Today’s modern IT organizations are challenged by the need to improve rapid delivery of critical business services. Each business service relies on successful completion of complex workloads that can be spread across multiple applications and platforms. In such complex environments, a single failure can have a significant impact on an organization’s ability to improve responsiveness to deliver goods and services to the business.

Dynamic data centers depend on the right combination of tools and technology to extract and analyze business and operational information. Mining data can reveal patterns that can help ensure critical systems remain available to help the business match customers to new products and services.

Rethinking the manner in which IT manages thousands of processes and jobs everyday by moving toward real-time automation of workloads not only enables companies to efficiently respond to business events but can also provide better customer service.

Resolving the Issues

Single platform workloads are no longer common. Instead, workloads are typically spread across the enterprise, enabling infrastructures that include System z, distributed and cloud. Without a central point of visibility and control, it’s difficult to manage multiplatform and application dependencies. You can’t see potential failure points. You’re unable to document regulatory compliance. You can compromise your ability to deliver quality services. The complexity can be managed using automated, intuitive, cross-enterprise workload automation systems.

Additionally, the ability to apply predictive analytics to operational data that’s gathered to improve IT processes and business services is a rich opportunity for achieving positive business outcomes. Critical business and operational data contains untapped insight about your business but correlating the data using traditional methods of reporting and analysis is too difficult. Applying analytics to this data enables IT to improve responsiveness and thereby optimize business opportunities.

But mere analytics is just the beginning in terms of realizing the business value of your operational workload data. 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 can better predict future conditions and behaviors. The algorithms involved in predictive analytics combine techniques from data mining and modeling 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 data can make better decisions more rapidly. Predictive analytics can help dynamic data centers achieve significant improvement and more cost-effective IT services.

By monitoring and analyzing critical application and infrastructure data with predictive analytics, dynamic responses can be applied to configuration errors, application performance metrics and system utilization changes, along with coordinated, policy-based action. The result is a dynamic data center that elastically scales IT services to improve business service quality and availability.

To make such a leap requires transforming your IT staff to deliver business innovation instead of simply maintaining the technology. The positive impacts of such an approach include:

• Reducing the cost and complexity of defining and managing mission-critical business application workloads across platforms
• Ensuring consistent and reliable service delivery based on actual usage patterns and data
• Enhancing business responsiveness through real-time automation and dynamic workload placement
• Making the best use of your existing resources while leveraging current IT infrastructure to deliver cloud services
• Orchestrating and automating IT processes.

Complex workloads with extensive requirements and integration points that generate volumes of data pose both a management challenge and opportunity. Dynamic data centers can deftly manage and optimize workloads using automation and predictive analytics. These capabilities deliver numerous benefits, including cost reduction, improved flexibility and efficiency in terms of IT support for business initiatives and reduced complexity that translates into greater purposeful usage of data intelligence that accelerates IT’s responsiveness to the needs of the entire business.