With the promise of unprecedented potential, artificial intelligence (AI) and predictive analytics have permeated into every field of business. Due to their ability to help retail staff serve customers better, personalize video recommendations based on users’ preferences, reduce employee churn, and detect fraud and security threats, AI and predictive analysis are rapidly being adapted across industry verticals. Forbes Insights neatly summed up the business benefits of AI and predictive analysis as:
Reduced operating costs.
Improved speed to market.
Transformed business and operating models.
For IT departments, the application of AI coupled with predictive analysis has far greater benefits. Given that IT departments power the entire business, they use far more applications than other departments and generate far more data. Which means, with the impressive leaps in AI technology, IT departments can now process and make sense of all that data easily. Here are a few ways IT departments can benefit from AI and predictive analytics:
Predict and eliminate downtime
AI in IT operations, or AIOps, can truly help improve IT efficiency and make way for faster decision-making. Traditionally, IT operations are reactive, i.e., they detect problems as and when they appear, and then attempt to resolve problems and restore services back online. With AI and predictive analysis, IT operations can break free of this reactive approach and become proactive.
IT operations are data-rich, meaning they generate huge volumes of monitoring data that hold valuable information that can help transform operations. AI engines can quickly crunch those large data banks and easily identify hidden patterns and trends that can shed light on recurrent problems and seasonal trends in your IT operations.
The report above clearly illustrates the seasonal peaks in alarm data (from both networks and applications) for the past two years, which concur with the bi-annual maintenance schedule. Maintenance activities on servers or networks are known to cause disruptions in network or application availability, leading to an increase in alarms during maintenance periods.
Applying predictive algorithms to the above report can help with foreseeing when applications or networks are likely to go down in the future.
Such cognitive insights provided by AI and predictive analysis are remarkably different from traditional analytics. They are more accurate and detailed than manually-generated predictive insights, as they can consume large volumes of historical data sets, quickly identify outstanding patterns from regular noise, and churn out predictions based on those patterns. Since AI bots are programmed to continuously learn and adapt, they improve and make more accurate predictions over time.
AI and predictive analysis also help IT operations managers monitor IT applications in real time and foresee potential failures. This can help them plan software and security updates in a manner that’s least disruptive to business services, as well as quickly assemble alternate servers or networks to share the load during downtime.
Plan resource requirements
Leveraging AI in resource allocation can improve service desk processes, making way for faster request resolutions and better SLA compliance. AI algorithms are better equipped to analyze, diagnose, and suggest resource requirements so that IT managers can make faster and more efficient resource management decisions.
For instance, IT managers can plan resource requirements by mapping incoming requests against several factors, such as geography (remote offices and city offices), time (busiest hours and off-hours), day (weekdays and weekends), or seasonal changes (holidays and vacations).
The chart below shows an increase in incoming requests from March to early August.
Plotting this data on a map shows that the requests originate from several remote offices, while the requests from the Austin headquarters have come down considerably. This is in line with the pandemic-induced migration, where employees prefer to work from rural districts instead of big cities. Typically, such seasonal migration results in an increase in technical issues related to VPN accessibility, Active Directory accessibility, etc.
Cut costs and plan IT budgets better
The IT asset life cycle is rife with wasted resources; each stage of the asset life cycle is known to include a lot of unnecessary expenditure that could have been saved by leveraging AI and predictive analytics. A typical scenario is over-procurement and underutilization of hardware and software assets. Planned restructuring, recruitment, and upgrades trigger massive purchases that cut into the IT budget. When these planned operations do not occur, these hardware and software assets tend to go to waste.
AI can prevent such waste and aid in intelligent decision-making at each stage of the asset life cycle. In the purchase cycle, it can help IT managers make smart purchase decisions by investing in assets that have a longer life cycle but relatively lower costs. It can also help plan asset purchases to ensure that assets spend minimal time on storage shelves.
AI and predictive analytics in IT asset management can also help reduce IT spending by giving IT managers a clear picture of asset retention costs.
The above graph shows the asset retention score of servers for the past five years along with a forecast for the next five years. The retention score is the ratio of asset maintenance costs to asset purchase costs. I’ve chosen servers for this illustration since they are usually the longest-serving assets in an organization. From the report, it’s clear that as servers near the seven-year mark, the cost of maintaining them is almost half their purchase cost, hinting that it might be time to retire them.
To conclude, AI and predictive analysis are critical to enable rapid digital transformations and data-driven decision-making in IT organizations. Together, these capabilities provide cognitive insights that can unlock several hidden opportunities for organizations.
Analytics Plus, ManageEngine’s flagship IT analytics application, offers out-of-the-box integrations with several popular IT applications, such as ServiceNow, Jira Software, Zendesk, and ManageEngine’s umbrella of IT products, including ServiceDesk Plus, Desktop Central, OpManager, and Applications Manager. Activated by voice and text-based inputs, Analytics Plus’ built-in AI-assistant, Zia, intelligently reads user questions and provides rich insights into data instantly. Analytics Plus also features robust predictive algorithms that can quickly analyze massive data loads to come up with the most reliable predictions for the future.
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*** This is a Security Bloggers Network syndicated blog from ManageEngine Blog authored by Sailakshmi Baskaran. Read the original post at: https://blogs.manageengine.com/analytics/analytics-plus/2020/10/30/leverage-ai-and-predictive-analysis-to-cut-costs-and-eliminate-downtime.html