When application performance and availability are impacted by the app-data gap, organisations are forced into a reactive mode. In the worst case, down time drives a fire drill complete with 'all hands on deck' and sleepless nights. In the best case, user complaints start a troubleshooting process that may go in circles with finger pointing between the storage, virtual machines, networking and development teams.
This cycle is dangerous for IT leaders, as there is little opportunity to partner with the business on proactive initiatives. IT is perceived as a barrier to business productivity rather than a partner in increasing competitiveness.
It is therefore imperative that companies close the app-data gap, not only to improve the performance of current work practices, but also to future-proof their organisation against any roadblocks in new processes.
Closing the App-Data Gap
While it is clear that positive cultural changes can help drive greater productivity, it is important to also look at how the IT infrastructure they are providing is impacting their workers' performance. Most often, IT teams' first instinct is that storage infrastructure must be 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 percent of unrelated storage issues.
Therefore, IT organisations need to leverage predictive analytics that incorporates both data science and machine learning to optimise the performance and availability of applications. These technologies are designed to help identify poor performance early, minimise 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.
Organisations looking to plug the gap should take a holistic approach to their IT infrastructure to address the issue and ultimately improve productivity. Investing in technologies that incorporate both predictive analytics which can automatically predict and prevent concerns as they arise will significantly reduce this burden.
Not only can this be achieved in the short term, by anticipating and quickly responding to complex issues, staving off hot spots, and simplifying planning, but predictive solutions can make forecasts for the application performance storage capacity. This information will ultimately help IT leaders plan for improvements to be made seamlessly and with minimum disruption.
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