More than half (57 percent) of workers in Southeast Asia believe that more than 10 percent of their workday is wasted waiting on technology to deliver the information they need, according to the ‘Mind the Gap’ report, which Nimble Storage produced in collaboration with Oxford Economic.
The report found that delays in propagating and refreshing application data – the app- data gap – can cause significant productivity drains and economic losses.
The report surveyed nearly 3,000 IT professionals and business application users based in the U.S., Germany, U.K., Australia, and Southeast Asia.
Respondents from Southeast Asia are one of the most demanding regions of their technology infrastructures, with 73 percent agreeing that the speed of applications they use significantly affects their ability to perform their best.
Fifty-seven percent of Southeast Asian respondents say they lose more than 10 percent of their workday waiting on technology to deliver the information they need, compared with 42 percent in the U.K., 39 percent in Australia, and 30 percent in Germany.
Forty-three percent of Southeast Asian respondents say they waste more than 10 minutes each workday waiting for a software application to respond.
While half of Southeast Asian business users say they avoid using some software applications at work because they run too slowly, only 18 percent of IT professionals think their users are either unsatisfied or very unsatisfied with the way software systems work at their companies.
Fifty percent of respondents avoid using certain applications at work because they run too slowly.Close to nine in 10 (88 percent) of respondents said they are less tolerant of delays than they were five years ago.
Millennials Experience the Effects of the App-Data Gap Most
As high as 77 percent of millennials say that sub-optimal application performance affects their ability to achieve their personal best, compared with just half of Baby Boomers and 72 percent of Gen Xers.
Half of Millennials surveyed say they’ve stopped using an application because it runs too slowly — significantly more than users in other age cohorts.
Over three-fourths of Millennials say they occasionally or constantly experience delays when accessing or inputting information with business software, compared with 60 percent of Baby Boomers.
“It’s no mystery why business users expect access to data to be immediate and continuous. The conundrum facing IT decision makers is how to predict and prevent performance bottlenecks before users perceive a slowdown in responsiveness,” said Suresh Vasudevan, CEO, Nimble Storage. “The performance bottleneck between the data and the application, which we call the ‘app-data gap’, negatively impacts employee work time and ultimately impairs business performance. We believe that by bypassing reactive approaches for root cause analysis that typically take days or weeks, IT departments can harness data sciences and machine learning to predict and prevent barriers to data velocity while fully empowering employees to achieve their best.”
Can Machine Learning Prevent Application Downtime?
Today’s IT infrastructure is full of complexity. A major app-data gap can disrupt data delivery, degrade worker productivity, create customer dissatisfaction and damage a company’s overall speed of business and reputation. While it’s easy and commonplace to point to data storage as the primary culprit for the app-data gap, the factors leading to application slowdowns come from a range of issues across the infrastructure stack.
According to a Nimble Storage Predictive Analytics Report —which analyzed more than 12,000 cases documenting examples of app-data gap related issues across the Nimble install base of more than 7,500 customers — 54 percent of all issues have nothing to do with storage. The majority of issues arise from challenges with configuration (28 percent), interoperability (11 percent), non- storage best practices impacting performance (8 percent) and host, compute or VM-related issues (7 percent). Of the 46 percent of storage issues detected, hardware and software problems, software update assistance and performance setbacks are most common.
These findings debunk the IT professionals’ first instinct that storage infrastructure is the primary cause of application performance issues. This presumption leads IT professionals to implement fast flash-based storage technologies to accelerate performance but flash alone does not address the 54% of unrelated storage issues.
To close the app-data gap, we believe IT organisations need to leverage predictive analytics that incorporates both data science and machine learning to optimize the performance and availability of applications. These technologies are designed to help identify poor performance early, minimize or eliminate the effects of an issue, prevent businesses from encountering the same problem as their competitors and continually improve performance and availability for users.