The COVID-19 pandemic brought about a cultural shift to more agile and intelligent ways of conducting business operations, powered by the integration of digital technologies in every business area.
Research conducted by Statista says that the direct investments in digital transformation, including moving data to the cloud, using technological devices and tools for transformation, and automating processes, are expected to reach a total of 7.8 trillion U.S dollars.
Consequently, most organizations are working in a highly dynamic and componentized IT landscape with hundreds of apps, services, servers, and micro-services running in a hybrid environment.
In such a scenario, managing cybersecurity becomes complex yet highly essential for the smooth running of business operations. For one, organizations may deploy multiple IT management tools that cater to a particular domain but do not provide a holistic view of the entire infrastructure. Secondly, enterprises can no longer afford to respond to IT events after they have occurred and need a more proactive approach to IT infrastructure management.
Even a tiny IT incident can disrupt entire business operations and cost millions of dollars in lost revenue. A recent survey reports the average downtime for an outage as 79 minutes, with the hourly cost of downtime being $84,650.
One can certainly not forget the damage to customer loyalty and brand image. When Australia’s biggest supermarket chain suffered a nationwide 30-minute IT outage, checkouts were closed down, and shoppers were forced to leave their groceries at the store.
While technologies like advanced analytics and AI improve current business operations in several domains and deliver more value to customers, can they be used to prevent IT outages and costly downtimes?
Let’s find out.
Predictive analytics: Using AI to prevent IT outages and costly downtimes
Today’s customers and employees expect a seamless and undisturbed digital experience.
Legacy IT monitoring systems have historically been very manual and reactive. However, as seen above, organizations can no longer wait for an IT incident to occur before stepping in to find a solution.
What can organizations do then? In such a scenario, enterprises can leverage artificial intelligence in IT operations management, or AIOps, that can jump in with predictive capabilities to (A) foresee future incidents and (B) automate fixes before they disrupt operations.
As defined by Gartner, “AIOps platforms are software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”
One of the most significant use cases of AIOps, predictive analytics, improves application and infrastructure uptime, reliability, and performance by preventing critical outages and reducing maintenance costs in the process.
How does it work?
Historical and real-time IT data is the backbone of AI-driven predictive analytics. However, merely having the data won’t suffice. This is where AIOps steps in.
Machine learning, a subset of AI, analyzes this data to predict potential IT incidents and automate a remediation workflow. Here’s how it works. The technology –
- Studies data patterns to baseline normal behavior, detect anomalies, and leverage statistical measurements to determine performance thresholds.
- Adapts thresholds to anomalous activity and changing behavior.
- Reduces event noise and false alerts by analyzing past data and getting a solid grasp on what actually leads to failures.
In short, predictive analytics gains insight into the past state of your IT infrastructure to correlate it with current events, proactively fix issues, and prevent costly downtimes.
By predicting and preventing imminent outages or automating fixes, predictive analytics –
- Improves metrics, such as mean time to detect (MTTD), mean time between failures (MTBF), and mean time to resolution (MTTR)
- Ensures unparalleled online customer experiences by preventing outages and maximizing uptime.
- Decreases IT maintenance costs and eliminates loss of revenue due to costly downtimes.
- Enhances productivity and efficiency by allowing internal IT teams to focus on initiatives that add value to the business.
Moreover, predictive analytics studies the historical utilization of infrastructure resources, such as memory and CPU usage, to predict when a resource will reach its total capacity and ensure additional capacity is added on time to avoid business outages and costly downtimes.
While predicting potential events in advance prevents costly outages, in case an adverse event does happen, AIOps can also help in quickly resolving such issues, thus mitigating the impact of the outage.
While a traditional IT operations management system generates thousands of alerts, logs, and signals for a single event, AIOps can reduce such noise by narrowing down the root cause of the issue and escalating the problem to the right team of subject-matter experts (SMEs) for faster remediation. Additionally, the system develops relationships between anomalous incidents, which helps predict similar issues in the future.
Learn More: An IT Leader’s Essential Guide to AIOps, 9 KPIs to measure the impact of AIOps
How can Acuvate help prevent IT outages and costly downtimes?
At Acuvate, we help clients automate IT operations management, monitor an increasingly complex IT infrastructure, predict outages, and prevent costly downtimes with our AIOps solution.
Our robust AIOps solution boasts of several capabilities, such as –
- Intelligent alerts and incident management
- Predictive analytics and insights
- Anomaly detection
- Performance base-lining
- And much more!
To know more about our solution, please feel free to schedule a personalized consultation with our AIOps experts
