DevOps and the Dynamic Data Center
Of course, DevOps encompasses much more than workload automation. DevOps isn’t just technology … and it isn’t just a manifesto or a methodology. DevOps requires technology and people operating holistically and with a single perspective. This is a tall task, and one that can’t be easily achieved without improved automation and analytics capabilities.
Businesses today are operating at a velocity that can result in incredible and sometimes unexpected demands on the dynamic data center. DevOps predictive analytics helps correlate the capabilities of System z, distributed systems and the still-evolving potential of architectures such as cloud to deliver greater flexibility and responsiveness to changing business demands. An analytics-based approach for DevOps business and operational data, coupled with sharing data and workload patterns across multiple, integrated platforms, can work together to create dynamic data centers that better support the needs of today’s rapidly evolving business environment.
Aligning Workload Automation Predictive Analytics With DevOps
Mentioned earlier, predictive analytics is capable of managing, analyzing and discovering new opportunities from business and operational data. Predictive analytics examines trends and activities along with real-time data, so IT DevOps can better predict future conditions and behaviors. The algorithms involved in predictive analytics combine techniques from data mining, machine learning, modeling and statistics to analyze current and historical facts in order to make predictions about future events. Dynamic data centers that automate, coordinate, correlate, mine and analyze the large amounts of systems management data can make better decisions more rapidly. Predictive analytics can help dynamic data centers achieve significant improvement and more cost-effective IT services.
Predictive analytics can be used to bolster delivery of business service by capturing, correlating and extrapolating problem areas and providing the ability to predict where problems might occur before they happen. Delivery of a business service involves multiple data center components, such as workload automation, infrastructure, storage, applications and databases. Generating predictive analytics across these correlated components to predict potential disruptions and then leveraging the automation platform to dynamically provision and optimize the related data center components empowers the dynamic data center. The ability to automatically identify, alert and correct for problems before they occur can significantly reduce the work of IT professionals, freeing them up to tackle more long-term, critical business projects.
Benefits: DevOps, Predictive Analytics and the Dynamic Data Center
Combining everything we’ve discussed up to this point brings us to using predictive analytics on operational data to improve the behavior of IT and systems management. The data center becomes more dynamic and availability of critical business services improves as predictive analytics is applied to DevOps business and operational data, and workload automation is leveraged to coordinate activities across business services and the data center components. Workload automation drives critical business services that are very visible, such as ATMs, shopping Websites, payroll processing, manufacturing, goods distribution and even healthcare systems.
The impact to your systems and IT professionals can be quite profound. First, the combination of DevOps and predictive analytics can improve productivity at the service level. Applications will better service the business because they’re run more effectively with fewer outages and more rapid service.
Automation and predictive analytics can be applied to correct problems that previously involved a technician to fix. From the perspective of an IT professional, being removed from day-to-day reactionary problem remediation can afford you the opportunity to step away from the process-by-process operational management and extend your view to the business level. Furthermore, automated management of tasks can free up time for the technician to delve into learning about technology trends, new software and releases, and develop the collaboration that’s imperative for a DevOps-based organization to thrive.
Relentless growth of business and operational data will continue. Forward-thinking DevOps organizations must deploy automated, predictive analytics to help their dynamic data center realize its potential to match the velocity requirements around analyzing business and operational data to help business grow and keep IT at the center of the dynamic data center.
These capabilities, when fully realized, will deliver a number of benefits, including cost reduction, improved flexibility and efficiency, in terms of IT support for business initiatives, and reduced complexity that translates into greater purposeful use of data intelligence that leads to accelerate their contemporary business.