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.

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.

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 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.

Control-M runs all of our payments, batch, and managed file transfers.

Approximately 250 people use Control-M on a daily basis. Their main roles include schedulers who create batch jobs, testers, and administrators and operators.

Control-M enables us to better support our DataOps and DevOps initiatives because we have a release system every month, which allows us to work more effectively and we're automating that system with Control-M.

Control-M orchestrates workloads across several environments. Currently, we use it across five environments.

Control-M handles complex data pipelines and analytics processes by allowing us to integrate with different services. For instance, we use it with our Power BI for our reporting tools, and for pipelines for release functions as well.

We only use on-premise for the creation and automation of data pipelines with Control-M. We don't have the cloud yet.

The main features of Control-M that we appreciate the most are the scheduling and the automation capabilities. When it works, it really works very well for us. Another feature that we appreciate is the file watching service because we interact with many different sections who drop files in various locations. It's great to have that feature to kick off batch when a file becomes available.

Control-M's performance in building, scheduling, managing, and monitoring workflows is amazing. I love Control-M and enjoy working on it. I appreciate how it has automated our workflow because before we had Control-M, it was a manual workflow that was open to user error and took much longer to complete because of the manual checks we had to do before we moved on to our next piece of batch. We've been able to automate it and this has enabled us to triple our workflow.

Having more options with regards to scheduling in Control-M would improve it. There are certain scheduling features that aren't available currently in Control-M, which would be nice to be added.

I've been using Control-M for six years.

I assess the stability and reliability of Control-M as very reliable because we very rarely have any downtime. The only time we would ever have any downtime is during our reboots, which we need to do failovers for, and those are very brief. When we are actually doing our batch, I don't remember a time we've had any downtime on our production side.

Control-M scales with the growing needs of my company brilliantly. It's been really good for us. We've added so much to it over the last two or three years. We've added more users, more jobs, and all different types of jobs including OS jobs, MFTs, and file watch, and it's been absolutely no problem for us to add. We've probably doubled our amount of jobs in the last two years, so we've had absolutely no problem with it. It's scaling perfectly.

My experience with customer service and technical support has been positive. Anytime we've had a problem, which is very rarely, or we've had a question, it's always been answered very swiftly or I've looked through the community myself. The community is actually really good, and I really appreciate that side of it because I even contribute on that side. I've never had a problem.

Positive

We were using an in-house product for running our batch before integrating with Control-M. Control-M enabled us to automate that batch, but it also enabled us to bring in many more things that weren't currently being done by us.

The biggest return on investment for me when using Control-M is the reduced errors, quicker runtimes for our batch, and making the job easier.

I have not considered other solutions while using Control-M.

Control-M's performance has improved our timing and the speed of the releases. It's halved our release speed basically in the last two years.

The measurable benefits and improvements my company has achieved with Control-M include reduced errors, which is the first big one. Before we used the previous in-house software, we would have a lot of manual errors that would have to be changed manually if there was a change in a variable or anything else. Now it's all automated, and the system runs for itself. We've tripled our workflow and we are doing more in less time than we used to previously.

Control-M has enabled new capabilities and business processes that weren't possible before. Our integration with external partners has become easier due to the MFT capability that we weren't doing before. Now we're able to connect through them. These were manual processes done through moving files manually from one server to another, through different partners, but now we're able to integrate it all as one.

Regarding licensing, we handle licensing through endpoints. We've had to increase our endpoints because we're increasing the amount of workflow we have and the amount of jobs that we have. We've had to increase the number of endpoints we've needed. It's very easy to handle the growth and it's easily done.

I haven't used the Control-M Python client, but I've used a small amount of API.

I have not been using Control-M since I've been in my company because I've been in my company since 2009, and we started using it in 2019.

The impact of the migration to Control-M on our business-critical operations has led to fewer errors and fewer failures. Our biggest success story has been the lack of errors. Even when we do have errors, having the outputs available to us allows us to clearly see what the problem is.

I would rate this review ten out of ten.

My use cases for Control-M involve it being a job scheduler that can accomplish many things for our organization. It includes a large number of dependencies and many different types of jobs. We run jobs on Windows and Linux, and we have ARO pods, so it is a significant part of our environment.

What I appreciate most about Control-M is that once you become familiar with it, it is very easy to use. When jobs run, the application displays pretty colors so you can quickly determine whether a job is successful, running, or unsuccessful. It is very easy to follow what is happening on the screen. The ease of use is what I value most about the product.

What I dislike about Control-M is that sometimes things can become very complicated with certain job setups. I am not certain if that is really Control-M's fault, but setting up complicated dependencies with difficult scheduling and the Control-M New Day time can become very complicated, especially with the New Day feature.

Regarding scalability, I do have a concern with it. When we change OS versions and want to do an upgrade, that is the one time I have an issue with the product. Control-M does not allow us to build a new server to scale up our environment to a different OS or anything of that nature. We have to build the new server, migrate our configuration over with the same application version, and then upgrade. This is a major issue for me, and there should be an option to build a new server and upgrade by simply putting the new version on it.

I have been working with Control-M since approximately 2002, which makes it 24 years.

From a stability perspective, I have been using Control-M for 24 years, and we have only experienced one major issue with it during that entire time. I would give it a 10 out of 10 for stability. The server never crashes because of Control-M, and we never have any CPU or memory issues since the product is very stable.

Regarding scalability, I do have a concern with it. I have never needed to scale it out much due to not having a large environment. However, when we change OS versions and want to do an upgrade, that is the one time I have an issue with the product. Control-M does not allow us to build a new server to scale up our environment to a different OS or anything of that nature. We have to build the new server, migrate our configuration over with the same application version, and then upgrade. This is a major issue for me, and there should be an option to build a new server and upgrade by simply putting the new version on it.

Anytime I have needed help from BMC, they are very responsive. I have been working with Control-M for almost 25 years, so I do not follow up with them frequently, and I do not use their services often. However, anytime I have had questions, they are right on the ball. Their support for the product is very good.

I do not have to wait very long for them to answer me, and they usually provide a resolution very quickly. I handle several products, and in comparison to all of them, the quality and speed of Control-M's support seem to be the best. They always reply back as soon as I open a case, usually the same day within hours, regardless of the case's priority. They work on it quickly, getting me an answer within two or three days at most. There is very rarely a time I go longer than a couple of days without a response. Their support is among the best available.

I find that the actual installation of Control-M is fairly straightforward. There are two components, so I can basically install each separately. It is not overly complicated to install. The setup can become complicated depending on how much I have to configure. Basically, my company tries to leave it out of the box for most of the configuration, so we do not do much customization after it has been installed, as Control-M does a very good job of setting proper default standards for us.

Control-M is on the expensive side because I know we have looked into other pricing for other products in the past, and it is on the expensive side of things.

The only alternate solutions I really use for comparison are Windows Task Scheduler or Cron. Those are just the basic default schedulers that come with the operating system.

We definitely use DevOps and DataOps with Control-M. The DataOps we have integrated basically uses scripting methods to accomplish this. We do not use many of the available plugins. On the DevOps side, we have ARO pods, so we set up a pod environment, which was somewhat tricky at first because we were one of the first to start using this type of setup. Now it is much better. I have checked the documentation they have now, and it is significantly better than it used to be. In the beginning, it was quite complicated, as they pointed you to GitHub and said to look there for all the details. Now they have much better documentation, making it much easier for us.

I believe my relationship with BMC is transformative. They always keep us up-to-date on things. They have seminars and user groups, which is another significant benefit. I attend a Toronto user group basically once a year, and we get all kinds of valuable information from that. I believe it is a good relationship in that regard.

Overall, I give Control-M a rating of nine out of ten for everything, including the price.

My main use case for Control-M is working as an administrator and scheduler, setting up AFT jobs with respect to their AFT and OS jobs. Apart from that, I also work on connection profiles and their respective connection setup between two servers, troubleshooting Control-M agents and their respective configurations.

For example, if I want to schedule a specific job for a specific day, Control-M is very useful, allowing me to schedule or set up a job with respect to either a monthly, weekly, or daily basis while considering specific holidays configured on that particular calendar. In addition, if I want to run some script, I can use OS jobs to run a specific script on that server. I can also transfer files between two servers using Managed File Transfer or AFT, transferring files by setting up their configuration on both ends. These are the major tasks I typically work with.

I use these scheduling and file transfer features daily as part of my workflow. I mostly set up jobs in production and non-prod environments, organizing the respective job setups in different categories. For AFT jobs, I typically transfer files from an S3 bucket to a Windows Server or a Unix server to a Windows Server or Unix to S3 bucket, using various platforms such as server to server, server to shared drive, or shared drive to server. In AFT file transfer, I set up connection profiles between two servers for host one and host two, ensuring both hosts communicate properly using Control-M Configuration Manager, also known as CCM. Once the connection is set up, I configure the job using the same Control-M connection profile. I provide details such as source and destination path using that configuration for proper communication.

Managing a Control-M agent is one key part; in case Control-M agent fails, I troubleshoot it using basic troubleshooting skills, such as selecting the troubleshooting option on the agent to reflect the initial level of error. Additionally, I check the library and error log for a specific agent. I also review the new day process (NDP) to ensure that all jobs load daily to avoid production failures. Calendar setup can also be beneficial for any job scheduling. I work on creating new Control-M jobs that operate under the planning and are created concerning the workspace. Furthermore, I track jobs that flashback from the regular cycle. If there are issues with different Control-M servers across my environments in the UK, US, and Asia Pacific, I troubleshoot any server outages or failures.

Regarding measurable improvements since using Control-M, when jobs fail due to specific errors, I am notified promptly, which is essential for handling issues in the production environment. Instead of manually checking all failed jobs, I configure notifications based on priority, enhancing my time-saving efforts across job management.

The biggest lesson learned from using Control-M is understanding how jobs get scheduled within minutes and can function independently without intervention. I have gained insights into configuring job properties such as command jobs, file transfers, file watchers, SAP jobs, and more. My extensive experience of over nine years with Control-M offers me great opportunities, and I look forward to improvements, particularly in AI features relevant to Control-M.

In my experience, the best feature Control-M offers is the File Transfer capability, especially useful for larger sizes. I am currently working on a project transferring files larger than 6 GB, which is easy to accomplish. Additionally, the File Watcher job type is valuable as it checks for files in a specific path and triggers actions based on file availability.

For the File Watcher feature, it helps me keep an eye on specific files, triggering transfers as soon as they become available. When setting up a File Watcher job, I ensure specific configurations, such as file availability time and run cycles. The job detects file availability on the platform; once the file is available, the File Watcher job triggers, triggering its successor job. This eliminates the need for constant monitoring of file availability, allowing me to focus on other tasks. Additionally, AFT file transfer for larger volumes is smooth and efficient; other tools take a lot longer for significant file transfers. Control-M lets me track the percentage completed during file transfers as well, enhancing my monitoring. CCM allows me to track all Control-M components effectively, including agents, servers, and add-ons.

Control-M is exceptionally useful for building, scheduling, managing, and monitoring production workflows. It allows me to set up jobs promptly and test them collaboratively with input from my operations and development teams without delays. Control-M enables my IT personnel to refocus on critical operations once jobs are set up for recurrent activities such as monthly or daily runs. For example, one of my teammates established a payroll job that now runs automatically, reducing manual effort and allowing for more focus on different project requirements.

For improvements, there is some slowness in terms of logging, as Control-M can be a heavy tool that could benefit from reducing file size. Overall, the tool is great, but it would be helpful if it were lighter and easier to use in my daily operations. In my non-prod environment, I notice significant lag, making it difficult to work effectively. Enhancing the tool's speed and perhaps integrating AI-related features could significantly benefit daily activities.

I choose nine because once the tool is available in a lighter version, it would be even more useful. The features are excellent, but the heaviness of the tool makes it take longer to open and close, causing frequent hangs during multiple activities. For instance, switching from monitoring to history can cause significant delays. This impacts productivity, especially in a production setting where speed is crucial.

I have been using Control-M for around eight to nine years.

Control-M is indeed stable.

Regarding scalability, Control-M scales well, allowing me to operate in four different environments across four regions without concerns.

Customer support from BMC is excellent; whenever I create a case, they respond promptly, ensuring there are no issues. I would rate customer support a perfect ten.

Previously, my organization used tools such as OPC and Tivoli before switching to Control-M, as my organization recommended it. The GUI in Control-M is significantly more user-friendly for managing daily activities and job setups.

The deployment of Control-M servers takes about one to two hours on the AWS platform.

For deployment and maintenance, I find that two to three people suffice for maintaining a single server. However, if I need to manage multiple servers across different environments, more staff will be necessary. The requirement really depends on the configuration size and deployment needs.

Although Control-M is costly, it does yield a return on investment for larger organizations, but it is not suitable for small teams or individuals. The pricing is quite high, which can be a disadvantage for smaller groups, although the efficiency gains do save time and money overall.

Regarding pricing, setup costs, and licensing, I am not heavily involved in those areas, as that falls under my network team. However, the pricing for Control-M seems high compared to other tools, and licensing can be complex, with annual renewals required. The cost has been substantial compared to similar tools I have encountered.

Before selecting Control-M, I evaluated other scheduling tools such as Tivoli and Mainframe job track but decided to stick with Control-M for my long-term needs.

For those considering using Control-M, I would advise it is an excellent choice for managing workflows and orchestrating jobs as per project demands. I have been using Control-M in various roles over the past 10 plus years, and I recommend it, provided you understand processes and setups. I give this review a rating of 9.

In my previous project, we were using Control-M, and we automated the data pipelines using SQL Server Agent jobs and created the Databricks workflow. We had some data available in SQL Server and some in Databricks, and because we had two systems, the orchestration process was completely different, and we were not able to manage or create a dependency because both tools were different. That is why we implemented Control-M in the past project and automated all the SQL Server jobs and the Databricks workflow using Control-M. By using a single platform, Control-M allowed us to create a dependency between the SQL Server and Databricks data. On the reporting side, we were using the Tableau dashboard as well, and for Tableau, we were using the extract to display the data. We were refreshing the Tableau extract using Control-M. In my last project, overall all the data pipelines including the Tableau extract refresh were done using Control-M.

We expanded a lot because previously we were using multiple tools for the same orchestration purpose, such as Databricks workflow and SQL Server Agent. Now, we are using the same product or a single tool for multiple tasks, which is very helpful for developers as well as business stakeholders.

I appreciate Control-M because of the dependency it offers. As I mentioned, we had some data available in SQL Server and some in Databricks, and it was hard to create a dependency when we were working on different tools. That is why we chose Control-M so that we could create a dependency, and we had some highly critical banking data in that project. The SLA was very minimum, and we had to get the dashboard refreshed every morning at 7:00 a.m. Due to the SLA features in Control-M, we chose it in the last project.

I find that Control-M provides a single UI platform where I can monitor all the jobs. Previously we had different jobs, so we had to monitor each job individually. With Control-M's single platform UI, we can monitor all jobs. The main benefit is that Control-M has a retry functionality, so if any job fails during execution or due to bad data quality, we can retry the job. Once we receive the data, the job can execute automatically. The alert mechanism also triggers emails to business stakeholders whenever any job fails. These are the main features I prefer about Control-M.

Previously, we set up alerts so that whenever there was a delay in the file, it automatically sent alerts to business stakeholders indicating the file's unavailability. Whenever there was a delay, it triggered an email to notify that we were expecting the file at a certain time. Additionally, we set up a file-based trigger. Since the time of file arrival is not consistent, we configured the job to execute automatically when the file arrives, ingesting the data into our final database. This file-based trigger was a key feature we explored.

I think the pricing is a factor, and it is high. I am currently working in a multinational company that has purchased the premium enterprise-level license for all developers, so it is not a big deal for our project. However, someone in a small company or startup might face pricing constraints while implementing Control-M, as the pricing seems a bit high compared to other tools such as Airflow.

One area for improvement in Control-M could be pricing, and another is the learning curve. I feel that when someone starts working with Control-M, they need at least one month to onboard and understand all the features. Although documentation is available, understanding all features takes time. Another recommendation would be for UI improvements, as I felt the UI seemed outdated.

I feel that it is a little bit difficult to integrate Control-M with technologies for DataOps and DevOps processes, especially initially, as I needed about one and a half months to understand the complete features and flexibility of this tool. From a developer standpoint, it is not very user-friendly. However, once I become skilled in this tool, it provides great flexibility.

I have been working with Control-M for six plus years.

I faced a performance issue once because we created a very large data pipeline with multiple dependencies in Control-M. So, we narrowed down one workflow into multiple sub-workflows, which improved performance. Processing a large amount of data can be complex and time-consuming.

I can raise a support ticket for BMC software whenever I have any technical issues, and they respond within a three-day SLA, providing full support.

I would rate the tech support of Control-M as 8.5.

I evaluated Airflow before choosing Control-M. In Airflow, we faced a similar situation because we had to create different cron jobs for each Python script. We had 100 plus Python scripts fetching data from multiple source systems, and in Airflow, creating dependency between each cron job was very hard. That is why we switched from Airflow to Control-M.

The initial setup process was done by our infrastructure team. I worked as a developer to create jobs, but the actual setup was quite good and well-supported by BMC software.

Our initial setup was completed with full support from the infrastructure team. After that, the workflow creation and job creation in Control-M were entirely managed by our developers.

The initial setup process was done by our infrastructure team. I worked as a developer to create jobs, but the actual setup was quite good and well-supported by BMC software.

Our initial setup was completed with full support from the infrastructure team. After that, the workflow creation and job creation in Control-M were entirely managed by our developers.

I think the benefit is very high. If a company does not have any budget constraints, they should definitely explore Control-M because it allows for end-to-end orchestration of the project without needing separate projects for the data pipeline and downstream applications such as reporting. All tasks can be accomplished using one product, providing significant value if budget constraints are not an issue.

I find it cost-effective, but I am not fully certain about the overall ROI.

The biggest lesson I learned from using Control-M is that it provides a single UI to monitor all jobs, making it much easier compared to my current project where I use Airflow, which involves managing multiple cron jobs across different tabs.

We do not have any direct contact with BMC software, so I would not describe the relationship as transformative.

I rate Control-M as nine because it simplifies complex data structures and pipelines.

My use case for Control-M is scheduling batch jobs in the banking sector. Previously, I used it for support jobs, and subsequently utilized it in development activities. When making changes to jobs, we need to locate them in Control-M. We monitor the screen and search for jobs with different filters such as the order date filter. In our project, we use the order date filter because jobs run on every order date. Based on the order date, we navigate to the particular date and verify if our job is running. If it is running, we check the status and review the logs.

Previously I used AutoSys, and when I transitioned to Control-M, it was initially exhausting because Control-M allows longer job names. Based on the job name, we can understand what the particular job is doing. Here everything comes into one number as we are using short job names, which was initially irritating. Once I became accustomed to it, it became simple to organize things efficiently. We can see all the links, predecessor links, and dependencies visually.

When we select a job in Control-M, we can see all the linked connections, including predecessor jobs. Sometimes when a job has multiple dependencies located far from the current location, we need to scroll down to see the linked jobs. We have a feature to right-click and go to the dependent waiting info, but whenever we click on a job, it should visualize the link and display the job name on the particular line it relates to. This would be a beneficial feature to implement.

From my knowledge and job role, business users do not typically use Control-M since they are interested in the end product rather than the scheduling tool. However, they are concerned with ETAs for their reports. When batches get delayed, business users need to determine when their particular report will be ready. Currently, they must ask the technical team for timing updates. If Control-M could develop a portal showing ETAs for business reports, it would eliminate the need to consult the technical team.

In my six years of experience, I have primarily worked with existing jobs. For creating dependencies, the current process requires multiple steps, including going to the predecessor and action items and setting up in two places. Implementation of drag-and-drop functionality would simplify this process. Users could create dependencies by clicking on the source and dragging it to the destination, with additional setups for actions and notifications available through a separate menu.

I have been using Control-M for approximately five to six years in my career.

Regarding stability, in my previous project, I experienced lagging with refresh times of six to seven seconds. In my current project, I feel more comfortable as the refresh time has improved to one or two seconds.

Control-M is particularly suitable for large companies. Many big companies are migrating from AutoSys to Control-M. The graphical visibility of processes is clearer than other job scheduling solutions, which is Control-M's biggest selling point.

I have never contacted Control-M technical support or customer support as the client team typically handles any issues.

Neutral

I have used AutoSys, where I developed numerous jobs. AutoSys uses dependency configurations that I found easier to set up compared to Control-M.

I cannot speak to the ease or difficulty of Control-M's initial deployment as it was already implemented when I joined the project.

I have limited visibility regarding Control-M's pricing since we use it as clients and work as contractors.

When joining projects, Control-M is typically already implemented, so I have not had the opportunity to migrate from previous solutions.

Regarding Control-M's management and orchestration of workflows across enterprise, we currently use on-premises solutions. In my previous project, we used cloud solutions, but I found the on-premises solution more effective for technical people, while cloud solutions are more suitable for business users. When scheduling is cloud-based, there can be lag time as it takes longer to reflect changes from on-premises to cloud environments. With the current trend moving toward Databricks, cloud implementation becomes a viable option for jobs already running in the cloud. However, for jobs running on an on-premises server, cloud implementation may not be the optimal choice.

I rate Control-M a seven out of ten.

Control-M is similar to Stonebranch and Redwood. It is a workload automation system that automates steps typically defined by humans. Previously, if humans performed 100 steps to complete a task, they can now input these steps into Control-M and let the system run them automatically. 

The most common use case is in banking. In banking operations, there are daily transactions between customers that need to be processed, closed, summarized, compiled, and sent to the core banking system for execution. Manually, this process could take more than eight hours per day. With Control-M automation, this time can be reduced to one hour or even 30 minutes. Before the cutoff time at 12:00 a.m., Control-M performs the batch job or end-of-day process, executes it, passes it to the core system, and marks it as a finished job for the day.

Control-M makes it simple to create, integrate, and automate data pipelines. Essentially, you have a single dashboard where you can manage everything. You can create the job, perform quality checks before promoting it to development, and then execute it in production. You can also monitor the jobs to see if they fail or trigger any alerts that require attention. Overall, the process is very straightforward and simple.

It is pretty easy to integrate with technologies for data operations and DevOps processes as things change. Control-M is API ready, so as long as the other side also has an API, it’s a done deal.

Stability is crucial in the banking or financial sector, where operational downtime must be minimized. Control-M provides exceptional stability compared to competitors such as Redwood, Stonebranch, or Perpetuity. The second valuable feature is the user-friendliness of the tools, making it easy to learn and use.

Control-M can execute batch jobs and monitor the jobs it executes, though it does not monitor servers or other systems.

Since the system is stable, clients don't typically request rapid improvements. However, one area that could be improved is the AI capability and AI generative features, as these are becoming increasingly important in modern systems.

I have been working with it since late 2010, approximately 15 years.

After migration to Control-M, there is an initial stabilization period. Once properly implemented, the system becomes very stable, which is one of its strongest attributes.

It is a scalable solution. Our clients are enterprises. About 85% of banks in my country use Control-M.

I would rate their support an eight out of ten.

There was a previous solution from either Redwood or Stonebranch, and another tool called Perpetuity. Customers were successfully persuaded to migrate from these solutions to Control-M. The success rate of migrations is usually more than 97%, and it may even be almost 100%.

Implementing it is not straightforward. It requires careful installation, customization, and configuration. Unlike simply installing Microsoft Windows or Office, this solution demands significant effort and time. It is not as simple as it may seem.

Technical migration can be completed in a few months, but full implementation including user adoption and socialization typically takes about a year. This is because the tool has various users beyond IT, including business users, trade finance users, and branch users who need time to learn and become comfortable with the system.

We have previously attempted to work with the services team from BMC and found their design to be good. However, we prefer not to proceed with the actual on-site implementation. Their prices are quite high, and their approach is too rigid for our needs. Therefore, we believe it is better for us to handle the implementation ourselves.

Automation can benefit customers in many ways, particularly when it comes to saving money. For example, if we rely on human workers, we may face long Recovery Point Objective (RPO) times, which could extend up to 88 hours a day. Additionally, humans are prone to making mistakes. Let's consider a scenario where an operator is manually summarizing transactions from a single branch. If that branch has 10,000 transactions in one day, it can be quite challenging for the operator to keep track. This may lead to issues like double data entry, where the operator mistakenly inputs the same data twice. Such errors can be disastrous for a bank, potentially resulting in financial losses. By implementing automation tools, you can minimize human errors and improve efficiency.

The Total Cost of Ownership (TCO) for Control-M is more economical compared to other solutions. While competitors might offer lower initial prices, they often include hidden costs that emerge after the first year. Control-M maintains a straightforward pricing model based on license count, implementation, and training, without unexpected future costs.

Control-M uses a simple pricing model based on the number of jobs per license, where one job equals one license. After assessing customer needs, proposals typically include the required number of job licenses plus implementation fees. The implementation fee covers installation, customization, configuration, job building, testing, and execution until the solution is running perfectly.

When you purchase a license, it's typically through a subscription model. Some people refer to this as a subscription type or ops type. If a customer opts for a subscription, it's similar to renting a car; you must renew it each year. It's important to note that there may be an annual price increase determined by BMC, and the specifics will be decided by the principal.

BMC is an important strategic partner for our company. Around 70% to 80% of our business comes from working with BMC, or more specifically, from selling BMC products.

The biggest lesson learned is the importance of providing perspective to customers rather than simply following their requests. It's crucial to understand and discuss their requirements thoroughly, such as questioning why they need a specific number of jobs.

I would rate Control-M a nine out of ten.