What is your primary use case for Splunk ITSI (IT Service Intelligence)?

I recommend Splunk ITSI (IT Service Intelligence) for large enterprises and the government sector.

Splunk ITSI (IT Service Intelligence) is primarily used for managing alerts and events. It helps me monitor different APIs in inbound and outbound scenarios and triggers alerts. The tool is primarily used to handle threat intelligence and manage event alerts, despite certain limitations like false positives.

I worked on multiple projects using Splunk ITSI for log monitoring, including monitoring mobile data usage for a telecom company, working with an insurance company and a retail application, and monitoring payment applications for a bank.

We are using the solution for correlation searches. We've integrated Splunk with ServiceNow. We're creating aggregation policies to trigger actions in ServiceNow. We use it with the ServiceNow add-on. When something happens in ServiceNow, it's correlated to ITSI as well.

I use ITSI for different companies but with the same objective: to correlate alerts from different sources and assess them according to multiple frameworks. For example, I can combine the alerts from different sources into a single episode. The analyst can resolve the issue without looking in multiple places to get the necessary information.

We use ITSI in the health industry. In the UK, the NHS currently uses ITSI as one of its monitoring sources of information. In ITSI, service components are based around each area of the NHS. For any solutions that have been digitally transformed and require monitoring related to our vaccination campaigns, the logs are ingested through Splunk and monitored through ITSI.

I have experience utilizing Splunk ITSI in financial institutions and federal government settings. As a Splunk administrator at a bank, I focus on the platform's administration and development aspects. We are migrating from an on-premises environment to the cloud, leveraging Splunk ITSI to provide a unified view of the client's infrastructure. Through ITSI-generated reports, we are developing a strategic roadmap to guide our clients' IT journey.

It monitors every level of infrastructure in our environment, including remote locations across the world.

We have a couple of different use cases including incident management, correlation, and mapping out incidents.

We use the solution to monitor throughout the enterprise. We get alerts and create incidents and use it in our ticketing tool.

We use Splunk ITSI to monitor the different stages, spaces, and processes of payment operation.

Splunk ITSI is our platform for data ingestion from various sources. We leverage it to manage Kubernetes configurations, licenses, reports, dashboards, and user permissions. Additionally, we utilize ITSI for field extraction and data model retrieval.

We use Splunk ITSI to collect the infrastructure metrics and visualize them.

It has enabled effective monitoring, allowing for a comprehensive view of the growing complexity within the IT infrastructure.

We use the solution for event management, observability, application management, application performance management, anomaly detection, problem detection, and creating different rules for the anomalies for different events. It's application performance monitoring. The entire area of service is managed by ITSI, and offers automated detection and everything.

I work for a consulting company that contracts with an organization to provide operation center services. We use Splunk ITSI as one of our key centralized monitoring tools for the organization. Our goal is to collect data from both the organization's centralized database, Spine, and their cloud platforms, such as AWS and Azure, and send it to Splunk for monitoring. Splunk then creates reports, alerts, and dashboards that we use to visualize the data and make the most of it.

We have medical use cases. We monitor batch processes for our medical system. We batch-process data ingestion from our data warehouses just to make sure they're performing appropriately. If there's an outlier we'll report it or create an incident.

We have some business-oriented monitoring. The technical components are aggregated to business services up to a certain level. We could do a lot more, but this is what we are doing currently.

I have used Splunk ITSI to build a lot of glass tables and set up thresholds. We have also used MLTK for machine learning, predictive analytics, and anomaly detection. We use MLTK, which is an external application. We can get notified of issues well before the time to take proactive action.

We utilize Splunk ITSI to enhance our IT operations within our infrastructure. Our goal is to monitor only the most critical KPIs. Additionally, we have access to a detailed overview of the KPI services and entities, allowing us to identify issues in real time. We deploy Splunk ITSI both on-premises and in the Splunk cloud. We implemented infrastructure monitoring using ITSI to track various aspects such as latencies and specific components like CPU and memory. I can now provide detailed information about the specific cause of CPU-related issues. The problem lies in determining the process through which we can obtain a high-level overview of our services. When we delve deeper, we have access to numerous details to identify the KPI responsible for disrupting the service application. I can now explore ways to monitor its performance and locate the service in question. With ITSI, we can receive alerts and easily navigate to the precise location to resolve the problem.

There are multiple use cases, which include heat maps, glass tables, and predictive analysis. The first one is mainly related to heat maps. For example, if you want to monitor the health of a server, you can prepare heat maps for that. When you set up any kind of alerts, they can get missed because people are too busy to check their emails. With these heat maps, the color changes automatically. The Cron job runs behind the scenes, and you don't need to run them manually. You can also set up a glass table in ITSI for the architecture. For example, a setup like Amazon would have web services, databases, queues, and other things. For the purchase and other things, it has to connect to the external world, so you need to place the complete architecture over there, and you can assign the threshold value. If there is an issue with any of the points, for example, there is an issue with the connectivity of the database, the heat maps would change in color, which helps you to easily identify that there is an issue. It also has a concept called predictive analysis. For example, your WhatsApp chat backup happens every 24 hours or 7 hours, but you cannot predict how much bandwidth it's going to use during the backup. It might even use 100% of the bandwidth. You cannot set a proper threshold. In such cases, you can use predictive analysis. It'll analyze the data patterns, and based on the data pattern, it predicts if everything is good or if something is bad. It can predict if something is going to fail. You can have an integration with the ticketing tools. For example, if something happens on any server or PC and you've directly integrated the tickets from Splunk to ServiceNow, it's automatically going to create a ticket in ServiceNow. There's also a concept of episode review wherein it groups the alerts so that there's no ticket spam in ServiceNow. For example, if you are monitoring a server and it's down, there might be 10 to 20 alerts, which would create 10 or 20 separate tickets and spam your ticketing system. In such cases, you can use the episode review feature. It will merge all those tickets into one and include all the details in that.

Splunk ITSI is used to analyze data and create alerts. This helps us to maintain our security best practices.

Our use cases for Splunk ITSI have been created around our critical services like payment gateways.

We use this solution both on the cloud and on-premises. We are currently using the most recent version.

We primarily use the solution for monitoring our infra. We use it for monitoring the potential application, depending on what the data source ingestion is. There are many use cases. Based on the data source, we can know the best recommendation use case to provide to the customer. For example, if you are ingesting data from the firewall, you can see any traffic from the firewall itself.

I use Splunk IT Service Intelligence (ITSI) for SIEM.

We are a solution provider with many technologies. We use Splunk to customize solutions with Splunk. For example, we try to give our customers a great visualization experience. And sometimes we develop on the Splunk platform, like JavaScript, to provide the customers a better visualization. We also implement ITSI. In-house we can implement Enterprise Security.

I work for the Royal Bank of Canada. I work in a group called Investor and Treasury Services IT. We take care of all the IT systems within the Investor and Treasury Services arm, which is a global unit. My role is to ensure that we have the visibility and capabilities to ensure our systems are resilient so we can resolve any problems that may arise very quickly, and move on. My role generally deals with everything from application performance management to maintenance automation. Overall, my single goal is to increase the resiliency of our applications and gain better insight into how our operations are working from an IT operations and application maintenance perspective.

We use ITSI mainly for IT Infrastructure Operations Monitoring. The service model health scores allow us to identify when KPIs are starting to impact our services and to proactively manage our environments. To date, we have leveraged this data within Splunk to enable alerting so that we can solve incidents in real-time, but we are growing into our usage of the ITSI model for predictive modeling of our environment. Our infrastructure includes commodity hardware, mid-range, mainframe, on-premise data center, and cloud offerings. (Please note that these views are my personal opinions and not those of my employer)

The primary use case of this product is for infrastructure monitoring, and involving machine learning with IT-related scenarios.