You can’t know what to buy if you don’t know what you’re
trying to accomplish.
This idea is now widely accepted and has certainly made the rounds in plenty of contexts. However, there’s one key area that continues to be overlooked, but would benefit greatly from its wisdom – enterprise IT architecture, engineering and operations. As enterprise data centers continue their perpetual shifts in size, new technology adoption and ever-increasing complexity, we’re left with the realization that data center activity and IT output are critical areas in which inadequate performance monitoring and measurement, and incomplete analytics are all too prevalent.
Performance Analytics
When making decisions about budgetary investments, you need a clear understanding of what you’re trying to accomplish. For IT, it means identifying accurate baselines for performance and availability, and developing the infrastructure that can support your benchmarks and needs at the appropriate cost. Every application and workload has different needs, and additional factors, such as budget and scale, will determine the best direction. Just because something worked for one company – or worked for you in the past – doesn’t mean it’s the best decision for your organization going forward.
Incomplete Data or Personal Bias
Developing a high-performance, reliable IT Infrastructure has to come down to more than intuition and non-specific data sets and metrics. Those approaches worked when we didn’t have access to robust, deep data that measured end-to-end I/O activity across the open-systems stack. The analytics that determine optimal configurations and investments must be based on all available data and designed to deliver a correlated understanding in the appropriate context. It can’t just be about statistics that measure IOPS per second with no correlated understanding of how the workloads are performing across the stack; nor can IT teams expect one solution to work across the board. A whole new infrastructure won’t support healthcare facilities just because it worked well for managing an e-commerce organization.
Falling victim to analytics based on incomplete or out-of-context data can result in truly poor investment and configuration decisions and poor experiences for end users. Making the right decisions means understanding the right data and correlations with unquestioned accuracy, and truly understanding what you’re trying to accomplish, ranging from the baseball field all the way to the IT Infrastructure.
This idea is now widely accepted and has certainly made the rounds in plenty of contexts. However, there’s one key area that continues to be overlooked, but would benefit greatly from its wisdom – enterprise IT architecture, engineering and operations. As enterprise data centers continue their perpetual shifts in size, new technology adoption and ever-increasing complexity, we’re left with the realization that data center activity and IT output are critical areas in which inadequate performance monitoring and measurement, and incomplete analytics are all too prevalent.
Performance Analytics
When making decisions about budgetary investments, you need a clear understanding of what you’re trying to accomplish. For IT, it means identifying accurate baselines for performance and availability, and developing the infrastructure that can support your benchmarks and needs at the appropriate cost. Every application and workload has different needs, and additional factors, such as budget and scale, will determine the best direction. Just because something worked for one company – or worked for you in the past – doesn’t mean it’s the best decision for your organization going forward.
Incomplete Data or Personal Bias
Developing a high-performance, reliable IT Infrastructure has to come down to more than intuition and non-specific data sets and metrics. Those approaches worked when we didn’t have access to robust, deep data that measured end-to-end I/O activity across the open-systems stack. The analytics that determine optimal configurations and investments must be based on all available data and designed to deliver a correlated understanding in the appropriate context. It can’t just be about statistics that measure IOPS per second with no correlated understanding of how the workloads are performing across the stack; nor can IT teams expect one solution to work across the board. A whole new infrastructure won’t support healthcare facilities just because it worked well for managing an e-commerce organization.
Falling victim to analytics based on incomplete or out-of-context data can result in truly poor investment and configuration decisions and poor experiences for end users. Making the right decisions means understanding the right data and correlations with unquestioned accuracy, and truly understanding what you’re trying to accomplish, ranging from the baseball field all the way to the IT Infrastructure.
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