Top 10 Innovative Data Center Automation Tools in 2024

Managing a data center used to be a manual, siloed process requiring huge IT teams just to keep operations running. Servers had to be provisioned, workloads scheduled, jobs monitored, capacity optimized…the list goes on.

As data volumes and complexity grew exponentially, these outdated methods became bottlenecks, threatening business-critical systems. Something had to change.

Enter data center automation.

Leading enterprises like JP Morgan and Volkswagen radically transformed their operations by adopting smart automation tools. They minimized human intervention in data management, allowing IT teams to focus on innovation vs maintenance.

According to Gartner, 80% of traditional data centers will disappear by 2025, with the remaining modernizing through automation and cloud. A survey by Spiceworks revealed that 61% of organizations currently use data center automation, with 86% reporting benefits like enhanced efficiency, faster deployment, and lower costs.

The potential impact is massive. Wikibon estimates that automating data center operations can reduce costs by $3 million per year for the average enterprise.

So how exactly does data center automation achieve these game-changing outcomes? Let‘s examine the key capabilities:

How Data Center Automation Drives Efficiency

Workload Automation

Automating processes like job scheduling, file transfers, and workload balancing is fundamental. Modern tools use workflow engines to orchestrate tasks across servers and applications. This removes the need for complex scripting.

Sophisticated workload automation (WLA) enables modeling dependencies between jobs and optimally schedules workflows. Built-in workload balancing prevents congestion or failures.

Insight and Control

Data center automation also improves visibility. A centralized dashboard displays metrics across all connected systems. Monitoring utilization in real-time allows predicting impending bottlenecks and allocating resources effectively.

Granular data on automated processes also uncovers issues that require troubleshooting. Analytics pinpoint optimization opportunities.

Integration

Automation tools seamlessly integrate legacy systems like mainframes with modern cloud platforms and container orchestration. This simplifies connecting disparate data and workloads into unified workflows.

Pre-built adapters avoid needing complex custom integration. APIs and SDKs also allow hooking into automation capabilities from other platforms.

Continuous Optimization

Advanced algorithms analyze historical workload patterns to forecast future needs. Simulation of scenarios identifies the optimal schedules and resource allocation.

Machine learning models continually refine automated decision-making to meet SLAs as the business evolves. This intelligence moves toward real-time self-correction.

These capabilities make data center automation pivotal for managing the scale, distribution, and security of modern hybrid environments.

Next, let‘s explore the top 10 tools dominating this space.

Top 10 Data Center Automation Tools

1. ActiveBatch

ActiveBatch provides a single pane of glass to integrate and manage workloads across on-premise, hybrid, or cloud environments.

The drag-and-drop workflow designer simplifies creating automation processes without scripting. ActiveBatch leverages a distributed architecture and agent-based technology to enable centralized control with flexible remote execution across systems.

ActiveBatch‘s distributed architecture for central automation

Detailed logging and alert notifications provide insights into operations. Role-based access controls secure the platform across distributed teams. Compliance capabilities like encryption and SSH protect data transfers.

For example, Vero Skatt used ActiveBatch to integrate automation across six environments including IBM i, Windows and Apache Hadoop. ActiveBatch reduced manual efforts by 90% while enhancing security and compliance.

Leading companies like Experian rely on ActiveBatch to connect 190+ systems into an automated enterprise data pipeline. The workflow automation boosts the productivity of IT teams by 2x.

2. Redwood RunMyJobs

Redwood RunMyJobs takes a modular approach to automation. It acts as a centralized hub connecting disparate applications, scripts, databases and tools across the enterprise technology stack.

Redwood provides pre-built automation for common IT processes like business workflows, data transfers, security patching, job scheduling and more. Templates get new customers up and running quickly while its orchestration engine handles dependencies and workload distribution.

Real-time dashboards give visibility into bottlenecks impacting business-critical KPIs while meeting security and compliance requirements. Role-based access ensures automation control stays with designated admins.

For example, ALSO Group used Redwood to reduce 46,000 ERP process definitions down to just 570 automation scripts. Operational efficiency jumped as staff could focus on value-add IT projects.

Organizations like AMD, Epson and Avaya standardize automation with Redwood to optimize costs and resilience. The tool integrates readily with leading ERP, CRM, BI and other systems.

3. Stonebranch Universal Automation Center

Stonebranch takes a universal approach to workload automation. The centralized Universal Automation Center enables a single point of control to orchestrate and monitor workloads across cloud, on-premise, virtualized, and containerized environments.

Stonebranch Universal Automation Center architecture

A universal agent executes automated processes across diverse servers and applications. A universal data mover securely transfers files and data between systems. Hundreds of pre-built adapter simplify third-party integrations.

For example, Liberty Mutual relies on Stonebranch to automate over 2,500 business-critical processes across mainframe, distributed systems, ETL, and cloud platforms. Ops efficiency jumped over 50% within months.

ITERGO manages over 15 million daily data center transactions for the ERGO Group with Stonebranch. The workload automation platform seamlessly connects 38,000 users across private cloud, IBM z/OS, and distributed environments.

4. BMC Control-M

Control-M by BMC optimizes and orchestrates workload automation through powerful forecasting and policy-driven scheduling. The predictive analytics engine analyzes past workload patterns to forecast resource needs and identify the optimal schedules.

Control-M assists with capacity planning by simulating scenarios to accurately provision needed resources. Automated SLA management ensures critical jobs get priority. Out-of-the-box application templates accelerate deployment.

Control-M architecture showing workload automation capabilities

For example, Rogers Communications automated over 1800 batch schedules with Control-M, reducing job failures by 92%. The workload automation improved reliability for mission-critical subscriber billing and customer care apps.

Juniper Networks uses Control-M to manage over 20,000 jobs per day across global data centers. Automation reduced downtime by 60% and improved scalability to support rapid growth.

5. JAMS Scheduler

JAMS Scheduler simplifies building automated workflows for Windows-based environments. The .NET framework enables tight integration with PowerShell for advanced process automation capabilities using a familiar interface.

JAMS provides a unique visual Workflow Builder that displays job dependencies, triggers, and recurrences through a graphical diagram. This gives visibility into cross-system workflows for troubleshooting and optimization.

Security and compliance capabilities like encryption, obfuscation, and access controls help meet rigorous standards like PCI DSS. JAMS includes hundreds of pre-built actions covering common automation scenarios.

For example, Jupiter, an asset management firm, relies on JAMS to securely orchestrate over 36,000 daily FTP, email, and database processes across their global Windows infrastructure. Automation provides the resilience and compliance needed for their financial systems.

Organizations like RR Donnelley, Titleist, and the US Navy use JAMS for mission-critical automation. Tight Windows integration plus robust security powers these complex implementations.

6. IBM Workload Automation

IBM Workload Automation optimizes scheduling and resource allocation through AI and machine learning. The Data Advisor continually analyzes workload metrics, detects anomalies, and identifies opportunities to improve efficiency.

Forecasting algorithms predict job runtimes and resource needs based on historical data. Simulation capabilities allow modeling different workload scenarios to prepare for new application rollouts.

IBM offers hundreds of out-of-the-box integrations with platforms like SAP, Oracle, SAS, IBM MQ, and leading cloud providers. The universal dashboard provides a single point of control with drill-down visibility.

For example, KMD used IBM to automate processes across mainframe, Power, and x86 systems. Machine learning optimized load balancing increased throughput by 10-15%. Improved visibility saved 1,000 operational hours annually.

Global enterprises like General Motors and Electrolux run IBM workload automation across mission-critical systems handling billions of transactions daily. The AI and analytics provide continual improvements.

7. OpCon Workload Automation

OpCon by SMA Technologies takes an event-driven approach to workload automation. Any events like a file landing, system failure, database trigger, or schedule can kick off OpCon processes through its robust API.

This architecture lends itself to lights-out automation across enterprise apps, databases, file transfers, scripts, and custom programs. For example, automated alerts can trigger failovers, restarts, deployments and more.

Integrations and packaged workflows accelerate automation for ERP, HR, Financials, Supply Chain apps and more. Unique capabilities like Workload Balancing and Automated Command Resolution optimize distributed jobs and retries.

For example, OTS achieved near-zero downtime by having OpCon handle errors and manage IT processes seamlessly across core systems. Acme Paper automated over 180 mission-critical processes with OpCon, increasing IT bandwidth 10x.

8. Tidal Workload Automation

Tidal Workload Automation is designed to simplify automating processes like batch processing, data transfers, and complex job workflows. The browser-based UI offers intuitive controls for modeling multi-step workflows including branching logic.

The tool specializes in handling workload variability – from automatically rerouting jobs during peaks to improvising when unplanned events occur. Integrations connect diverse platforms across cloud, virtualized, and on-premise environments.

Advanced analytics and AI engines optimize schedules and resource allocation. The simulator allows modeling hypothetical workload scenarios to fine-tune system configuration.

For example, one insurance firm uses Tidal to automate daily spikes from 25,000 to over 100,000 jobs. Despite massive fluctuations, Tidal has helped maintain 100% SLA attainment for over a decade.

Swiss railways relies on 1,700 Tidal schedules to dispatch trains daily. Tidal‘s reliability and scalability keeps these large-scale logistics on track.

9. AWS Batch

AWS Batch enables running mass batch jobs seamlessly on AWS. The serverless service dynamically provisions the optimal compute resources required to complete jobs.

Users submit batch jobs and AWS Batch handles scheduling, capacity provisioning, scaling, and monitoring the workload. The AWS cloud automatically adjusts capacity up and down to meet processing demands without overprovisioning.

This makes Batch ideal for data processing, model training, media encoding, and other workflows with variable requirements. Users pay only for the compute resources consumed.

For example, Arm Limited relied on AWS Batch to migrate its complex chip design workloads to the cloud. The automation and elasticity accelerated product engineering while optimizing costs.

Major companies like Philips, PBS, and Fender run big data, genomics, and graphics workloads on AWS Batch. It provides cloud-scale resources without manual intervention.

10. Autosys Workload Automation

Autosys by Broadcom specializes in handling peaks and variability for enterprise workloads. Its automated job scheduling scales seamlessly across platforms like mainframes, ERPs, custom apps, and cloud platforms.

Smart agents provide bi-directional integration with hundreds of applications out-of-the-box. The solution offers comprehensive capabilities ranging from visual workflow authoring to lights-out production scheduling.

Powerful analytics and machine learning algorithms optimize resource decisions and identify automation opportunities. Autosys also excels at automation for IT operations like job monitoring, alerting, and reporting.

For example, Hanwha Life Insurance relies on Autosys to automate over 8,000 batch jobs across its mainframe and UNIX systems running critical financial applications.

HSBC, Shell, Honda, and other Global 1000 companies trust Autosys to automate millions of mission-critical jobs across hybrid environments every day.

Evaluating Data Center Automation Solutions

Here are key criteria to consider when evaluating data center automation tools for your business needs:

Environments Supported – Look for tools that provide cohesive automation across on-premise, cloud, container, and virtualized platforms.

Scalability – The solution must be proven to handle increasing workloads and data volumes over time.

Third-party Integration – Consider the availability of pre-built connectors and APIs to integrate with tech stack.

Ease of Use – Intuitive interfaces and workflow visualization enables more self-service automation.

Analytics & Simulation – Robust data analysis allows continuous optimization and capacity planning.

Developer Enablement – Open SDKs and APIs boost customization and extensibility.

Security – Evaluate authentication, encryption, and data security capabilities.

Support & Community – Consider educational resources and community forums for continued learning.

The Future of Data Center Automation

Data center automation is reaching an inflection point. The proven benefits of liberating data operations from manual intervention are simply too massive to ignore.

As cloud adoption accelerates, hybrid environments will become the norm. Organizations must integrate automation across legacy systems, private data centers, and public cloud platforms – with security woven throughout.

Emerging technologies like IoT, AI, and big data analytics will place even greater demands on automation intelligence for allocating resources, predicting failures, and rerouting workloads in real-time.

Data center automation tools will evolve into smart platforms leveraging ML and heuristics to provide lights-out optimization. They will tap into data lakes and analytics pipelines to gain deeper context. Basic job scheduling will transform into autonomous orchestration with business KPI alignment.

This next wave of intelligent automation promises to unlock the true potential of data center operations – making IT a core competitive advantage.

Are you looking to assess automation solutions for your data center or cloud environments? Our unbiased experts can help you find the right technology for your specific needs and use cases. Contact us to get started.