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Predictive analytics in cloud performance monitoring: A game-changer for IT sysadmins

Predictive analytics is a branch of business-driven data science that attempts to answer one fundamental question: “What happens next?” That’s a pretty valuable question for any industry, but we’re seeing some really interesting progress in healthcare and print management, in particular. Running print systems in complex, multi-cloud environments while balancing cost, performance and security is always a challenge. So how can we use predictive analytics to better anticipate future change, particularly in clinical environments? Let’s find out.

The evolution of predictive analytics

If we boil it right down to simple terms, predictive analytics is simply the process of using data to predict future outcomes. You can trace its origins right back to traditional statistical models like linear regression and time series analysis. And while these techniques are still valuable, we’ve seen huge leaps over the last few years with the rise of big data and machine learning.

Decision trees, random forests, neural networks and deep learning algorithms now give organizations the power to crunch vast amounts of data, and even make real-time predictions. AI systems can now autonomously learn from data, adapting to changing conditions and optimizing outcomes – without much human interaction whatsoever. It’s a brave new world for business - and for the cloud. Just think, what decisions would you make if you had a near-perfect 20/20 view of the future? That’s the power of predictive analytics.

Predicting patient load surges in healthcare

Predictive data analytics is revolutionizing how hospitals and healthcare providers manage surging patient loads. By leveraging historical data on patient admissions and discharges, algorithms can identify patterns in patient flow, seasonal variations, day-of-the-week spikes, demographics, epidemiological data, even weather conditions. Organizations are even fleshing out this tech with real-time data integration, pulling in data from electronic health records (EHRs), admission/discharge/transfer systems, and ED dashboards, to provide up-to-the-minute predictions for medical staff.

The benefit of all this is pretty obvious: if you know when the busy times are going to be, you can scale up your resources and provide better quality healthcare . But there are flow-on effects, too. Hospitals can quickly identify operational bottlenecks. Budgeting becomes easier, and there’s less chance of inefficient staffing. As Dr. Ali Tinazli wrote in Forbes : “Technology can vastly improve how hospitals and clinics function globally, improving the flow of data and knowledge transfer – and, ultimately, improving the cost pressure.”

Optimizing cloud printing with predictive analytics

As a print management company, especially one that deals in cloud printing and performance monitoring, we’re constantly looking at how predictive analytics can improve our clients’ print environments. And we’ve noticed something: as a particularly resource-heavy activity, and one that requires streamlined cooperation between software, hardware and cloud-based tech, printing is uniquely positioned to benefit from predictive analytics. It’s going to be a total game-changer.

Print job prediction. By analyzing historical print job data, such as time of day, types of documents, seasonal changes and other factors, predictive analytics can forecast future print job demands. This is huge for clients in healthcare and other industries like education where print volume tends to surge around specific times or events. Organizations can now capacity plan more effectively, ramping up their cloud print environment when they need it most.

Printer maintenance prediction. Predictive analytics is kind of magical, because (with the right data inputs) it can straddle that line between software and hardware. In the case of printers, clients can now use predictive analytics to crunch error logs, maintenance history and usage patterns, predicting (with a fair degree of accuracy) when a printer is likely to fail, or need replacement parts. This is great for business continuity, and really helps shrink printer downtime.

Inks, inks and more inks. By analyzing historical data on ink and toner usage, predictive analytics can flag future consumable needs. Organizations can now pretty accurately predict future demand on stuff like inks, toners and paper, which makes budgeting a breeze. No more stockouts, no more inefficiency. You can literally buy exactly what you need.

Keeping costs low. All these benefits flow towards the big one: cost optimization. Printing is an expensive enterprise – Gartner reckons it’s about 1% to 3% of your total revenue – so anything we can do to print more efficiently and cut costs is a good thing. Predictive analytics means organizations can make informed, strategic decisions about their printing habits, reducing unnecessary printing and saving money at the same time.

Data-driven decision making

As sysadmins, we’re often thinking, “If I only knew then what I know now…” Hindsight is 20/20, but AI-powered predictive analytics is getting so good that 20/20 foresight is almost within our grasp. And these technologies will only improve with good data, and more powerful processors. Here are just a few ways predictive analytics can make your life easier as a sysadmin.

Capacity planning. Want to forecast future resource needs based on historical data and trends? Predictive data analytics looks at stuff like system performance metrics, application usage patterns, growth predictions, and then flags when you’re going to need those crucial server upgrades. No more second guessing.

Performance optimization. Spend most of your day fine-tuning hardware configs and running out caching strategies? Predictive analytics tools like RapidMiner, IBM SPSS and Alteryx can quickly identify performance bottlenecks – and this is the good bit – before they even occur.

Security threat detection. Predictive analytics is often being used now to look at network traffic patterns, system logs and behavioralbehavioural data, to try and detect anomalous user activity. Not only that, these systems can analyzeanalyse multiple hypotheses to calculate the most likely cyber outcome of a particular event.

Overall, predictive analytics helps sysadmins in high-demand industries make informed decisions by providing actionable insights into system performance, capacity requirements, maintenance needs, security risks, and resource utilization.

Cost management and predictive analytics

Cloud performance monitoring is all about how to make your cloud environment more efficient. Essentially, how to do more with less. Can you get the same outcome using fewer instances, less storage, a smaller bandwidth? If so, that represents cost savings for the business, and a more efficient cloud structure all-round.

This is the sort of thing that predictive analysis can really help with. By looking at key performance metrics, seasonal user data, cost trends, projected growth and spot instance pricing data – all at the same time – you’re getting not just a holistic analysis of your cloud performance, but the ability to predict how that performance might change. It’s a heads up. A window into the future. And for most businesses, that’s a crucial edge. 99% of success in business is simply knowing things before everyone else, and predictive analytics has given organizations that power.



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