You’re generating data right now. You clicked on this blog. You’re reading this blog. That next action you take? You’re giving us data. (Thank you.) This is just in the past 10 seconds. Imagine how much you generate in single day. Now multiply that by ten thousand, and you’ve got the enormous amount of data an enterprise generates in a single day.
The good news is, you can leverage all these analytics to delight your customers and improve operational efficiency. The bad news is, the data needs to have meaning in order for you to do anything of value. And with the amount of data growing bigger as you read this (You’ve stayed on this page for 30 seconds. Thanks for the data.)—making sense out of it can be a challenge.
You clicking on and reading this blog is generating performance data about this content. It tells me that you liked the headline enough to click on it. The actionable insight it gives me is to write similar blog headlines. There are many different types of data around how your IT ecosystem is performing as well: applications, databases, devices, servers, microservices, clouds, and so much more. If there’s an outage on the app that your entire business relies on—such as an enterprise that relies on their website to sell their products—you need accurate data to ensure that the outage gets remediated ASAP. In fact, Garter sites that bad or unclean data is the primary reason why 40 percent of all business initiatives fail to reach their targeted benefits.
Because if it’s dirty data, finding and fixing that outage fast—before it impacts your customer—is much more difficult. And if your customer is impacted by the outage, imagine what it does to your bottom line. In fact, recent research from Gartner shows that poor data quality is responsible for close to $15m in losses annually.
Now, how do you get accurate data so that you can find and fix problems faster so that your customers are not impacted? Well, in order for all of this big data from every component of your IT ecosystem to provide actionable insights, it should be normalized (cleaned) into a common data model or data lake. Because if there is an outage in a business-critical application and the data is saying the root cause of the outage is in the application when it’s really a server error—how is your ITOps team going to fix the outage?
As enterprises get overwhelmed with so much data, many are turning to artificial intelligence for IT operations (AIOps) to help. Because, as enterprises begin to integrate AIOps into their business strategy, they’re realizing its uses expand and supplement conventional application performance monitoring, network performing monitoring, and diagnostic tools.
Sure, you have an application performance monitoring tool like Dynatrace monitoring your applications. And a network monitoring tool like Solarwinds making sure your servers are up and running, but what is keeping all this data together and making sure it’s accurate? 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. That’s where ScienceLogic comes in. (But we’ll get to that later.)
But because of gaps, errors and the lack of consistent formatting conventions and metrics, getting the most out of your big data—and providing the best experience to your customers—is quite a challenge. AIOps helps to close those gaps and delivers data in a single format, so that it’s possible to derive the insights necessary to implement continuous process improvement and, ultimately, turn AIOps into a platform for process automation.
One of the most significant benefits to AIOps is in giving enterprises the ability to master the challenges inherent with big data, and to trust the accuracy of their big data. Because now that you’re working with the right data, you have the power to automate the “routine work” that your ITOps team spends the bulk of their time doing in order to find and fix the problems. By automating these processes, you’re moving at the speed of business—giving your customers the best experience possible.
And that’s how ScienceLogic can help.
- The ScienceLogic SL1 Platform collects and stores all your big data collected by your all of your 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.
- And 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 (AIOps).
We are also proud to be a Bronze Sponsor at Telstra Vantage in Melbourne, 3-5 September—where topics like big data and AIOps will be discussed in breakout sessions, booths, and more. We’d be happy to discuss with you one on one how AIOps and SL1 can help your enterprise transform ITOps—and customer experience—with big (accurate) data.