To increase the pace of innovation, boost operational performance, and ensure business stability, organizations need to upgrade the ways they manage their IT Ops procedures and strategies. One emerging technology that is gearing up to become the next big thing in IT operations management as it changes the way infrastructure is managed is Artificial Intelligence for IT Operations (AIOps).
Defined by Gartner as the “solution that uses artificial intelligence and machine learning to automate tasks
and processes that have traditionally required human intervention”, AIOps is gaining momentum in the IT space as it enhances performance monitoring and promotes greater visibility into the network and operations infrastructure. But what exactly is AIOps and why should businesses leverage this emerging technology?
SourceForge had the chance to speak with Dave Link, the founder and CEO of ScienceLogic, to discuss the value of AIOps for IT operations management. Link also offers insights on how organizations can achieve successful AIOps strategy and shares how businesses can seamlessly maintain and ensure data quality and enhance operational readiness by taking advantage of ScienceLogic’s SL1 platform.
Q: Please share with our readers a brief overview of ScienceLogic. When was the company established and what types of organizations do you currently serve?
Dave Link, the founder and CEO of ScienceLogic
A: ScienceLogic was established in 2003 and currently serves global enterprises, managed service providers, and public sector agencies.
Q: The old way of doing IT Operations doesn’t cut it anymore as more businesses enter the digital transformation era. One approach that is promising to revolutionize business IT is AIOps. Gartner defines AIOps as the combination of big data with machine learning. Can you please tell our readers more about it? What does the definition mean in practice?
A: At ScienceLogic, we think the definition of AIOps is more nuanced than just combining big data with machine learning. The quality of data matters, because if you’re using inputs that are impure, then the old adage of ‘garbage in, garbage out’ applies. To achieve the type of results that AIOps promises, practitioners need data that is real-time, clean, and contextualized. When properly designed and applied, AIOps helps organizations discover and remediate issues within the application and infrastructure layers of their IT environments before they adversely affect the bottom line.
Q: Why is AIOps gaining momentum and what problems does it seek to solve?
A: Developers have proven extremely agile at adopting and incorporating the latest technologies into their applications, but operations have been slower, resulting in a gap between developer vision and practical implementation. IT operations teams today do not have the skills or bandwidth to tackle everything developers throw their way.
Current tools like APM provide only a limited sliver of insight and do not show crucial data in relation to adjacent infrastructure or application layers. IT operations teams today are up to their ears in IT monitoring tools, yet are still unable to make sense of their data because these tools operate within silos and exist as a multi-vendor patchwork of technology.
Enterprises have tried to mitigate these problems by using infrastructure and application data to inform IT and business decisions. The key challenge to understanding how the performance of critical applications is impacted by underlying infrastructure has been the lack of context. Being able to get data from one location to another or to interact across operational domains is crucial and can be solved with intelligent integration and automation into a common data model.
Q: In your whitepaper “Success Factors for AIOps – A 5-Step Approach to Operational Readiness,” you identified the essential ingredients behind a successful AIOps strategy: high quality, real-time data, with full context. How can organizations maintain and ensure data quality?
A: Earlier, we mentioned the necessity for AIOps to have data that is real-time, clean, and contextual. And while that sounds easy, we understand that it is worth examining how to maintain and ensure data quality in such an ephemeral environment. In our upcoming whitepaper, we lay out a five-step process that includes:
- Data Collection. Continuously discovering and collecting data from every source within the IT environment;
- Data Preparation. Ensuring the data collected is complete, duplicates are removed, and it is all labeled consistently;
- Data Enrichment. Adding meta-data to related devices or service metrics to provide the context into relationships;
- Data Analysis. Reducing the amount of illegitimate, non-actionable data and consolidating where possible;
- Data-Driven Action. Once the data has been collected, organized, and infused with context, can automate and streamline workflows necessary for business agility.
The paper dives deeper into each step and lays the groundwork for obtaining high-quality data.
Q: What is your advice for organizations looking to adopt AIOps? How should they go about choosing the right AIOps platform for their needs?
A: Enterprises should look to AIOps as a solution, not a product. It should be seen as a journey that takes years to fully accomplish but yet creating business impact along the way. AIOps is an ecosystem play and must emphasize an architecture that is capable of ingesting a variety of data sources, high volume input and output of data, and capable of supporting rapid velocity in development cycles. Successful organizations will emphasize the automation and real-time nature of data preparation as they will on the sophistication of the algorithms that power outcomes.
Q: Tell us about ScienceLogic’s SL1 platform. How exactly does it enable organizations to see, contextualize, and act upon their operational data?
A: Our SL1 platform is the first context-infused AIOps engine that enables Ops to move at the speed of Dev and provides enterprises with operational data that is real-time and contextually rich. By creating context-rich training data that feeds modern artificial intelligence and machine learning platforms, SL1 helps enterprises automate issue identification and resolution, which are essential for building resilient digital experiences.
SL1 focuses on providing the training data for AIOps through a “See, Contextualize, Act” framework. This allows enterprises to deploy automated AIOps solutions based on their data in a coordinated, automated, and holistic way, beyond what traditional solutions provide. SL1 automates real-time discovery of applications and infrastructure across IT silos and multi-cloud environments, then uses automated topology maps to establish real-time relationships between disparate data sets to drive context. The result is automated issue discovery and subsequent remediation across a diverse range of technologies including configuration management database (CMDB), DevOps, and application performance management (APM).
Q: What makes ScienceLogic SL1 stand out over other similar solutions in the market today?
A: SL1 leverages patented-innovations, PowerMap™ and PowerSync™, to grasp how the performance of mission-critical applications is impacted by the underlying infrastructure, a common disconnect in modern performance management tools. Multi-dimensional topology maps, known as PowerMap, enable real-time service health views that inform, analyze and act on the health of the business service. PowerSync, on the other hand, is how we collect and share our common data model fueled by context, with the IT ecosystem to accelerate automation.
Enterprises get universal visibility in one single platform with all the data and functionality they need. Users know exactly what is happening with IT operations at all times. Enterprises can also apply real-time context to their data, thereby helping create efficiencies with real-time, data-driven automation that allows systems to do routine, repetitive work and automate responses to error conditions or streamline incident handling. Most importantly, enterprises can monitor any technology, any vendor, anywhere. SL1 is designed to extend and tune to meet specific customer needs easily.
Q: What are the key trends and technologies that IT leaders need to watch out for?
A: Nearly every business must be digital, or they will become irrelevant. Yet, operating a digital platform is more complex due to the necessity of business agility and the ephemeral state of IT that they are using to achieve that agility. It started at the infrastructure layer but evolved quickly towards DevOps movements emphasizing service development and rapid release.
Today’s performance management systems must provide operational insights at the mission-critical service layer, derived from a deep understanding of dependency mapping between applications and its underlying infrastructure. SL1 delivers on this promise, changing the future of IT operations with the help of AIOps.
Q: Are there any upcoming releases/products that you would like to tell us about?
A: We are focused on SL1, which we launched in May of this year, but we recently received $25 million in investment funding from Square 1 Bank to promote our global expansion, new product development, and continued growth into new market segments. We were also recognized by Enterprise Management Associates as one of the top companies leveraging AI in IT operations and recently named to Deloitte’s Technology Fast 500 Fastest Growing Companies.
About ScienceLogic
ScienceLogic is the leader in IT operations management. Headquartered in Reston, Virginia, ScienceLogic helps today’s organizations by providing modern IT operations with actionable insights to help predict and solve problems faster and more efficiently. ScienceLogic’s cutting-edge AIOps platform sees everything across cloud and distributed architectures, contextualizes data through relationship mapping, and acts on this insight through integration and automation.