Control-M Reviews

My main business use case for Control-M at NBCUniversal involves setting up schedules, running batches, fixing jobs, and everything that's involved with the actual Control-M side. The main business uses for Control-M revolve around bringing data together for every application that NBC uses. NBC is a media entertainment company, known for Universal Studios, Paramount, CNBC, MSNBC, and quite a few others. We utilize Control-M with everything that NBC does, such as for warehouse, database, and timekeeping. There are quite a few other areas where we actually use Control-M, but those are the main things. Additionally, we run backups to ensure the data is kept intact, so in case of any reporting issues, we always have something to restore back to and present as required.

Control-M supports our DataOps and DevOps initiatives by allowing us to set up job schedules based on requests from teams. These schedules outline what is needed, the sources used, and how it transforms the data to their requirements. This is quite useful in the way Control-M operates. For DevOps, the process is similar; teams provide what they need, and we input that into Control-M. We test it first to ensure it does what it's supposed to do before going live, ensuring nothing disrupts live production or information. We do use Control-M to orchestrate workloads across multiple environments. Currently, we're looking at possibly integrating Control-M with DataDog, which we use a lot. We also plan to bring together Control-M and AWS for certain applications, as there's a transformation occurring with some applications using AWS alongside Control-M. Although we have the old IBM AS/400 system, where schedules have been put on hold, we still utilize it with Control-M. Control-M effectively integrates everything, including running a schedule on AS/400 that processes data and sends it back for further manipulation.

The features of Control-M that I like the most include the ability to easily integrate or bring in different platforms into Control-M. For instance, AWS, mainframe, TWS, and something that's running on Autosys can all be brought into Control-M, converted to how Control-M runs it, and then the batch can be executed. This centralizes various applications in Control-M, which doesn't just have to handle batch processes, but also other tasks like reporting on required data. I find this functionality very useful and the setup is impressive, with more advancements yet to come.

With Control-M, my company has achieved several measurable improvements since I started. The metrics indicate that the number of failures has dropped, and we have addressed the issue of excessive false alerts that I encountered when I joined. Previously, we received an overwhelming number of alerts daily, but now we manage to maintain that at a normalized level, perhaps around five to fifteen alerts, depending on running core batches and their setup.

I think Control-M can be improved by enhancing integration capabilities. I would like to see an integration for OpenTelemetry, which we're looking into moving forward into early 2026 onwards. I believe it should be made easier so that even a basic person can have a grasp of how the GUI works and how everything connects together. Additionally, more integration with other platforms would be beneficial. I know there are over one hundred integrations already, and they are still working on many more, but certain integrations that we would use could probably be brought in sooner.

I have been utilizing Control-M for the last probably twenty-four years now.

The biggest return on investment I see when using Control-M stems from how it simplifies processes. Control-M makes everything easy to use and approachable, allowing multiple sources to consolidate data effortlessly. You can have numerous sources feeding into one job on Control-M, which will process that data whether it takes seconds or several hours. Upon completion, this data is ready for users, and any issues can be traced back to rectify the situation.

Neutral

The biggest return on investment I see when using Control-M stems from how it simplifies processes. Control-M makes everything easy to use and approachable, allowing multiple sources to consolidate data effortlessly. You can have numerous sources feeding into one job on Control-M, which will process that data whether it takes seconds or several hours. Upon completion, this data is ready for users, and any issues can be traced back to rectify the situation.

Regarding pricing, setup cost, and licensing for Control-M, this aspect can be challenging. Licensing constantly evolves as needs change. We are considering a transition to Helix in early 2026, with ongoing discussions between BMC and high management regarding pricing structures. Once an agreement is reached, the transition will proceed.

In my current field, I have been working for more than five years. Approximately five thousand to ten thousand users interact with Control-M in my company, and their main role is view only, meaning they cannot actually do anything with the jobs. They can see what's running and if something fails, they come to us, and we take the action on the backend to resolve that issue. They have full visibility of data running on their applications, but the actual batch and job side is more controlled by us. They can view the GUI to see something running or if something is not running, but they cannot do anything else with it.

Control-M integrates fairly well with new or changing technologies within our DataOps or DevOps stack. Depending on the application and the team, some may choose not to use Control-M, opting for external solutions instead. However, from my perspective, Control-M can handle any DataOps-related tasks or platform-related processes without a problem. If something is not already integrated in Control-M, reaching out to BMC enables them to create that module and facilitate data needs effectively. I have used Control-M client and we are currently utilizing the API for transitions with another platform. My current project involves DataDog integration with Control-M, exploring how both platforms can work together for alerting, synthetic tests, and more. This integration is a work in progress, but BMC staff have assured me that it should be quite easy to integrate both platforms.

From my experience, Control-M has enabled new capabilities and business processes that were not possible before. The advancements in integration allow us to gather data from various applications and platforms seamlessly. Control-M can handle everything required, ensuring data is fed to clients or application teams according to their needs. Overall, based on my years of experience with Control-M, the improvements have been significant, even though issues can arise that require support from BMC.

In assessing the creation and automation of data pipelines across on-premises and cloud technologies with Control-M, I have found that while on-premises can typically address most needs, certain issues may arise. If my team cannot resolve an issue with a pipeline, we can reach out to BMC for full support. Moving forward with SaaS, I believe many of these issues have been addressed and the integrations look promising, although we have not fully transitioned yet.

For building, scheduling, managing, and monitoring workflows, Control-M offers a straightforward approach. If you are new to Control-M, monitoring is typically your first step, which leads to planning. This requires a bit more involvement to understand the dependencies and actions required for certain batches to feed others. A first-time user can become comfortable with Control-M in a week or two, especially with some guidance. Control-M operates efficiently and learning resources such as videos and online documentation are available for support.

I rate this product nine point five out of ten.

At the organization, customers place an order on the website and pay using a credit card, gift card, or another payment method. However, the customer journey does not end with payment. Control-M orchestrates the entire process chain that happens behind the scenes. Customers do not know whether their payment has gone through, as numerous processes must occur to determine payment success, product delivery capability, fraud detection, and payment gateway issues. Control-M helps the organization understand if the payment process has completed successfully. If payment failures occur, the organization needs to know in advance so that customers receive communication about their order status, whether payment succeeded, remains pending, or has been flagged for fraud. This proactive communication prevents customers from calling customer service or following up with internal teams.

Another use case involves product delivery. After a customer places an order, the product must travel through the system. During this journey, logistics problems may arise, stock shortages may occur, or inventory may remain stuck in the warehouse rather than being delivered to the store. Control-M orchestrates the delivery process so that customers receive notification when their delivery is created and communication flows to logistics and supply chain teams. The system alerts when issues occur, allowing the organization to proactively inform customers about their orders and address problems before they escalate. Rather than taking a reactive approach, Control-M enables a proactive method of ensuring process alignment and preventing disruptions.

Currently, Control-M's ecosystem does not include embedded machine learning or artificial intelligence. However, the organization is undergoing S/4HANA transformation. SAP S/4HANA includes Joule as an AI assistant. Once Joule is integrated into the ecosystem, the plan involves exploring how it can integrate with Control-M. Control-M has already released Jet AI and Copilot functionality, which may be utilized to orchestrate processes faster and derive greater value rather than manually orchestrating jobs and creating key dependencies.

The ecosystem does not currently use Kubernetes. Cloud-native solutions exist for digital and data platforms, which currently run on their native scheduling systems not integrated with Control-M. Future integration of these systems with Control-M is being considered.

A few years ago, during busy seasons such as Boxing Day or Black Friday sales, legacy systems became overwhelmed by data volume. There was no way to monitor when system load increased or when incoming volume exceeded capacity. Orders would enter the queue but not process, preventing customers from receiving promised two-hour deliveries. The system could not pass orders to downstream systems in time, disappointing customers expecting timely gift delivery or holiday purchases. Implementing monitoring in these systems with advance alerting resolved this roadblock. If a roadblock occurred, alerts were sent in advance so the organization could take appropriate action. This monitoring generated additional business revenue by ensuring customers received priority before technology limitations. Preemptive system monitoring confirmed that underlying and dependent processes across systems and downstream SAP systems remained healthy, enabling timely product delivery to customers.

The most important feature is risk level management and SLA management. Orders and shipments arrive at warehouses, goods receiving must occur, goods must move to stores, and customers pick up their purchases. If stock cannot move from warehouses to stores, this represents a main use case. Control-M orchestrates this process, ensuring stock reaches stores so products can be sold and revenue is not lost. In the retail industry, every minute of failure represents a dollar lost. Control-M orchestrates processes and sends alerts in time so appropriate action can be taken before issues escalate.

The organization operates within a SAP ecosystem containing a mix of legacy and non-legacy systems along with cloud applications. The organization is pursuing S/4 transformation with cloud applications like BTP and integration suite. Control-M orchestrates not only specific systems but integrates multiple SAP and non-SAP systems, creating orchestration flows as a chain process rather than isolating individual platforms. Once data is extracted or files are generated, they can connect to API platforms or middleware to transfer files to destination systems. Non-SAP or non-legacy systems can consume data immediately as part of orchestration without manual intervention, email, or Teams chat.

Control-M orchestrates all processes with dependencies that can be time-based, event-based, or scheduled batch jobs running automatically at defined times. Sequencing flows automatically through digital, SAP, Salesforce, or SAP-driven systems based on respective schedules. Everything is automated and sequential with no manual touch required. Any process failure triggers alerts configured in Control-M and integrated to Snow, creating incidents that allow support teams to respond immediately rather than using reactive approaches.

The organization conducts six-monthly SOX audits. Most batch processes run overnight, creating deliveries or handling goods receiving. Any failure creates alerts that help manage operations in advance. Audit reports review incidents from six-month or annual periods, examining audit trails. Control-M provides complete audit logs of job failures, what happened, how they happened, and complete job logs that traditional SAP cannot maintain for long periods. Control-M logging helps answer SOX compliance questions from governing bodies.

Control-M's service level agreement feature is particularly valuable. For large processes running for many hours, service level agreements can be applied to show completion time, duration, and expected finish time down to the second. This allows proactive communication to business stakeholders about process duration rather than having operations teams pestered about where processes are hanging. The business gains complete visibility and stakeholders remain informed, preventing midnight alerts and providing preemptive time management.

Last year, there were questions about Control-M because certain BTP job types and SAP integration job types were not defined within Control-M. Integration required workarounds using web services to connect to BTP. Now job types have been configured to enable easier cloud application integration, eliminating the need for workarounds and providing out-of-the-box availability to integrate iFlows and integrations, improving operational management.

Additional features are difficult to specify at this stage. However, as artificial intelligence continues evolving, Jet AI capabilities and intuitive dashboards would benefit the business significantly. Since every job ties to its own SLAs, making dashboards more relevant and intuitive would allow business teams to access them directly and see where processes are held up rather than relying on operations teams. This capability would represent a significant win for business customers.

Control-M has been used for the last 16 years throughout an 18-year career.

Experience with both on-premise and SaaS Control-M demonstrates improvements in stability. On-premise versions experienced hiccups and outages during upgrades or version updates, with 10 to 15 minutes of downtime requiring job holds. In the retail or banking sectors, small disruptions represent significant operational impacts. The SaaS solution shows marked improvement, with updates occurring on backend servers causing no disruption to processes or flows. The SaaS solution runs smoothly without hiccups, roadblocks, or outages, providing consistent service performance.

The organization has experienced significant growth. Migration from on-premise systems to cloud applications revealed the need to orchestrate on-premise legacy systems alongside cloud applications. Web services provided an alternative approach for integrating with BTP or integration suite until Control-M evolved. Currently, more applications are being onboarded daily into Control-M through BTP, S/4, and AWS solutions, enabling better operational management through comprehensive end-to-end orchestration rather than isolated platform dependence.

Scalability depends on use cases and organizational processes. At initial Control-M onboarding, approximately 4,000 jobs were configured. Currently, the organization has scaled to 7,000 jobs, representing a 20 percent increase in job executions and job scheduling. More job types continue entering the ecosystem along with Jet AI and Copilot capabilities, helping organizations identify process gaps and determine where Control-M integration improves operations. More intuitive information from Control-M enables effective process streamlining. The potential for continued growth exists with new job types and innovations being embedded within Control-M, benefiting all organizations.

Control-M customer service receives a rating of eight. The sales support team at BMC and associated counterparts have been supportive in onboarding and recommending solutions for specific use cases. When ready-made solutions were unavailable, they provided guidance on achieving desired outcomes. For example, when BTP or SAP integration suite job types were not available last year, BMC recommended using web REST API services to integrate processes, enabling orchestration of on-premise legacy systems with cloud applications. Though some nuances existed where immediate answers were not available due to organizational time pressure or required research and development, turnaround time from BMC has improved significantly in the last six months, demonstrating faster response to customer needs.

Traditional SAP scheduling tools run jobs within the SAP system only. If dependencies exist between non-SAP or non-legacy systems, creating these dependencies requires manual activity followed by emails, Teams messages, or phone calls to notify colleagues when jobs complete. Control-M orchestrates all chain events within the system itself, removing the burden of manual coordination. Once a job finishes, Control-M automatically triggers the next step without manual intervention. If processes break, Control-M creates events, alarms, or incidents in systems like Snow, allowing respective teams to take required action. This capability helps larger organizations manage operations effectively.

As an end user of Control-M, the organization uses the application rather than implementing it directly. Earlier in the career, the GUI was used as a client, making migration to SaaS seamless. No hiccups or roadblocks occurred during migration. Jobs visible in the on-premise system maintained the same status in the SaaS product. Whether executing, executed, or successfully processed, the same job status replica appeared in SaaS. No disruption occurred in processes, and no issues were encountered during migration.

Revenue increase results from ensuring customer fulfillment. If customers cannot receive their orders, they do not return to stores, negatively impacting NPS scores and customer feedback. Delivering to customers ensures recurring orders. The organization has seen revenue increases of 20 to 30 percent over the last two years. Before implementing Control-M orchestration, proper communication was lacking and customers or internal teams had to chase orders to determine status or identify blockages. Now all processes are streamlined and customers receive proactive notifications about order status. Customers are well informed, making their experience easier and operational processes easier, resulting in the 20 to 30 percent revenue increase.

The GUI portion in the previous SaaS product offered good value through single licensing. The current SaaS application ties pricing to the number of executions, representing a model change by BMC. For critical processes requiring many executions, higher costs result. Organizations must tidy up their processes to derive maximum value from Control-M and reduce costs. The execution-based model encourages organizations to design and orchestrate processes in structured ways rather than running as many executions as desired. Organizations should be pragmatic about process design and orchestration, determining processes according to available licenses and ensuring maximum value extraction rather than running excessive executions or burning costs simply because the tool is available.

Previous organizations discussed Tivoli as a scheduling tool, though personal experience with it is limited. The understanding is that Tivoli cannot integrate with non-SAP systems or create dependencies with them. Control-M handles orchestration effectively and can integrate with mainframe systems, data platforms, and non-cloud applications, creating independent orchestration and process flows. Control-M has proven effective in orchestrating processes across various organizational environments.

Control-M deserves a rating of ten as an orchestration tool because it orchestrates end-to-end process flows and provides alerts when issues occur. Control-M integrates with service management tools such as Snow or Workday, creating alerts and dashboards that help support teams identify failures immediately rather than running through hundreds of organizational processes. Control-M enables pinpointing exactly where processes have failed and triaging from that point of failure. BMC has effectively integrated different databases and job types into Control-M to help orchestrate end-to-end processes.

I mainly use Control-M to automate daily data processing, financial reporting, database updates, and schedule application jobs because it helps ensure data is available on time and reduces manual work across business operations.

We are using Control-M for financial services.

We use Control-M for approval mainly for sensitive financial data processing, production deployment, and critical business workflows. It helps ensure proper authorization, follow company policies, and reduce risk before important jobs run automatically. This also improves accountability and compliance.

We mainly use Control-M to schedule and control workflows, including data preparation, model training, and deployment tasks. Integration with tools such as Azure ML and MLflow can help manage dependencies, automate execution, and improve overall workflow reliability.

Control-M supports our DataOps and DevOps initiative by automating data pipelines, managing dependencies, and scheduling application deployment. It improves coordination between teams, reduces manual tasks, and helps ensure reliable and timely delivery of our business processes.

I use Control-M to orchestrate workloads across on-premises environments. It helps manage job dependencies, centralize monitoring, and maintain consistent execution across different platforms. This improves flexibility and makes operations easier to manage.

We use Control-M to manage both SAP and non-SAP workflows. It helps schedule and monitor SAP ECC and BW jobs alongside database operations, data transfer, and application tasks. Centralized monitoring makes it easier to manage dependencies and ensure smooth execution across different systems.

We use Control-M to manage data processing tasks through job dependencies, scheduling, and automated workflows. It helps coordinate tasks across databases, SAP systems, and cloud platforms. We monitor job statuses through a centralized dashboard and use alerts to quickly handle failures. This ensures smooth data flow and reduces manual effort.

Our DataOps and DevOps stack includes Control-M, SQL databases, SAP systems, and a cloud platform. We use these technologies for data integration, workflow automation, application deployment, and job monitoring. Control-M helps connect different tools and manage dependencies, ensuring smooth execution across environments.

We are planning to use the Control-M Python client or API in the coming days.

Control-M has improved our overall operational efficiency by reducing manual work, minimizing job failures, and ensuring timely data processing.

Control-M has helped us save around 25 to 30 percent of manual monitoring time. We have also seen fewer job failures, faster issue resolution, and better SLA performance through automated scheduling alerts.

Control-M helps free up IT personnel by automating repetitive scheduling, monitoring, and troubleshooting tasks. This allows my team to focus more on important activities such as system improvement, security, and business-critical operations. This is very important for our organization because it improves productivity and reduces operational workload.

Control-M has helped us achieve faster workflow delivery, improved SLA performance, and fewer job failures. Automation has reduced manual effort and improved error handling. Better monitoring and audit trails have also strengthened operational efficiency and compliance across our organization.

Control-M has improved our daily operations by automating job scheduling and reducing manual monitoring. We have reduced manual effort by around 30 percent and improved job completion time by 20 percent and reduced delay through faster failure alerts. It also improved SLA management and overall team productivity.

Control-M is helpful because it simplifies workflow management, reduces operational complexity, and brings all workflows under one platform.

Before implementing Control-M, audit preparation involved manually collecting job execution reports and approval records from different systems that took too much time. After implementation, everything is centralized. Automated logs and audit trails have made it easier to retrieve information, verify activity, and prepare compliance reports very quickly.

Control-M's AI capability provides fairly accurate and reliable output for monitoring and identifying workflow issues. These capabilities help analyze job patterns and highlight potential problems. However, output should still be reviewed by a team, especially for security and governance-related decisions.

Control-M integrates well with AI and ML tools by supporting automated workflows, data pipelines, and scheduled model execution. It helps manage data processing, monitor ML jobs, and ensure timely execution. Better integration with AI platform predictive monitoring would make it even more useful for us.

The approval process in Control-M is fairly easy once everything is configured. It helps organizations maintain better control over critical jobs, avoid unauthorized changes, reduce operational risk, and improve compliance. It also gives the team more confidence in automated processes.

Control-M provides good visibility through its dashboard and allows business users to track job statuses, monitor progress, and identify failures without depending completely on our IT teams. This is important for our organization because it improves transparency, saves time, and helps the team make decisions more quickly.

Control-M requires clear audit trails, proper tracking of data and model changes, and role-based access for production AI workloads. Control-M helps monitor workflow execution, maintain accountability, and enforce approval processes. These controls are important for security, transparency, and compliance.

Control-M helps us manage workflows across on-premises infrastructure. We use it to schedule jobs, manage dependencies, transfer data, and monitor execution across different environments. This improves visibility, reduces manual efforts, and ensures smooth coordination between all environments.

Control-M performs very well in building, scheduling, managing, and monitoring workflows. Its interface makes it easier to create jobs, manage dependencies, automate repeated tasks, and track execution in real-time. Failure alerts and centralized monitoring help my team resolve issues quickly. I believe it improves productivity, reduces manual effort, and ensures reliable workflow execution.

Control-M helps us meet SOX and audit requirements through detailed job execution logs, audit trails, role-based access, and approval workflows. These features help track who executed or modified jobs, maintaining accountability and providing useful records for compliance checks and regulatory reporting.

Control-M integrates with ITSM tools such as ServiceNow to improve incident management and approval processes. It helps automatically raise alerts for failed jobs, create incidents, and track resolution statuses. This improves communication between teams, speeds up troubleshooting, and ensures better control over production changes.

Workflow orchestration is very important for our DataOps and DevOps initiative. Control-M helps us automate production workflows, manage dependencies, and monitor jobs across different environments. It reduces manual efforts, improves delivery speed, minimizes errors, and ensures critical business processes run reliably.

Centralized management of workflows across on-premises servers is very important for our organization. It improves visibility, simplifies scheduling, reduces manual efforts, and ensures reliable execution across different environments. It also gives us flexibility to scale as business needs grow.

Control-M has made it simpler to create, integrate, and automate data pipelines across on-premises. Its centralized interface helps manage job dependency, schedule workflows, and monitor execution. Although initial configuration takes some time and effort, it reduces manual work and makes overall pipeline management easier.

Control-M can improve AI capabilities for governance and security by adding intelligent threat detection, automated compliance checks, and unusual activity monitoring and role-based access recommendation. AI-based alerts and audit reports would help identify risk early and maintain better control over sensitive workflows.

One challenge is managing dependencies between Kubernetes jobs and cloud-native applications and traditional batch processes. Monitoring and troubleshooting across different environments can also be complex. However, Control-M provides centralized scheduling, better visibility, and automated workflow that is helping my team improve coordination, reduce manual work, and manage hybrid environments more efficiently.

Control-M is currently used across several important areas of our organization, mainly for job scheduling, data processing, workflow automation, and monitoring. We plan to increase its usage for cloud-based automated workflows, AI and ML operations, and DevOps processes to improve automation and efficiency.

Around 25 to 30 users across our organization are using Control-M, including IT operation engineers, DevOps engineers, data engineers, database administrators, and system administrators.

I would rate Control-M eight out of ten for scalability. The solution is very good.

BMC is very supportive.

I would rate customer service an eight.

I did not consider alternate solutions before Control-M.

BMC helped us understand the migration process by providing guidance on planning, workload assessment, and migration strategy. Their support helped identify dependencies, prioritize critical jobs, reduce migration risk, and ensure a smoother transition to Control-M with minimum disruption to daily operations.

We have also seen a good ROI of around 25 percent because it improved job completion efficiency and reduced operational delays. Faster issue resolution and fewer job failures have also helped us save time and improve overall team productivity.

Control-M is very helpful for handling all the workload management because we can handle everything from a centralized dashboard. It reduces manual intervention.

I would recommend Control-M for anyone considering this solution for their operations.

I have been using Control-M for more than five years. Our main use case for Control-M is using it to automate, orchestrate, and monitor our complex business workflows across multiple applications, servers, databases, cloud platforms, and data pipelines from a single platform.

We are using Control-M for batch processing where we are running end to end of our transactions or any kind of report generation. We are also using it for data pipeline orchestration by extracting data from ERP or from other solutions to transform and load into our warehouses or getting the data directly from our Power BI environments. The third use case is for application workflow automation where we are executing workflows involving our servers, APIs, and custom applications in the correct sequence.

We are also using it for IT operations automation by scheduling backups, system maintenance, log cleanup, patching, and health checks regularly.

It is also being used for SLA monitoring and for our cloud and hybrid workloads because we are coordinating jobs across our multiple cloud providers such as Azure, Google Cloud, and AWS, as well as for on-premises infrastructure. It also tracks our critical jobs against deadlines and sends alerts or escalates if they are delayed or there is any failure.

Control-M offers several best features that are widely available in the market, and it has very large areas of strength. It has an excellent orchestration feature where for any job failure or any kind of notification triggers, it instantly notifies all the users. It has a very huge integration ecosystem where you can integrate any kind of third party application or your legacy application such as SAP, Informatica, Oracle, Azure, DataBricks, or anything else.

It is also providing central visibility so that our operation teams can monitor thousands of jobs from one dashboard. It provides benefits such as SLA tracking, failure analysis, audit trails, historical executions, and business service monitoring. It is also very helpful for our enterprise governance because it provides governance mechanisms such as Role Based Access Control, audit logs, compliance, and approval workflows.

We are using integrations through the APIs, integrating our SAP, Snowflake, AWS, Azure, or DataBricks directly with Control-M so that we are getting information from very truthful sources. It has a very large integration mechanism, so we do not have to worry about whether the application is working or not. We have peace of mind on our end.

One of the great things about Control-M is that it has a very large community base across the globe. If you need help at any moment, you can easily reach out to the community groups and the community members, and the support system is also very excellent, so you do not worry about who to contact about small things or any kind of complex related issues. You can get help at any moment of time.

Even though Control-M is considered a market leader, there are several areas where customers find they want improvements. A simpler user interface is the highest priority. Many users or team members have provided feedback that creating and managing workflows could be more intuitive. Potential improvements could be a modern drag-and-drop workflow designer, easier job configurations with fewer clicks for common tasks, and better dashboards for non-technical users. Introducing AI-powered operations would also be beneficial.

Control-M offers automation, but AI capabilities could go further, providing better cloud networking experiences, easier integrations, enhanced analytics, lower licensing complexity, and faster troubleshooting would make it more beneficial for enterprises. Stronger AI governance and improved DevOps support can be added in some parts.

Control-M has AI capabilities, but regarding AI governance, future capabilities could include features such as AI-generated workflow recommendations, policy-based approvals, explainable automation decisions, and risk scoring for critical workflows. That would be beneficial for the enterprise.

Cost control improvements in customer support and better UI, business process visibility, some features such as digital twins for workflows, autonomous self-healing, and intelligent resource optimization are a few other needed improvements.

I have been working in my current field for more than 15 years.

Control-M is stable.

Control-M's scalability is also stable.

Customer support is good. I would rate it nine out of 10.

We have evaluated and used different solutions previously. In my experience, I have seen tools such as Windows Task Scheduler, Broadcom Automic, Stonebranch, and Redwood RunMyJobs. We switched because we found Control-M to be more powerful and more useful according to our organizational requirements.

My experience with pricing, setup cost, and licensing is moderate. Control-M is positioned as a premium enterprise solution, so the pricing is generally on the higher side compared to open-source schedulers or simpler workload automation tools. The initial setup cost is high because it generally includes licensing, infrastructure, and implementation services. Although the licensing is comprehensive, it can be complex depending on factors such as the number of environments and agent modules you are leveraging. I would rate the pricing competitiveness as six out of 10, licensing as seven out of 10, and ROI for large enterprises such as us as nine out of 10. The value for buying Control-M is between 8.5 to 9 out of 10.

The time saved is about 22 to 25 percent. The return on investment is overall nine out of 10, and Control-M saves money on human efforts by 35 to 40 percent, reducing our overall headcount.

It has returned the investment that we have because it has all the features that any organization requires. For example, it has automated orchestration features, higher operational efficiency, provides business benefits, reduces our downtime, enables faster processing, offers greater reliability, and improves visibility across our organization by using a central dashboard for monitoring jobs for any kind of status failures and SLAs. It also helps with our regulatory compliance, scalability, and business continuity areas.

Broadcom Automic and Stonebranch are a few of the options we evaluated.

The biggest lesson I learned from using this solution is that Control-M is my favorite. The biggest lesson is that workflow orchestration is much more than scheduling jobs. It is about managing dependencies, handling failures gracefully, ensuring SLA compliance, and providing end-to-end visibility into business processes. As environments become more distributed across cloud and on-premises systems, centralized orchestration becomes increasingly important.

Control-M is very helpful for building, scheduling, managing, and monitoring our workflows. It is a very mature, highly reliable platform that performs particularly well in large enterprise environments with complex and business-critical operations. For managing workflows, it offers centralized administration, role-based access, workload prioritization, audit trails, and lifecycle management. This helps standardize operations across multiple teams, environments, and geographic locations.

It reduces our manual efforts and operational errors by 30 to 35 percent. It provides a unique and centralized dashboard for better visibility into workflow execution, which again helps us reduce our manual efforts by 25 percent. It provides faster incident detection and recovery and manages our incident responses by 25 percent. It improves compliance and auditability by 12 percent and enables easier management of hybrid cloud and on-premises workloads, resulting in improvements of about 15 to 20 percent.

Integrating Control-M with technologies for our DataOps and DevOps processes is moderate. I would rate it seven out of 10. Control-M provides a broad set of out-of-the-box integrations and APIs for enterprise technologies. In our environment, we have integrated Control-M with technologies such as SQL databases, cloud services deployed in AWS and Azure, REST APIs, Kubernetes, and enterprise data platforms. These integrations enable us to orchestrate end-to-end workflows from data ingestion through processing and reporting while improving our overall visibility and reducing manual intervention. Our technology landscape involves containerized applications, cloud-native workloads, CI/CD applications, pipelines, and API-driven services. We ultimately reduce our reliance on legacy batch scripts and manual scheduling tools by consolidating all workloads into Control-M. This centralized approach improves governance, standardizes scheduling, simplifies monitoring, and strengthens SLA management.

For building, scheduling, managing, and monitoring production workflows, I would rate Control-M nine out of 10 because it helps us in all these areas. It is one of the most natural enterprise workflow automation platforms, particularly for organizations running complex business-critical processes across hybrid environments. Workflow orchestration is extremely important to both DataOps and DevOps initiatives because it ensures that the data pipeline executes in the correct sequence, and data is available on time for downstream analytics and reporting to remain reliable. The biggest business value is reliability because instead of managing hundreds or thousands of independent jobs, organizations gain end-to-end visibility, automated failure handling, SLA tracking, and governance, which ultimately reduces operational risk, improves productivity, and helps ensure critical business processes are completed on time. I would highly recommend this solution to prospective buyers. I have given this review an overall rating of 10 out of 10.

My main use case for Control-M is a PO validation use case where I receive an email with details. I capture the details from the email and process a PO, which is basically to approve or reject a PO.

Control-M helps in this PO validation process by orchestrating the different steps involved in this workflow. When a new purchase order is received, the process involves extracting information from a PDF, validating key details such as a PO number, vendor information, payment details, payment terms, and approval details and signatures. Control-M can coordinate these activities by managing job dependencies, ensuring that each stage starts only after the previous one has completed successfully. For instance, the validation stage should begin only after document processing and data extraction are complete. If a job fails or encounters an issue, the workflow can be monitored and alerts can be sent to the support team to investigate further.

Another important aspect of my use case is visibility and operational control. In the PO validation workflow, multiple documents are processed simultaneously, and delays or failures in one stage can impact the downstream activities. Control-M's centralized monitoring and dependency management capabilities can help me identify bottlenecks. It helps me track job execution and ensures that the overall workflow runs in a structured manner.

Control-M has positively impacted my organization by improving operational efficiency. Particularly in environments where multiple processes and systems work together, one of the biggest advantages is reducing the need for manual coordination and monitoring of individual jobs. Coordinating processes like document processing, data extraction, and validation through a structured workflow reduces errors, delays, and improves process consistency. Centralized monitoring makes it easier to help me take corrective action without manual checks at each stage. I see value in improved visibility, better dependency management, and more controlled execution of recurring processes. While I haven't personally measured specific productivity gains, these capabilities contribute to better operational efficiency and scalability in an enterprise automation environment.

Regarding positive outcomes, time saved is definitely one of the metrics, and ROI is another. With multiple stages involved, this happens sequentially, leaving no space for error. Without centralized orchestration, teams monitor individual jobs and manually coordinate dependencies. Control-M organizes these activities. If there is a failure, it gives clear visibility on the failure, and the potential improvement lies in error eradication. Another potential improvement is reduced manual monitoring; I don't have to look at the nitty-gritty of each process. I can trust the process for better consistency.

The best features Control-M offers, which I found most valuable, are centralized workflow monitoring, job scheduling, dependency management, and automated failure notifications. The ability to define dependencies between different jobs is particularly useful because it ensures that activities supposed to be processed later execute only when their prerequisites are completed. I also see strong value in its integration capabilities, especially when coordinating workflows across databases, enterprise applications, file systems, and cloud environments. Centralized visibility makes it easier to monitor execution.

One thing worth highlighting about the features is Control-M's integration capabilities. In an enterprise environment, workflows rarely operate within a single application; multiple applications are involved. For instance, they involve databases, file transfers, CRM systems, ERP systems, cloud platforms, and other automation tools. Control-M's ability to orchestrate workflows across these applications or environments is a significant advantage. I also find centralized monitoring very valuable. Having visibility into job statuses, execution histories, dependencies, and failures from a single interface makes troubleshooting easier, allowing teams to identify issues before they significantly impact any business process. For my scenario, that means better coordination between document processing, validation, and filling up values in different applications and all the downstream updates. These capabilities can help improve operational visibility for leaders to see how the team is performing and reduce manual intervention.

Regarding Control-M's AI capabilities, I believe governance and security are important considerations, especially when introducing AI capabilities in enterprise workflows, and I think Control-M is doing a great job at that. In the PO validation scenario, documents may contain sensitive vendor information, PII, payment details, and approval records. Therefore, access control, data protection, audit trails, and traceability are essential. I expect AI capabilities to operate within established enterprise security policies with appropriate role-based access, and human oversight should be maintained for critical activities, particularly financial approvals and exception handling. From an enterprise perspective, I see governance, compliance, and secure integration as important factors in building trust in any AI-driven automations.

Regarding Control-M's AI capabilities, accuracy and reliability of output are particularly important when introducing AI. I believe AI capabilities should be supported by proper validation mechanisms and exception handling, which Control-M is greatly looking into. I would say it provides significant value to enterprise automation and remains committed to this idea.

One area where I feel Control-M could improve is in overall user experience and ease of configuration. For new users of workload automation, making workflow creation, dependency configuration, and troubleshooting more intuitive would help them adapt to Control-M faster and reduce the learning curve. I would also like to see more intelligent monitoring capabilities, such as AI-assisted failure analysis or root cause suggestions, or recommendations for optimizing job execution. This improvement would help companies adapt faster and allow teams to move beyond simply identifying failed jobs. Another area worth enhancing is the integration experience with modern AI and RPA platforms for easier orchestration involving intelligent document processing APIs and automation bots. Overall, Control-M has strong orchestration capabilities, but improving usability, intelligent monitoring, and simplified integrations would make it more accessible and powerful for automation teams.

Regarding needed improvements, the learning and onboarding experience for someone getting started with Control-M could use more practical or end-to-end tutorials. Industry-specific implementation examples would be greatly helpful. Clear documentation with real-world scenarios, troubleshooting guides, and best practices for integrations will make development time faster. I would also value more hands-on labs or guided exercises covering complex workflow orchestration, dependency management, and API integrations, which would help users move from understanding individual features to implementing complete enterprise workflows.

I have been using Control-M for about a couple of months.

Control-M is very stable.

For what I have tested until now, Control-M's scalability is great. When UAT started, it began with two employees, and about 25 to 30 processes ran concurrently, which it handled very well. For scalability, on a scale of 1 to 10, I would definitely rate it a 9.

Customer support has always been great. Control-M has a thriving community, and I have received extensive help for any queries. I value timely responses and clear troubleshooting guides; while I would prefer better documented troubleshooting guides, the support has been very great, which is crucial for new users and administrators. I would rate customer support a 9 out of 10.

I have previously worked with various competitors, including UIPath, Automation Anywhere, and Microsoft Power Platform. However, these primarily address RPA and business process automation requirements, while Control-M focuses on enterprise workload scheduling and orchestration. My exposure does not describe a direct migration from another platform to Control-M; rather, I see Control-M serving a different purpose, particularly when an organization needs centralized scheduling, dependency management, and monitoring of complex workflows across multiple enterprise systems.

I have seen a return on investment, which can be measured through monetary savings showing fewer employees needed. I am looking at saving more than $100,000, which reduces headcount by about 12 to 13. These metrics I have calculated indicate the potential ROI stemming from reduced manual operational effort.

My understanding is that Control-M follows an enterprise licensing model, and overall cost depends on the deployment scale. In an on-premises deployment, organizations also need to consider infrastructure provisioning, installation, configuration, maintenance, and support costs. However, for larger organizations managing complex workloads, the value should be evaluated against operational efficiency, centralized monitoring, and reduced manual effort it provides.

Before choosing Control-M, I evaluated options such as UIPath, Automation Anywhere, and Microsoft Power Automate.

On a scale of 1 to 10, I would rate Control-M an 8. This is based on the brief hands-on exposure I had and my assessment of its enterprise workload orchestration capabilities.

I chose 8 because Control-M offers strong enterprise workflow automation, particularly in job scheduling, workflow orchestration, dependency management, and centralized monitoring. What keeps it from being a 9 or 10 for me is the opportunity for improvement in user experience and the learning curve for new users. I would also like to see more intelligent monitoring, such as AI-assisted troubleshooting.

Control-M is mostly deployed on-premises in my organization. Although I have had brief hands-on exposure to Control-M through practical sessions, I understand it supports different deployment modes based on an organization's infrastructure. Here, it is predominantly on-premises.

I recommend Control-M to organizations looking for a centralized enterprise workload automation and orchestration platform. Before adopting it, it is essential to clearly identify processes needing orchestration, evaluate integration requirements, and invest time in learning job scheduling, dependency management, monitoring, and failure handling. For organizations already using RPA or AI-based automations, I suggest exploring how Control-M can complement those solutions rather than compete or replace them. For example, in the PO validation process, AI can handle document extraction and validation while Control-M coordinates the overall workflows. My advice would be to start with a well-defined use case, run a practical proof of concept, and evaluate scalability, operational benefits, and ease of administration before committing to a wider enterprise rollout.

I have been using Control-M for more than six years. Initially, it was mostly just monitoring the jobs, but now I also do some troubleshooting around that.

My main use case for Control-M these days involves multiple jobs running in our contact center systems. We have multiple nodes to begin with, and some of them are responsible for maintaining the predictive dialer calling list for records sourced from multiple platforms. Along with this, we also have certain jobs deployed for our reporting purposes, where our databases are synchronizing with other Genesis databases. Additionally, we have multiple log archiving systems or jobs that have been deployed as well. We have some ServiceNow jobs that trace and manage the employee profiles, and then we have some speech-related Nuance jobs scheduled as well.

One of the major use cases of Control-M that we use is our log archival process. This process integrates file movements with job scheduling and enables secure file transfer by using both FTP and SFTP file transfers. It triggers the job when the file arrives, and then it also validates the file completion and size before actual processing. So, in the contact center cluster, one of the jobs that we have is the Informat job that extracts the caller data from Informat and transfers it to various downstreams such as BIH or Connect Direct. Apart from this, we also have various SQL stored procedure purging jobs in Genesis, and there is one main, important Cassandra job that runs on the Cassandra nodes, selected for incremental backing up. The Pulse housekeeping, where the job runs and cleans the ECP snapshots every 30 minutes, is one of the major, significant jobs that we use. Along with this, we also have a cyclic job that runs every 15 minutes on each of the MCP nodes. Every 15 minutes, it resyncs the job, basically for the audio file resyncing that happens from one of the applications to a given directory. This means the most recent file that has been uploaded is put into all the MCP boxes every five seconds, and then the right announcement gets picked for the user to hear.

The log job archival basically copies and archives all the Genesis log files for a period of retention given. It logs the files from site one to a specific site location and site two to another specific site location. This is not only in production; it is for all environments including Dev, SIT, and QA that we have. We have also automated that all archived log files older than three days are gzipped, and all these files will be moved to a different archive location than the location that it has initially been sent to. It also makes sure that we are masking and the schedules are followed, which are not getting archived.

The best features Control-M offers that make all this possible for me include the job scheduling, which is most importantly critical. It enables us to schedule jobs across multiple platforms such as Unix and Windows together, and also the jobs running at very specific times help eliminate a lot of manual task execution by triggering based on either a file arrival or even a system event. It also enables us to run the jobs in the right order. Along with this, we also have the data pipeline and ETL automation, which helps various data engineering and analytic teams automate the Hadoop jobs and trigger downstream analytics after the data ingestion. All the ETL processes are managed better in terms of both data validation and quality checks. Additionally, the business-critical processes meet deadlines, for example, the ServiceNow data that we have to receive before 8:00 AM in the morning, or the month-end or quarter-end batch runs that need to happen, are done in a timely and accurate fashion.

The job scheduling and sequential jobs have been the most important feature of all. The rsync specifically, where the cyclic jobs run every 15 minutes without any manual intervention, makes sure that the process is streamlined and does it without any manual intervention, which helps a lot.

Along with this, end-to-end workflow orchestration, which is basically event-driven or file-driven, differentiates Control-M from any other basic schedulers. It is not just about running a job on a schedule, but it also enables complete business workflow from an application to multiple platforms and multiple environments. Dependency-based execution ensures that the previous job or the upstream job has completed before starting with the event, and multiple other conditions can also be set. The cross-technology enablement allows one workflow to span across multiple systems, from cloud services to databases to Unix and Windows, providing a single point of control for everything.

Control-M is a very nice product that is practical, but it is challenging to understand how certain features work. The UI and user experience sometimes feel complex and can be simplified a little bit to provide cleaner dashboards. The major complexity is the licensing complexity and access-related challenges.

Simplifying the UI can provide us better use of the application itself. Probably some more documentation around how to use the schedules or the alerting systems would also be helpful.

Control-M systems have been stable even during upgrades and patches, with very minimal disruptions to the system, so it has been stable throughout.

We started using Control-M with very few teams starting with about 50 users, but now we have about 3,000 plus users using Control-M in my organization.

Control-M customer support has been good, but we have not had the opportunity to extensively talk to them because we have an in-house support team that we reach out to before contacting the actual BMC vendor.

The other alternatives that we previously used were mostly cron jobs and other system jobs. We briefly used IBM workload automation but did not proceed with that. We also used Jenkins with some plugins, but ultimately, we did not pursue alternatives such as AutoSys. I believe Control-M is hard to replace.

The organization explored AutoSys and IBM workload automation before ultimately choosing to go ahead with Control-M.

We have a team of 25 to 30 members who are responsible for the deployment and maintenance of the Control-M setup. Our team includes architects and designers as well as deployment and support personnel.

I see a return on investment with Control-M. The other challenge we currently face is that they have started charging us, which is more of an enterprise-level decision, as they began charging us for each job run we have.

I do not have a major role in terms of pricing, setup cost, and licensing. Our team was only not allowed to access Control-M for a certain duration due to licensing constraints, which I feel is a challenge, but I was not directly involved in any of these pricing, setup, or licensing related discussions.

The impacts that Control-M has caused for my organization have very visibly increased operational reliability. Before Control-M, most jobs were script-based, such as cron jobs, and there was a lot of dependency on manual monitoring. Until the jobs were reported as failed by the business teams, we would not have had visibility over them. Now with Control-M, we have an end-to-end workflow which is centrally managed. If a node has failed, it sends notifications, and there is a lot of error handling built in. There are multiple automatic retries, reducing human intervention. In terms of issue detection and resolution itself, we have dashboards configured that enable us to get alerted even before the businesses are impacted or the businesses report the impact, allowing us to solve issues proactively. This has also increased productivity improvement.

When one of our reporting downstreams processes data and uploads it to our systems, it used to take an hour for the data to actually reflect. Businesses would notice missing data in the systems when they consumed the data. Now, within the duration when the job runs, it counts the number of rows we have, which means if the job fails, it is notified immediately within that 15-minute duration, helping us rerun the job. This means issues that were reported in an hour's time now get reported within the duration of the job running, which is within 15 minutes, leading to a significant improvement in how we see that the reports are being run.

There is a huge user base in our organization, with about 3,000 users using Control-M. The levels of usage vary; some have read access and just view the jobs, while others perform deployments in terms of job scheduling and other tasks.

We extensively use Control-M to schedule multiple banking-related jobs in varied fields, not just the contact center. We definitely intend to increase the usage.

The biggest lesson I have learned from using Control-M is that it is a best-in-class workload automation platform, effective in building, scheduling, managing, and monitoring complex workflows, especially for critical applications such as DataOps and enterprise DevOps environments where reliability and SLAs play a major role. The cross-system orchestration matters significantly more than speed alone, as it ensures jobs run accurately and efficiently.

My advice for others looking into using Control-M is that no matter how many systems you have, Control-M is the most competent and enterprise-scalable tool available. With various requirements, it is extremely reliable in monitoring and scheduling, making it an excellent choice. I would rate Control-M an 8 out of 10 overall.

The main use cases in Control-M for the organization involve moving from the old infrastructure to the new infrastructure. The organization previously operated on mainframe systems and went through a transformation to modernize the mainframe, which is where Control-M came into the picture. The organization was using an old product called CA 7, and it has been migrated to Control-M. The mainframe itself has been migrated to AWS on Linux. Control-M is primarily used for processing all data overnight, around 800 jobs, of which 500 are critical, making it important for operations.

Currently, AI is not being used in Control-M, but the organization is planning to use AI in the future through Jet in Control-M. From research, Jet is already included in the SaaS version, but the organization is running a standalone version that requires additional configuration. The organization has identified that Jet could be utilized going forward, with some configuration required. Enabling Jet would be the first step to using AI in Control-M, which would help not only from a creating perspective but also provide insights about the schedule and assist in managing the batch.

In terms of integrating Kubernetes or cloud-native workflows with traditional batch processing, Control-M plays a role as the organization is in the process of identifying what else is running outside Control-M. There could be a cloud-based scheduler, a bot-based scheduler, or an RPA-based scheduler. Control-M has different connectors that could be used going forward. The organization is gathering information to identify which other schedulers can be taken into consideration, and a plan will be created to move those into Control-M eventually. The goal is to see everything in Control-M as a one point of orchestration. Control-M has many features and connectors that can connect to existing legacy or systems running schedules, such as CloudWatch or JAMS, which is another scheduler. RPA can also be migrated to Control-M, and Control-M assists in this integration.

The current organization has realized many benefits since implementing Control-M, including significant cost savings in the sense that the amount of work required to achieve something has been reduced because the number of processes required to define has reduced significantly compared to the old system. In terms of hiring extra people, that has also been reduced because Control-M allows fewer people to manage complex systems. The old system required involving more people because of more complexity and more manual tasks to be completed. Control-M is also quicker than the old system in terms of efficiency.

Estimating in terms of percentages, at least 20 percent more efficient has been achieved in terms of cost savings. Approximately 50 percent time saving has been realized. About 50 percent reduction in labor required has also been achieved.

The main feature of Control-M is the interface itself, which is quite easy to navigate. When managing thousands of automation programs, the layout in Control-M makes navigation really easy. A tree view, network view, and flow diagram are available for different perspectives. The second most important feature is the viewpoint that helps identify the critical path of the whole process. Multiple tools in Control-M can map out the critical path easily, allowing identification of the most critical jobs needed to get from one point to another. Control-M can isolate everything else and show a filtered view so focus can be placed on the critical path. The third most important feature is reliability. Control-M works as designed approximately 99 percent of the time. The time feature also allows going back in time to see what happened, which enables pinpointing where problems occurred.

The importance of the first two features mentioned is because they make navigation easier. Control-M visualizes from point A to point B on the screen similar to how Google Maps shows routes, providing easy navigation so users understand what to do next and where to look to find problems easily. The user-friendly interface makes work easier.

In terms of managing the integration and sequencing of data processing across diverse data platforms and cloud environments via Control-M, File Watcher is used, which is an innovative feature of Control-M. File Watcher makes work easier because multiple parameters can be defined to determine whether the file or data has been received. Multiple things can be done around it and customization can be completed per requirements. As soon as something is managed by File Watcher, it has the ability to pass on to the next process. The organization has been using File Watcher, and it is working well and is quite stable.

In terms of compliance requirements such as SOX, audit trails, or regulatory reporting, Control-M plays a significant role. The main feature used from Control-M is Control-M reports, which is a built-in feature, and any report can be pulled out as needed. Actions can be tracked to see what was done during the time, and logs can be pulled out from Control-M to help with auditing. If any changes were made to the schedule, the version history feature of Control-M can be used to track changes and determine what went wrong or what happened in the past at specific times of the day or year.

There is always room for improvement. The web interface of Control-M needs improvement. When comparing the web interface versus the desktop interface, the web interface lacks many things that the desktop interface has. If Control-M and BMC could examine the desktop version of Control-M versus the web application and attempt to match those things, that would be a significant achievement.

Control-M has been used since 2008, totaling 18 years of working with this product. Each time working with this product, it feels like a perfect match in terms of how things should work and personal interests, which are all part of the role and the product.

In terms of stability and reliability of Control-M, it is at approximately 99 percent reliability. Control-M has hardly ever crashed, and most of the time if there is a problem, it comes down to either the system, a network issue, or some other issue not related to Control-M in most cases. Control-M uptime is more than 99 percent, making it highly reliable. In terms of stability, Control-M is on a quite high scale.

The architecture of Control-M is designed to be most stable because it has three different components: the enterprise manager, the server, and the agent. Due to this design, it functions as a fail-safe where if one thing goes down, it does not affect the other thing. There is hardly a situation where all three things would go down, making Control-M pretty stable by design.

Control-M scales quite well with the growing needs of the organization. At one previous employer, 15,000 jobs were running at one point. At another, the team had 8,000 jobs, but the overall organization had a total number of jobs of approximately half a million.

In terms of scalability, Control-M is enterprise-level. That is the reason most Fortune 500 companies have adapted Control-M as an automation tool. Control-M is definitely scalable and improving every day, with each new version containing many features.

Although not directly looking at the accounting side of things in Control-M, from what is known, it has paid off in terms of career development and inclination towards Control-M. There is always more inclination towards Control-M because all repetitive tasks can be automated and Control-M is one of the best tools that has been worked with. Control-M is the only tool that has so far matched expectations or actually exceeded expectations in terms of automation. From that perspective, it has definitely rewarded in terms of career investment into Control-M.

I would rate Control-M at nine out of ten on a scale of one to ten. Nothing is perfect in this world, and there is always room for improvement. The web interface definitely needs improvement, and there may be other areas like a Batch Impact Manager that need improvement as well. In terms of reliability and user experience, Control-M is ten out of ten, but in terms of the overall product, it is nine out of ten because it has room for improvement.

The use case for my clients involves everything, as it depends on the customer environment and the platform. I have managed around 60 business cases during the last five to seven years. My customers use Control-M for the management of the production plan to manage other internal applications, cloud services, or infrastructure services. The minimum infrastructure I integrate or manage is around 1,000 jobs per day, and the maximum is around 200,000 per day.

Integrating data ops and DevOps processes for my clients is straightforward.

The scale I work with depends on the service. In the Silkan organization, we have six different houses with different practices. For time and materials services, integration is relatively easy because we work with the customer's technical team to advise and implement the tools based on the previous production plan. The customers I deal with are generally from the CAC 40 in France and are typically very large companies like those in the SBF 120 or SBF 250. For managed services, I engage with companies that have between 500 to 5,000 employees and just below 2 billion in turnover per year.

I work with all industries and do not have a specific sector.

The best features and what I appreciate about Control-M are the power of the tool, the ability to manage different applications, and to have a comprehensive overview of the production plan. Another aspect that my technical team appreciates is the continuous improvement of the tool because we know that more than 200 developers enhance the tool every year. Additionally, I have skills for other schedulers, but my main partner is truly BMC for the quality of the support, the tools, and the relationship with the alliance team.

Deploying Control-M is straightforward, and while the installation can be completed in less than one day, the migration and full integration require visibility on the production plan to estimate the time needed effectively. For different volumes, such as a production plan for 1,000 or 100,000 jobs, the time management varies significantly.

Maintaining Control-M is easy, and it does not require complex updates or renewals.

The area for improvement pertains to the next generation of scheduling tools, particularly integrating the capacity to translate business team feedback into technical jobs. A large part of my role today is transforming business team feedback and needs into technical jobs. However, my main value is to accompany the customer in their technical journey to migrate and manage their enterprise's critical applications. Therefore, the scheduler is primarily a tool for organizing production plans. If it becomes possible for business teams to directly interact with Control-M through an LLM to automatically create jobs with just a review by an engineer, I believe it would save significant time for managed services providers and enhance Control-M's perception among business teams.

I have been working with Control-M for 15 years.

I would rate the stability of Control-M as a nine, as I find it to be stable with minimal bugs, glitches, or downtime.

In terms of scalability, I would give it a seven.

The reason for my seven rating is due to the challenges when I have various environments across different continents and countries. I have to duplicate environments and handle multiple issues. Network problems, even short cuts, can cause some complications. Scalability involves delivering services globally, which customers demand, and while synchronization works well when confined to a single country with one or two environments, more complex arrangements can introduce challenges.

I would rate the vendor's technical support as an eight.

Positive

When comparing BMC Control-M with other products, I note that there are other technologies I manage, such as Vtom and Dollar Universe. However, I focus on managing alliances, so BMC remains my only partner for scheduler tools, although I know the technical team oversees various schedulers.

Control-M setup is straightforward.

I do not typically work with the BMC technical team to create migration strategies, as my experts manage, design, and implement the tools for my customers independently. My relationship is mainly focused on licensing pricing, daily sales meetings, and an annual technical summit to discuss features and future developments of Control-M.

Control-M represents good value. It is important that you understand my limits on Control-M because I sell it for my customers. I have some knowledge about the licensing and the relationship with the partner team of BMC. The main people who manage the alliance with BMC are my associates, including Antoine Bitran, who will have more information about Control-M. Regarding the technical part, I have a few people internally, around 15 experts who are Control-M certified, who handle the technical aspects. On my side and regarding my job, it is more about the relationship, the knowledge, and the feedback about customer satisfaction.

I would recommend trying the BMC cloud environment as the best way to experience Control-M. Implementing a few jobs using a free demo license for a few days or weeks is advisable, though it really depends on the customer's maturity, size, complexity, and criticality of the application, making it a nuanced recommendation.

Overall, I would rate BMC Control-M as a nine.

My use cases for Control-M primarily involve scheduling, especially with applications like SAP, database plugins, and mostly OS.

Using Control-M has made it simpler to create, integrate, and automate data pipelines. We used Airflow to run data pipelines at one point, but it lacked many features that Control-M offered. Fortunately, Control-M had a plugin for Airflow, so we could use Airflow for data pipelines but schedule them via Control-M jobs, which gives us a native, feature-rich scheduling environment and integrates with data pipelines along with Airflow.

BMC keeps up well with the technologies and developments out there.

What I appreciate most about Control-M is its stability. It is stable and usually does not go down. The consistency when the application restarts is flawless, and there is no usual data loss with Oracle as the backend. It is dependable based on my experience as an administrator using BMC across organizations.

Control-M is easy to integrate with other applications and technologies because it provides plugins for the latest solutions by default. If not, there is an Application Integrator built for that purpose, and there are community-driven integrations. From an alerting perspective, BMC had a very good affinity to its own tools, and it did not have ServiceNow integration, which is the most popular ITSM tool. We ended up using ancient SNMP calls to get the alerts out to custom tools. Other than that, integrations are good.

What I dislike about Control-M is that until very recently, and even now, it was not containerized or stateless. It is a stateful application, which is what I still dislike about it. It prefers its host, tries to maintain the connection, and lives on the host. It is not fully containerized or split into microservices, and it cannot be installed on anything with a database connection to start working. It prefers the host on which it was originally installed, and the statefulness of the application is what I do not like.

I have been using Control-M overall for 19 years.

Control-M is stable. Only heavy upgrades require downtime of approximately 30 minutes for the Enterprise Manager and Control-M server applications every year or every six months. Other than that, it runs continuously.

Control-M likes its nodes and is stateful. You cannot just add multiple nodes. It operates on active-passive high availability and does not scale horizontally much. Though capacity-wise it is good, at a given point in time, an instance runs on one VM, not ten. If that goes down, it can switch over to another, but that statefulness is what I do not like about scalability.

It works, but it does not feel scalable. One node at a time is not scalable.

Control-M's technical support is good. For priority cases, they are quick. P1 and P2 cases respond at the earliest, while P3 takes some time based on the time zone you are in and the time zone you are signed up for. They have always been helpful and go out of their way to get things done for me.

I would give Control-M's technical support a score of nine out of ten.

I migrated from Tandem to Control-M as my organization used Tandem earlier. They brought in Control-M because they had mainframe and CS-7, and I believe that is how it started for the current organization. In the previous one, some brought it for SAP, some brought it because of the mainframes. We had cron before Control-M or the mainframe had JCLs being scheduled by the mainframe itself, and then we brought in Control-M in the 1990s.

The initial deployment of Control-M, from what I did hands-on with migrations from the OS and new projects, was seamless. I did not bring Control-M into any organization as such. I went when it was already there. I enhanced, refactored, and migrated everything. In a development environment, it is seamless. You might still sometimes need to call support, but the documentation and a case here and there, along with a little bit of experience using it, works adequately.

Open source tools like Rundeck are more straightforward and easy to install, set up, and manage for someone starting, but Control-M still needs expertise in this area to get it started and working since it has its own configs and the way it works.

Control-M has gotten better at its pricing and with the latest offer for minimal jobs, it is acceptable given the value it provides. Mainframe is a bit expensive regarding the mainframe components, but I believe that is the way for any of the mainframe components. For distributed scheduling, non-mainframe, Unix, and Windows, it is reasonably priced.

I have used alternatives like Rundeck, and people use cron. I have also used Airflow, which provided some other functionality. Rundeck and Airflow are alternatives, and I regard Rundeck as an alternative.

I prefer Control-M more than the alternatives. It is a paid tool, but it is worth it.

I started working in my current field overall with Control-M after my first year in IT, so that would be 18 years.

Control-M requires maintenance on my end for patches when there are vulnerabilities, which are usually released by Control-M. Other than that, everything can be automated.

Control-M takes care of all scheduling jobs, and when you have Control-M, you do not need to use any other tools. If one application has a single server running a couple of jobs, I would still suggest using cron as that would be more suitable. If complexity arises, you can better use Control-M. Because it is not free and not unlimited, you pay per job license. Whenever something simple can be triggered by cron and easily managed, I would personally use cron on a single server with a handful of jobs. If I need alerting, monitoring, a GUI, integrations, and audit and compliance, I would switch to Control-M even if I have to pay per job. Competing scheduling tools are not usually required as Control-M will suffice with data transfer, mainframes, distributed, and everything.

My relationship with BMC is balanced between transactional and transformative. It has been good, but it depends on the organization's priorities. There was a push towards going to open source schedulers, wherein we as an organization had to wind down the job licenses. The licensing calls are always tricky and handled by the vendor management team. They have their own logistics. I am a purely technical person making technical decisions, and I probably push something if it is good, but then it would go that way. BMC genuinely tries to help their customers. They have built us plugins, but at the same time, it is about the money.

Overall, I would give Control-M a rating of eight out of ten because it is not vertically scalable, and they did not fully containerize it or make it into microservices. For that reason, I would give it an eight. If not for those issues, I would give it a nine.

My use case is workload automation. Control-M adapts to my environment by using data pipelines that are orchestrated through Control-M, providing all the data needed for machine learning initiatives.

Regarding specific machine learning operations workflows, I have orchestrated them by integrating Control-M with tools such as MLflow. I have primarily focused on integrations and DataOps. Beyond MLflow, I have also integrated Snowflake and Vortex. Snowflake executes SQL commands and handles data format and data transformations. Once data transformations are completed using Snowflake, Kafka, and Vortex, multiple integrations are performed through API with agents, and Vortex is also utilized.

For ITSM and change management tools, I have integrated Control-M with these systems.

For change management workflows, I create scenarios such as user creation in Active Directory, where the workflow executes and calls API scripts or automation for Active Directory to create the users. These workflows have been created for release management. Once a change is approved internally within the ITSM system with all necessary approvals from the CAB, it is critical to ensure the release happens according to the change guidelines. This requires a sophisticated and reliable system with robustness. Control-M allows me to perform the change and ensure that the data is returned to the ITSM.

The benefits I have seen from implementing Control-M include increased productivity by ensuring jobs are completed based on prerequisites. Control-M reduces my complete total cost of ownership through increased visibility, which reduces multiple tool sprawl and results in cost reduction. I have achieved increased productivity, better SLA management, and ensured that SLA records, SLA points, and SLA metrics meet KPIs. Control-M provides automated failure recovery so that remediation is also taken care of.

Moving into DataOps operations, Control-M ensures that my AI infrastructure and AI initiatives are all supported. The advantages include reduction in cost, increase in productivity, and ensuring compliance.

The best features I find in Control-M are its robustness and job visibility of complex job dependencies and status. When I have a mesh of jobs, Control-M can clearly identify and update and also run all these multiple jobs while providing clear output. Control-M offers robustness, visibility, and scalability.

Control-M helps with policies, approval, and risk gating capabilities. It is very robust in handling workflow approvals within my organization with multiple levels of approvals, even with stable rollbacks and clear status updates. Notifications are proper and informative. Control-M is the right fit for my needs.

Regarding visibility, Control-M provides full clear visibility of all processes. It has centralized visibility so that multiple platforms, multiple orchestrators, and different functionalities across departments can all be visible from a single pane of glass.

Control-M sits on top of many orchestration tools that are available in the market. One area for improvement is that I still have dependency on orchestration tools. If Control-M develops or acquires an orchestration tool and builds that capability within Control-M as a complete orchestrator, similar to Ansible or Terraform, my dependency on external orchestrators would reduce and I would use Control-M more extensively as a complete end-to-end tool. Building a full-fledged orchestrator inside Control-M would be ideal and would be a wonderful value addition.

Aside from an orchestrator, there might be value in performing some functions without headless architecture. An orchestrator capability should provide that ability. Control-M is bringing AI and LLM integrations, which is positive. The orchestrator capability is the main area for improvement at this time.

I have been using Control-M for more than five years. Throughout my five years of association with Control-M, I have found it to be successful when dealing with complex workload automation tasks.

Control-M is very stable. I rate its stability as a nine out of ten.

Control-M handles very large numbers of jobs, going beyond 10,000 jobs. My organization has many BMC Control-M specialists, and I have implemented Control-M organization-wide. My clients are large organizations, very large. My organization has 150,000 people. Control-M is very much scalable. I rate its scalability as a nine out of ten.

I rate the technical support at nine out of ten.

The return on investment I see is at least something like 30% easily.

Regarding pricing, Control-M is expensive compared to the competition, but it is worth it. Even though Control-M is expensive, pricing could be brought down slightly. One more improvement I was looking at from a pricing perspective is that SaaS pricing is too expensive. On-premises pricing has no limitation on the jobs and licenses, but when moving to SaaS, there is a big impact.

Control-M is fantastic because of its support, which is number one. The ability to scale up and the best implemented solution across the globe make Control-M stand out. There are competitors such as ActiveBatch and other companies, but Control-M is still the number one in workload automation.

My business role is as a partner and system integrator. I have Control-M deployed in every mode, both cloud and on-premises as well as in private cloud. For cloud services, I use prominently Azure and AWS.

My relationship with BMC is strategic and transformational. I would describe Control-M as positioned for performance in building, scheduling, and managing workflows. My final rating for the solution overall is around nine out of ten.