Picture this: You’re the VP of an enterprise whose main source of revenue is generated by its website. You’re sleeping peacefully in your bed, when your phone rings at 4 a.m. It’s your IT director. “The web site is down!” he exclaims with great urgency. Because even though it’s 4 a.m. on the east coast, it’s 9 a.m. in the UK—where 45% of your potential customers are up and ready to purchase. You are losing money by the second. What do you do?
At times like these, finding and fixing problems with speed and accuracy is crucial to your business. And it’s a huge challenge. But what if the legacy monitoring tools you’re relying on to help remediate and resolve outages are not enough? Well they’re not. And here’s why:
Reason #1: Visibility: Technologies are rapidly innovating, and legacy monitoring tools have not and cannot keep up.
To remain competitive today, successful organizations continuously extend their technology footprint to include cloud, virtual machines, microservices, containers, Kubernetes, and more. It’s no longer good enough to just provide visibility in the data center. In fact, 84% of enterprises have a multi-cloud strategy and 58% have a hybrid strategy. But unfortunately, many legacy monitoring tools have little to no cloud coverage. They were made for data-center-centric environments–not a mix of clouds, containers, and microservices. So, how do you see what is happening in your clouds and how it relates to what is happening in your data center–and more importantly, your business?
Reason #2: Big data: You have lots of different monitoring tools monitoring lots of different things. But you need something to pull it all together.
If you are a VP of an enterprise, you’ve probably got a lot of different tools monitoring a lot of different technologies and a lot of different platforms. Maybe you’ve leveraging ServiceNow for discovery, device management, and CMDB. And you also may be using AppDynamics for application performance monitoring, and Oracle for database monitoring, just to name a few. All these monitoring tools produce a substantial amount of big data. But what’s bringing all of this data together so that it means something?
In order for all of this big data from all of the components that make up your enterprise to provide actionable insights, it should be accurate and normalized into a common data model. Because what kind of results do you expect to get if the data you’re basing your actions on is wrong? Having one tool to collect, merge, and store a variety of data in an accurate and normalized data lake helps ensure that you’re sharing the right data across teams so that they take the right steps to remediate the problem.
Reason #3: Analytics-driven automation: In the ideal world, you want to find and fix problems before your customers are ever impacted or even aware that there’s an issue (i.e. an outage or performance degradation). And to do that, you want predictive capabilities that enable you to proactively identify looming issues.
At the same time, you want to automate the “routine work” that your ITOps team spends the bulk of their time doing in order to diagnose and solve the problem. And automation always starts with data—the right data. The right data that is actionable. Adding context to the data is key. It doesn’t make sense to learn the behavior of containers that come and go within minutes. On the other hand, it makes total sense to learn the behavior of the application or service that the container supports. By gaining full-service visibility and understanding the context of your entire IT ecosystem, you can better identify and resolve problems. And automate the steps for doing so. And you can’t find it with your legacy monitoring tools. Sure, your standalone APM tool is a great tool for monitoring the performance of your applications—but it’s just as important to understand the infrastructure impact on applications in order to find the root cause of an application problem so you can eventually automate.
3 Reasons Why ScienceLogic Can Help Keep Your Critical Business Services Running Smoothly
- 1. ScienceLogic’s SL1 platform is a unified monitoring platform that gives you visibility into your entire IT environment—the cloud, servers, network, storage, applications, and services.
- 2. SL1 collects and stores all your big data collected by your legacy monitoring tools and more —in a clean and normalized data lake—bringing context and meaning to your data by helping you see how each component works together to support your applications and business services.
- 3. Now that you have accurate data with context, SL1 helps you integrate and share data across your entire ecosystem in real time—applying bi-directional integrations to automate manual data and process workflows.