SaaS monitoring is the practice of tracking, analyzing, and managing the performance and utilization of Software as a Service (SaaS) technologies. The goal of SaaS monitoring is overall optimization. Specifically, SaaS monitoring enables you to:
Without monitoring your varied SaaS apps, you cannot be sure that you’re getting what you pay for or that you’re not wasting resources. And you certainly cannot truthfully respond to the questions that your leadership is asking.
When it comes to IT and cloud systems, there are many areas to monitor. An area of IT monitoring that might go by the wayside is SaaS monitoring. That’s because SaaS apps are often sold with the promise of “worry about nothing, we’ll take care of everything for you.”
The truth isn’t that clear. That’s because of the inherent nature of SaaS.
The SaaS delivery model allows users to access technology functionality on a subscription basis. This model shares responsibility between the cloud vendor and you, the business users.
(Related reading: IT spending trends.)
This convenience comes with an expectation — and it’s a big one: your organizations is expected to maintain visibility into your own processes and digital assets that are used and engaged with a SaaS service. Why? There are many reasons, including:
The challenge in realizing these objectives is two-fold: limited visibility into SaaS operations and lack of risk management controls.
Since the cloud technology runs on third-party servers, the vendor offers its customers only limited visibility into the underlying hardware operations. This makes it difficult, if not impossible, for business organizations to identify the infrastructure nodes and traffic workflows. Without this information, you as the customer cannot…:
But there’s still a lot that you can monitor at the application layer.
Let’s review the key components of SaaS monitoring strategy and tooling that can help your organization optimize cost, performance, and security of your SaaS solutions.
All sorts of data and information — including KPIs, metrics, logs, and events data — is generated across the network. This can be collected using agent-based or agentless monitoring functionality.
Here, you may opt for an integrated data collection pipeline, considering the the siloed sources of data generation.
In some cases, the SaaS vendor may provide API access to its log aggregation tool. If so, you can corelate this information and enriched with application performance metrics, logs, and traces within your own network.
Containerized systems run application components in an ephemeral state: these components are packaged and loaded into the infrastructure environment. Once the computing job is completed, the container resource allocation is decommissioned and made available for its next use case.
In the case of SaaS applications, any components running in such a state, or interacting with other container applications, must be discovered and analyzed in real-time.
In a software-defined infrastructure environment, hardware resources are allocated dynamically. The SaaS application is decoupled from the underlying hardware. Any change in workload distribution must be traceable and reachable by dependent applications, components, and systems.
Perhaps the greatest uncertainty surrounding SaaS solutions, and cloud-based technologies in general, relates to forecasting.
IT and business executives seek answers to challenging questions:
To address the technical concerns surrounding forecasting, start by identifying the metrics and data sources most relevant and impactful to…
For strategic concerns surrounding vendor relationships, service quality, and pricing, you should be able to forecast your long-term business goals and usage requirements. Use this knowledge as a basis for SLA metrics selection and pricing commitments.
(Related reading: third party risk management, explained.)
Historical information and traceability is important to enforce compliance controls and audits. SaaS monitoring may rely on large volumes of log data to push real-time alerts on application performance and security. This log data is also necessary to maintain usage history and context.
SaaS monitoring can help with distributed tracing functionality for root cause analysis using logs that lead up to an incident.
Centralized log management can take monitoring information for compliance related analysis in the future or real-time anomaly detection. This contextual knowledge can help your organization to:
SaaS monitoring is all about creating real-time, actionable insights. These insights should be presented with intuitive dashboards that synchronize data from distributed information sources.
Integrated APIs that fetch information in real-time into a centralized log data repository, where it is standardized and preprocessed before running analysis is therefore an important last step of a SaaS monitoring pipeline.
SaaS monitoring is focused on the access of raw data, but it still needs to be transformed into actionable contextual knowledge that is unique to your business use case.
An extended functionality of SaaS monitoring tools may incorporate AI-driven functionality to identify patterns within your SaaS monitoring data. These patterns can help manage SaaS costs and performance based on changing external metrics and usage trends — use this knowledge to plan scalability and resource allocation for your SaaS technologies for improved end-user experience and therefore, business outcomes.
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