AIOPs have become inevitable for the success of B2B SaaS businesses. In this blog, we look at how AIOPs can help in digital transformation.
A look at the organizational landscape shows that many companies, especially B2Bs, are moving towards digital transformations – using IT tools and technologies to boost organizational performance. The primary motivation for digital transformation (DX) is the many benefits of hybrid and multi-cloud infrastructures.
On the other hand, Artificial Intelligence (AI) has gained a lot of traction in recent times. Its application can be found in almost all business operations including IT. The use of AI tools and processes in IT operations helps boost productivity and accelerate performance. The use of AI for IT Operations is referred to as AIOps. The term AIOps was coined by Gartner in 2017.
AIOps is an alliance between data analytics, DevOps, machine learning (ML), and artificial intelligence (AI). From breaking down the data silos to ensuring that data and insights are easily available across the organization, AIOps plays a significant role in leveraging digital technologies. Thus, it plays a key role in accelerating the digital transformation (DX) of an organization.
In this article, we will discuss the role of AIOps in digital transformation.
AIOps – Artificial Intelligence in IT operations – refers to how an IT team handles data derived from an application environment.
The term was coined in 2017 by Gartner. They have defined AIOps platforms as
‘The ones using big data, modern machine learning, and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation, and service desk) functions with proactive, personal, and dynamic insight.
Earlier, IT operations within organizations used multiple dashboards to carry out different activities. However, having multiple dashboards meant silos across various infrastructures, applications, networks, databases, etc. This created chaos between the teams. This problem has been solved with an AI-assisted single dashboard. Further, AIOps can be used for automation, IT service management, performance analysis, anomaly detection, and event correlation and analysis.
AIOps also uses algorithmic analysis to assist IT Ops, SRE, and DevOps teams to improve speed and efficiency. This way, the teams get the ability to detect digital-service errors at an early stage so they can be resolved quickly.
Current-day IT environments are extremely dynamic. They have gone from static infrastructures to software-defined resources that keep changing on the go, and modern tools are a must to handle them. For instance, modernization is a key quality to implement microservices and containerization.
Organizations find it challenging to manage modern IT environments at three levels –
One of the key challenges comes with the ever-changing systems with transitory components.
IT teams face a major challenge with data generated by their systems. This complexity increases along with the increase in data volumes, variety, and specificity.
The next layer of complexity is with the tools used to handle the systems and the data. With the growing number of tools with specific functionalities, it turns challenging for organizations to ensure that they interoperate. Otherwise, there are silos – operational and data.
With AIOps’ ability to use machine learning and data science, IT teams get better visibility of the real-time data and can understand the issues — existing and new ones – and also suggest how to handle them.
AIOps is great, but to get the most value from the implementation, organizations need to deploy it as an independent platform that absorbs the data and then serves as a common platform to access it. It should include five types of algorithms to make this possible: – 1. Pattern discovery, 2. Inference, 3. Data selection, 4. Collaboration, and 5. Collaboration.
Using these algorithms, AIOps eliminates data redundancies and possible noise, compiles truly relevant data, and makes it available for the teams. Through algorithmic filtering, AIOps enhance the productivity of the Ops teams by reducing false positives that take up a lot of their time.
Once this is done, AIOps starts grouping the data to derive important insights based on various criteria, like demographics, type of the text, etc., The AIOps platform then communicates the final output to a collaborative environment so the teams can access it.
AIOps sees digital transformation (DX) as a mode of deriving data from an application and integrating this data with all the IT systems. The key intention here is to cut down the silos and make data-driven insights readily available. This further helps teams gain more visibility about the processes and their owners and provide insights into the customer experience. These insights can help automate IT operations and ultimately improve customer experience.
Before AIOps, organizations faced a number of challenges due to siloed systems. Such systems created gaps in data and made its access quite difficult. As a result, data was limited to the owners, and specific toolsets were required to handle it. AIOps solved this problem.
As we have already discussed, organizations using AIOps can benefit from greater visibility. IT teams have always suffered from data silos that obstruct data visibility and also create inconsistencies. However, AIOps solves this challenge by making data readily available across the organizational teams. AIOps unifies the application/business data and brings it under one umbrella. This way, the data has more visibility. Additionally, the use of automation also adds to the efficiency and enhances the performance of the IT processes.
Apart from bringing down the data silos, organizations seek to leverage automation through digital transformation. AI in AIOps refers to artificial intelligence. AIOps is powered by typical AI and machine learning tools like pattern matching. This way, AIOps enriches and correlates data from various sources. It cleans data, eliminates delicacies and false positives, and thus helps teams gain detailed insights and focus on relevant issues.
With an integrated AIOps platform, it is possible to bring all application and infrastructure operations under a single dashboard. This makes it easy to view and manage the information. Additionally, the AIOps platform also suggests which applications should be modernized first and how else could be done to accelerate the transformation.
Zero downtime is another benefit that AIOps brings to DX. Downtime is a major issue that leads to high losses, lowers productivity, and also casts an impact on the organization’s reputation. With the implementation of AIOps, SRE and DevOps can come together and respond to the problem. This is the reason for organizations to focus on DX with AIOps to gain significant business value.
Other benefits of adopting AIOps for DX include anomaly detection, malware traffic detection, outlier detection, vulnerability detection, and root cause analysis.
This is an obvious question that organizations have. Well, when looking at it for the first time, people may get very confused about how it fits into their existing environment. One thing to note is that AIOps does not replace existing tools. Rather, AIOps sits at the point where different domains (service desk, orchestration tools, etc.) intersect and integrate information across the domains. It offers a readily available output that all the domains can access.
In simpler terms, it can be said that an AIOps platform organizes and integrates the output coming from the organization’s domain-specific IT monitoring and management tools. Thus, the AIOps platform serves as a control layer for all the IT tools.31
In conclusion, it can be said that AIOps is a transformative technology that not only accelerates DX but also adds to its efficiency. However, organizations need to consider it as a journey rather than a destination and make continual improvements to succeed. With the right implementation, AIOps can assist and enhance DX initiatives and also boost It is recognition as an economic generator.
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