavior of each metric across the payment chain and discovers hidden relationships among the metrics. By understanding the expected behavior of metrics, Anodot detects when something anomalous happens, filters through the noise and false positives, and alerts on the issue before it can seriously impact customers or revenue. As an example, consider a global payments company that uses Anodot to continuously monitor payment approval rates across multiple dimensions such as country and currency. Anodot spots a drop in approvals for the Indian rupee. At the same time, the approval rates for the Indonesian rupee and the Pakistani rupee drop. The company recognizes that all three currencies are going through the same processing provider, indicating a problem with that provider that must be investigated. So, Anodot doesn’t just recognize a drop in payments; the system recognizes the correlations of the incidents that enables a conclusion about what changed in the business. This example shows how Anodot transaction alerts help companies react to changes that can affect payment optimization, but Anodot also delivers business insights that can be used proactively to really optimize payments. For example, merchants use smart routing of payments using simple rule engines. When Anodot notifies a merchant of a problem area resulting from routing payments in a specific way, the merchant can change the routing rules to go through a different processor. In fact, the alert can trigger the routing change automatically in order to respond even more quickly. This actionable alert allows the merchant to be proactive and avoid payment issues over the troublesome route. Issues Show Themselves as Anomalies If a picture is worth a thousand words, the graphic examples below show Anodot alerts to fintech clients that help them keep payments – and business – on track. [caption id="attachment_10744" align="alignnone" width="842"] A drop in approval rates in a particular payment gateway[/caption]   [caption id="attachment_10745" align="alignnone" width="1211"] A spike in partner API activity[/caption]   [caption id="attachment_10746" align="alignnone" width="1208"] Transaction count dropped to zero[/caption]   [caption id="attachment_10747" align="alignnone" width="878"] Drop in success rate for payment gateway[/caption]   Fintech companies can optimize their revenue with Anodot’s automated payment monitoring. Anodot’s AI-powered platform helps companies detect payment issues faster, intelligently route payments, optimize approval rates, and gain a competitive advantage in a crowded market.

You place your mug on your desk and boot your computer. Like every morning, you skim over various dashboards on one screen and sift through your email alerts on the other before you start pulling the regular reports. But this morning turns out to be nothing like other mornings. It is about to take a mean twist that will keep you from ever finishing your morning coffee. As your eyes run over the payment dashboard you realize there was a huge drop in transaction approvals overnight. You sit down slowly, rub your eyes, and secretly hope it’s a mistake. What in the world has happened? It takes a minute to get your thoughts together. When Dashboards Fail: A FinOps Nightmare Where is the failure? How much money is lost? Which merchants are affected? What will account managers say? A scenario like this can’t be ruled out in any company relying on dashboards to monitor payments and transactions. Dashboards help FinOps, BI, and commercial teams visualize business activities to ensure the payment ecosystem is running smoothly. However, as the amount of data to collect and monitor increases, the efficiency of traditional dashboards for digital payments monitoring becomes questionable. Why is payment transaction monitoring using a dashboard insufficient? 1. Siloed monitoring and disconnection of data sources and teams Business units monitor their data separately, gathering from various sources using different tools to cope with the abundance of data. This stifles collaboration and hinders comprehensive analytics efforts. In our little story, you are completely in the dark regarding the incident's causes or impact. You need to start involving programmers, IT, finance and product to get to the bottom of the issue. 2. Lack of granularity and context A dashboard monitors specific metrics separately but can not provide information on if and how specific data behavior might be related. You could be facing a software failure, configuration error, or malicious fraud. It’s up to you to connect the dots and gather clues. But time is critical. With every passing minute, the company loses money directly on unaccomplished transactions and indirectly on the time analysts and developers spend on the investigation. 3. Alert storms and false positives Alerts assume a respectable position next to dashboards in payment systems monitoring. However, the more data comes flooding in and the more agile it becomes, the higher the chances of false negatives and false positives. Alert storms are becoming normal. It’s easy to understand how a failure like the one in our example virtually vanishes among the influx of alerts. 4. Retroactive monitoring and static thresholds A dashboard monitors historical data. Thresholds and alerts are defined by past data behavior. Therefore, every time market or user behavior patterns change, the settings and definitions are no longer valid. Also, each metric has regular data fluctuations, which are overlooked by static thresholds. Just imagine the same story, with the drop in payment approvals remaining within the normal range because transaction numbers are usually the highest at the time in question. You wouldn’t even get an alert, and a lot more time would have passed till you realized something was wrong. It might even take a customer complaint to identify the glitch. 5. Extensive manual work Thresholds require manual adjustment. Additionally, each business unit focuses on its unique operational needs and goals. Extensive manual work is necessary to get the whole picture and understand the connections in data behavior. Teams end up filtering, organizing, prioritizing, and comparing data on excel files. The coffee in our example has long gone cold, and you are still busy downloading the relevant reports that help you figure out who needs to be informed about what and which steps to take. 6. Human errors So much manual work is not only time-consuming but also leaves much room for human errors. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] How to Save Time and Cost with Smarter Digital Payment Monitoring   1. Gain control over data volume and complexity The amount of data to monitor is challenging for dashboards but not for AI-driven platforms. Machine learning capabilities also add a level of autonomous data processing, identifying patterns, similarities, and connections in data from various sources in various formats. The insights answer actual questions that come up in the initial examination. It takes out a lot of guesswork and points you in the right direction. You’d be surprised at the amount of time you save and the stress you avoid. 2. Connecting and correlating data for higher resolution A tool that monitors 100% of your business data and isn’t limited to the preferences of a specific business unit can bridge the gaps between teams and data silos, provided it correlates all data points to identify connections in data behavior. Now imagine, the system synchronizes the plunge in payment approvals with data from other teams’ sources and detects a simultaneous drop in server activity. You see the position this puts you in? Instead of pulling the emergency brake and getting the entire company on board to solve the crises, so you don’t lose any more money, you’d tell IT to redirect traffic towards the affected server. Transactions would run smoothly within minutes again, and the relevant team could investigate and fix without the extra pressure of financial loss and loss of customers’ trust. 3. Accurate alerts - no more thresholds There’s a better way to identify anomalies in data than static thresholds. Your payment monitoring dashboard tool is happy as long as metrics are within the defined normal range. However, to get a precise assessment of what normal data behavior looks like, you need to consider time-specific and seasonal fluctuations. By recognizing regular patterns in every metric, the machine learns what is normal for which metric at which time. Real-time data monitoring becomes significantly more precise and eliminates irrelevant alert noise. 4. Real-time monitoring There are many factors causing transaction and consumer behavior to shift frequently: your business expands, your competitors change strategies, and new trends emerge. A monitoring tool that autonomously adapts to the new normal without the need to administer manual work can reduce uncertainty, time, and effort. With an AI-driven tool such as Anodot, you stop pivoting endless excel files and leave it to the tool to learn and make the necessary adjustments. Combining the above, you understand how much manual work becomes obsolete when you start using an AI-driven tool. At the same time, the chances of human error are reduced.   Payment Transaction Monitoring With Anodot   Anodot monitors all your business data and correlates data into actionable alerts classified according to severity and financial impact. Prioritization Anodot correlates real-time data and can therefore pinpoint the affected business areas and predict potential operational and financial impact. This allows for the prioritization of each alert. Moreover, you can rank alerts, so the system only shows what truly matters to you. Actionable Alerts Anodot goes yet a step further. In addition to receiving an alert, you can instruct the software to effect a defined action in a specific event. By configuring actionable alerts for critical incidents, the system prevents significant damage to the business. In other words, in case a server failure is detected, the system would redirect all traffic to an alternative server based on your API instructions. A Happy Ending with Anodot Let's rewind to the coffee mug on your desk. You quickly recover from your shock. The combined incident alert tells you that traffic from a specific vendor's server in Iceland failed at 3 a.m. The immediate necessary action - before investigating further - is to redirect traffic to another server. Oh wait, that's already taken care of by an actionable alert. You experience minimum financial loss because traffic was directed within minutes of the incident, customer experience remained unaffected, and operations keep running. What's left is to find what caused the failure in the server and fix it. But that's a job for the technical team.

Marketing teams routinely struggle with monitoring the performance and cost of their ad campaigns. Now, they have a solution that can be as easy as just a few clicks. We recently joined our partners at Rivery for a webinar demonstrating the new Anodot Markering Analytics Monitoring Kit. The kit allows users to track marketing campaigns in real-time and take the action needed to make the most of ad spend. Watch Webinar Anodot's Head of Product, Yariv Zur, joined Rivery's Solutions Architect, Alex Rolnik, to demo the kit in real time. Anodot is an AI-based business monitoring platform helps customers monitor, analyze, and correlate 100 percent of company data in real time. Rivery is a DataOps platform that extracts value from data sources, deploys data operations faster, runs transformation logic to structure data, and ingests data for usage in third party business applications, such as Anodot. In the webinar, Alex explained the challenges of implementing data models and workflows, including: Ingestion Transformation Orchestration Rivery makes these challenges more manageable with pre-engineered data workflows that enable instant insights, called kits. The benefits of kits include: Instant insights Structured to conform to industry best practices Maintenance free Can be used by anyone The new Anodot Marketing Analytics Monitoring Kit instantly deploys all of the data infrastructure you need — from data pipelines, to SQL transformations, to orchestration — to transform raw marketing data (Google Ads, Facebook Ads, etc.) into structured data (in this case, JSON) for Anodot. Joint customers can autonomously monitor their advertising performance while spending more time on mission-critical projects. Anodot monitors all your advertising channels for campaign anomalies, including: Clicks Impressions Conversions Reach Revenue Spend And much more. But of all the conveniences, the most important facet of the Kit is that it allows our mutual customers to take action fast.  

What is a Reserved Instance? An Amazon EC2 Reserved Instance (RI) is one of the most powerful cost savings tools available on AWS. It’s officially described as a billing discount applied to the use of an on-demand instance in your account. To truly understand what RI is, we need to take a step back and look at the different payment options for AWS. On-Demand – pay as needed. No commitments. Today you can use 1,000 servers and tomorrow it can only be 10 servers. You are charged for what you actually use. Spot - Amazon sells its server Spot. This means Amazon sells its leftover server space that it has not been able to sell without the use of a data center. The server is the same server that they provide with the on-demand option. The significant difference is that Amazon can request the server back at 2 minutes notice (this can cause your services to have an interruption). On the other side, the price can reach a discount of up to 90%. In most cases, the chances of them asking for the servers back is very low (around 5%). Reserved Instances - Simply put, you are committing to Amazon that you are going to use a particular server for a set period of time and in return for a commitment, Amazon will give you a discount that can reach as high as 75%. One of the most confusing things about RI (as opposed to On-Demand and Spot) is that with RI you don’t buy a specific server but your on-demand servers still get the RI discounted rate. What is being committed? Let’s look at the parameters that affect the height of the RI premise: The period: 1 year 3 year The Payment option: Full up-front Partial up-front No up-front (will charge 1st of each month) Offering Class: Standard Convertible Of course, the longer the commitment, and the upfront payment is higher, the assumption that Amazon offers is more significant. The above graph illustrates different RI options with respect to on-demand and recommending a specific RI that is tailored to each customer’s specific needs. In addition, when you purchase a RI, you are also committing to the following parameters: Platform (Operation system) Instance Type Region The RI is purchased for a specific region and at no point can the region be modified. To be clear, when we commit to Amazon on a particular server, we also have to commit to the operating system, region and, in some cases, instance size. Usually, after a few months the RI usage has improved its on-demand price and after the break-even point, every minute of running is considered “free” in relation to on-demand. Related content: Read our guide to aws pricing load balancer Related content: Read our guide to AWS Pricing Load Balancer Standard or Convertible offering With RI, you can choose if we want the Standard or Convertible offering class. This decision is based on how much flexibility we need. We can decide how long we are willing to commit to using the RI and we can choose both our form of payment and if we prefer to pay in advance. Obviously, the more committed you can be to Amazon (longer period, prepay, with less change options etc.) the greater the discount you will get. We still need to clarify the differences between Standard and Convertible. In the Offering Class Standard, you commit to specific servers while Convertible is a financial commitment. This means, you commit to spend X money during this time period and are more open to flexibility in terms of the type of server. Below is a comparison from the AWS website about the differences between Convertible and Standard. Now that we have a better understanding of what RI is, we need to understand how to know how much you should commit to Amazon and what kind of commitment meets your needs. As we know, we cannot predict the future, but we can make educated conclusions on the future based on our past activity. It is also important to note that when you commit to RI, you must run the particular server 744 hours a month (assuming there are 31 days). The discount only applies per hour so if you were to run 744 servers in one hour, only one server will get the discount. In addition, it can be difficult to understand how Amazon figures out the charge. For example, if at some point there are 6 servers running together, Amazon can decide to give each server 10 minutes of the RI rate and 50 minutes of standard on-demand rate. The decision which server gets the discounted rate is Amazon’s alone. If a particular account has multiple linked accounts, and the linked account that bought the RI did not utilize the RI at a given time, the RI discount can be applied to another linked account that is under the same payer account. RI Normalization factor Recently Amazon introduced a special deal for RI running on the Linux operating system. The benefit is that you do not have to commit to the size of the server but rather only to the server type. So assuming I bought m5.large but actually used m5.xlarge, 50% of my server cost would be discounted. The reverse is also true if I bought m5.xlarge but in practice, I ran m5.large it will get the discount (both servers will get the discount). Amazon has created a table, which normalizes server sizes, and it allows you to commit to a number of server-type units rather than size. In order to intelligently analyze which RI is best for you, it is necessary to take all the resources used, convert the sizes to a normalization factor and check how many servers were used every hour, keeping in mind that you will only get the discount for one hour of usage at a time. You also need to deduct RI that you have already purchased to avoid unnecessary additional RI purchases. Additionally, there will be some instances where servers may not run in succession and there is a need to unite between different resources. Lastly, it is also possible that certain servers may run for hours but do not complete a full month. Despite the above complexity and the need to analyze all of these factors, the high discount obtained through RI, may still result in a significant reduction in costs. Anodot’s algorithm takes all the above factors and data into account, converts the Normalization factor wherever possible, tracks 30 days of history, and uses its expertise to provide the optimal mix for each customer. Undoubtedly, RI is one of the most significant tools for reducing your cloud costs. By building the proper mix of services combined with an understanding of the level of commitment you can safely reduce your cloud costs by tens of percent. Optimizing AWS EC2 with Anodot Anodot’s Cloud Cost Management solution makes optimization EC2 compute services easy. Even with multi-cloud environments, Anodot seamlessly combines all cloud spending into a single platform allowing for a holistic approach to optimization measures. Anodot offers built in, easy-to-action cost-saving recommendations specifically for EC2, including: Amazon EC2 rightsizing recommendations EC2 rightsizing EC2 operating system optimization EC2 generation upgrade Amazon EC2 purchasing recommendations EC2 Savings Plans EC2 Reserved Instances Amazon EC2 management recommendations EC2 instance unnecessary data transfer EC2 instance idle EC2 instance stopped EC2 IP unattached Anodot helps FinOps teams prioritize recommendations by justifying their impact with a projected  performance and savings impact. Anodot learns each service usage pattern, considering essential factors like seasonality to establish a baseline of expected behavior. That allows it to identify irregular cloud spend and usage anomalies in real-time, providing contextualized alerts to relevant teams so they can resolve issues immediately. Proprietary ML-based algorithms offer deep root cause analysis and clear guidance on steps for remediation.

What is Business Analytics? Business analytics (BA) is the process of evaluating data in order to gauge business performance and to extract insights that may facilitate strategic planning. It aims to identify the factors that directly impact business performance, such as ie. revenue, user engagement, and technical availability. BA takes data from all business levels, from product and marketing, to operations and finance. Where analytics at the IT layer has a more direct causal relationship, at the business layer metrics are interdependent and their behavior regularly fluctuates – making business analytics an especially complex process. In this article we'll explore how the integration of AI in business analytics is critical as the volume and complexity of data continues to grow, challenging traditional methods of data analysis using BI dashboards. Why Business Analytics Matters? Regardless of size or type, organizations need to collect and evaluate data to understand how their business performs. Critical decisions, such as changing pricing structures, or developing additional products and features, follow an understanding of the numbers and their financial impact. According to Harvard Business School, 60 percent of businesses use BA to boost operational efficiency. For digital companies, this goes hand in hand with user experience. A smoothly functioning website or app is often a prerequisite for visitors agreeing to pay for goods. The study also says 57 percent of businesses leverage BA to drive change and strategy, helping identify hidden opportunities and detecting performance gaps that would be hard to grasp on intuition alone. In 52 percent of businesses, BA facilitates monitoring revenue, although the metrics involved aren’t always limited to financial data. The concept is to collect data from all business units and analyze their impact on financial performance. [CTA id="aa4483ba-9bbe-4bd5-8fc6-a2293a6f22cc"][/CTA] The Evolution of Data Analytics Until late 1960, business analytics relied on handwritten or typed business reports, and people used some form of a calculator to carry out statistical ascertaining. The motivation was gaining visibility into the company's activities and profitability by measuring, tracking, and recording quantifiable values, such as time and cost, and understanding how they relate. Computers made this a lot easier. With the onset of SQL and relational databases, collecting and analyzing statistical data moved to the next level. It was still only the beginning of modern data analytics. Data warehouses and data mining allowed for more data to undergo statistical analysis. Companies started to use the 'slice and dice' technique in which they break down large data sets into smaller segments to get a deeper understanding of specific points of interest. At this time, analysts still worked with historical data. Real-time data only entered the stage at the break of the millennium. When it became possible to analyze processes while they were happening, business analytics took on a much more significant role in digital business. Analytics could now be used as an operational tool and not merely as intelligence to back up strategies. Once again, though, the amount of data became unmanageable. The need to collect data from various sources presented additional challenges. Big data was born and, together with cloud computing, enabled businesses to scale. AI in Business Analytics Not too long ago, agile, interactive dashboards were the business analyst’s dream come true. But for growing enterprises, data analysis needs are outgrowing the capabilities of KPI dashboards. When the data analyst wants to investigate why a given anomaly occurs, they have to look at KPIs across data silos and manually identify relationships between them. Finding the root cause of an underlying issue can take a significant amount of time when analysts have to wade through dashboards as they work through a process of elimination. Using AI in business analytics allows organizations to utilize machine learning algorithms to identify trends and extract insights from complex data sets across multiple sources. AI analytics probes deeper into data and correlates simultaneous anomalies, revealing critical insight into business operations. Business analytics powered by AI can autonomously learn and adapt to changing behavioral patterns of metrics and is therefore significantly more precise in detecting anomalies and deviations. That means a significant reduction in false positives and meaningless alert storms and the surfacing of only the most business critical incidents. Unlike traditional BI tools, by detecting business incidents in real-time and identifying the root cause, AI business analytics helps you remedy problems faster and capture opportunities sooner. Benefits of Anodot’s AI-driven Business Analytics Using AI in business analytics solutions like Anodot, autonomously learn the behavior of 100% of your data and correlates metrics in real-time. Anodot monitors all metrics at scale, enabling operators to achieve complete visibility over the total of services, processes, partners, customers, and business KPIs. Leveraging Anodot’s AI capabilities, you can significantly cut both TTD and TTR and protect your revenue streams from disruption. Anodot's autonomous monitoring platform learns the behavior patterns of all backend and frontend customer experience data and correlates between metrics to create context and visibility. You can discover suspicious spikes or drops in engagement metrics or other user-experience-related parameters and act in real-time. In this example, an eCommerce customer was alerted to an unusual drop in approval rates for purchases paid for with PayPal. Monitoring user experience also helps you identify opportunities to optimize and implement them in your business strategy. Anodot allows you to take your business analytics to the top level. Take the next step towards fully autonomous AI in business analytics monitoring.

Organizations using Amazon Web Services (AWS) cloud traditionally leveraged Reserved Instances (RI) to realize cost savings by committing to the use of a specific instance type and operating system within the AWS region. Nearly 2 years ago, AWS rolled out a new program called Savings Plans, which give companies a new way to reduce costs by making an advanced commitment of a one-year or three-year fixed term. Based on first impressions the immediate understanding was that saving money on your AWS would be significantly simpler and easier, due to the lowering of the customer’s required commitment. The reality is the complete opposite. With Amazon’s Saving plans, it is significantly harder to manage your spending and lower your costs on AWS Plans, especially if you only rely on Amazon’s tools. 1. What are Savings Plans? To understand why the new Saving Plans significantly complicate cloud cost management, it is necessary to briefly review the two savings plan options. EC2 Compute Saving Plan The EC2 Savings plan is just a Standard Reserved Instance without the requirement of having to commit to an operating system up front. Since changing an operating system is not routine, this has very little added value. Compute Saving Plan With this product Amazon has clearly introduced a new line. The customer no longer has to commit to the type of Compute he is going to use. You no longer have to commit to the type of machine, its size or even the region where the machine would run, these are all significant advantages. In addition, Amazon no longer requires a commitment to the service that will use Compute. It does not have to be EC2, which means that when purchasing Compute Saving Plans, using Compute in EMR, ECS EKS clusters or Fargate can also be considered a guarantee and you will receive a discount. In RI Convertible, to get a discount on a different server type, rather than the original server for which we purchased the RI an RI change operation was required. With the new Compute Plan, it is not necessary to make the change and the discount is automatically applied to the different types of servers. The bottom line is that you commit to the hourly cost of computing time, however, you choose whether the commitment is for one or three years and how you want to pay i.e. prepayment, partial payment, or daily payment. At this stage, it sounds like Compute Saving Plans would simplify and lower your costs, as the commitment is more flexible. However, as we stated above, the reality is much more complex. 2. Are Amazon’s Saving Plan Recommendations Right for Me? Let’s start with the most trivial yet critical question, how do I know the optimal computing time for me? Amazon offers you recommendations of what your computing time costs should be and what they feel you should commit to buying from them. It’s interesting that Amazon offers these recommendations considering they don’t share usage data with their users. So what is this recommendation based on? Amazon is recommending to their users to commit to spend hundreds of thousands of dollars a month without any real data or usage information to help users make an educated investment decision. Usually when people commit to future usage they do so based on past usage data. The one thing that Amazon does allow you to do is choose a time period on which their recommendation will be based on. For example, based on usage over the last 30 days of a sample account, Amazon recommended a spend of $ 0.39 per computing hour. The IT manager can simply accept Amazon’s recommendation, but with no ability to check the data the resulting purchase could cost the company a significant amount of additional and unnecessary money. In the example above, there was significant usage over the last 30 days, however a couple of weeks prior to this, there may have been a significant change, such as a reduction in server volume and/or a RI acquisition and therefore the recommendation here should have been particularly lower. This is even truer if Saving Plans had already been purchased and had earned an actual discount. 3. How do I know which savings plan is best for my company? On this large and significant vacuum Anodot for Cloud Cost can provide a lot of value. Using Anodot, you can see your average hourly cost per day for the last 30 days. Since the Saving Plan estimate does not include the Compute hours already receiving an RI discount, Anodot only displays the cost of Compute on-demand. It is also critical for a user who has already purchased and is utilizing Saving Plans to know how this impacts his costs before making any additional commitments. Anodot shows the actual cost of each individual computing hour over the last 30 days to enable educated decisions that can impact significant multi-year financial commitments. Anodot utilizes its unique algorithm and analyses all your data to deliver customized recommendations on what will be the optimal computing time cost that you should actually commit to. It is important to note that when purchasing a Compute Saving Plans, it is not possible to know at the time of purchase what your exact discount will be. The actual amount of the discount can be only estimated in all cases other than RI. This uncertainty is due to an additional complexity that exists in Compute Saving Plans. Each type of server receives a different discount, so in practice the discounts that you receive depends on the type of server you actually run and if Amazon’s algorithm chooses to provide that type of server with the Saving Plan discounts offered.