Control-M Reviews

My main use case for Control-M is that I am interested in learning about middleware programming and how it simplifies tasks when using Control-M.

A specific example of a task or workflow I am hoping Control-M will simplify is workflow management, where manual processes can be reduced.

I want to explore more from this session regarding my main use case.

I manage the integration and sequencing of data processing across diverse data platforms and cloud environments with a straightforward approach.

My organization is planning to use Control-M to support AI or ML workloads based on project requirements.

My organization uses Control-M to address compliance requirements through audit and respective department heads.

Control-M has positively impacted my organization so far, as I have noticed several benefits.

The benefits I can identify include a combination of time saving and automated tasks.

The best features Control-M offers, based on my experience so far, are the simplification and how it makes performing day-to-day tasks easier in a quicker manner.

The simplification helps me in my day-to-day tasks because it combines automation and a generic UI, which is very easy and understandable for end users.

The recent AI addition surprised me as it provides more prompts for simplification.

Regarding Control-M's AI capabilities, I think its governance and security are top-notch.

The accuracy and reliability of Control-M's AI output are quite good so far.

Control-M has facilitated the integration of Kubernetes and cloud-native workflows with traditional batch processing by simplifying day-to-day tasks through its automated features.

I believe Control-M can be improved by including more AI features.

Improvements should focus on addressing more repetitive tasks.

I have been using Control-M for more than six or seven months.

Control-M is quite stable as it is in the exploration phase.

From what I have seen so far, Control-M's scalability is quite good.

I have seen a return on investment, specifically in terms of time saved.

My experience with pricing, setup cost, and licensing is moderate.

I have experience with AI and ML features in multi-cloud environments where I have been using AI and ML.

I chose a rating of eight because there is room for improvement in daily tasks, and based on current usage and the exploration phase, I would keep it at eight.

My advice to others looking into using Control-M is that the decision should be based on project requirements, as it simplifies tasks and saves time.

On-premises

I use Control-M day-to-day to obtain proper answers to typical questions as I would with any document.

In my daily work with Control-M, a specific example is that in the Java process restart, we are executing a workflow, and every midnight, they perform the restart. During that time, we have to create alerts that come and we need to disable them. The main use case with Control-M is that at midnight, the application team restarts all our Java services.

In managing the integration and sequencing of data processing across diverse platforms and cloud environments, for the cloud environment, we first need to whitelist that URL and verify that PII data should not be exposed in public.

We are currently planning to use Control-M to support AI and ML workloads.

For the Kubernetes cluster, we manage the integration of Control-M by deploying the agent, executing the workload and agent installation, opening ports, and then deploying.

My organization uses Control-M to address compliance requirements through regular testing and reporting.

The best features Control-M offers include workflow, which stands out to me the most.

I find the workflow feature of Control-M to be very useful because it automates manual tasks. Control-M positively impacts my organization by saving time; it is very time-saving.

As a system admin and monitoring lead, I can tell you about the time savings: every time I have to create that high availability alert for each application, which can have more than 200 alerts, and manually it takes a huge amount of time. Through workflow, we have a schedule, and it runs within a minute.

Regarding improvements for Control-M, there is nothing specific, but the UI should be more user-friendly. There is not really much to say about the needed improvements.

I have been using Control-M for one year.

Control-M is stable.

The scalability of Control-M is fast and meets expectations.

Customer support for Control-M is excellent. I would rate the customer support a 10.

I did not previously use a different solution.

I have seen a return on investment as we save time and money.

My experience with pricing and the licensing part of Control-M has been straightforward.

Before choosing Control-M, I evaluated Control-M itself and found it to be the right choice.

On a scale of 1 to 10, I would rate Control-M a 9.5. I chose 9.5 as my rating because I prefer it the most; that is my personal opinion.

Regarding Control-M's AI capabilities, I believe that PII data should not be exposed. The output is really faster and very efficient.

The advice I would give to others looking into using Control-M is that it is time-saving.

On-premises

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.

On-premises

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.

On-premises

My main use case for Control-M is for orchestration purposes.

A recent task I managed with Control-M for orchestration was to get the failing details from the server; Control-M was helpful to get all the details at one time. Whenever a failing scenario occurs, I can see it on the web page, and this is very helpful without any difficult tasks.

I do not have anything else to add about my main use case or how I use Control-M.

Control-M has positively impacted my organization by reducing the time I used to spend logging into one server to get the information; this was taking a lot of time. From a business perspective, it was very tedious to explain things to business people, but now, this is all articulated in one file and shown in the browser itself. This has most benefited us with time, which is very valuable. These are very good features.

Out of an eight-hour job, I am saving nearly 3 to 3.5 hours using Control-M, which is almost 35% to 40% of my time.

In my opinion, the best features Control-M offers include a flow that is very useful; there is a web page displaying all the jobs running, the jobs that are critical, and the jobs that need to be reviewed. For any job that is failing, I can get to know. If there are any jobs that are still running or have pending tasks, this is helpful from a business perspective. Business people can just see the web browser and get to know what's happening. They don't need to go deep into the technologies and everything; they can get to know easily.

I would not like to add anything else about the features.

Regarding Control-M's AI capabilities, its governance and security are among the most secure features I can use; it is more trustable and bankable when I use its AI capabilities. For example, if I have very useful data like scientific data that needs to be very secure, I can go with Control-M, which is very good and will be more helpful for security as there is no data deviation.

In my experience, Control-M's AI capabilities have an accuracy and reliability rating of almost nine out of ten, with an accuracy of almost 99 to 99.5%; it is working great from my perspective.

Control-M could be improved by adding some more features, such as time management and business perspective features; many other features, such as server management, could also be added.

I do not wish to add more about the needed improvements.

I have been using Control-M for only two or three months.

I rate Control-M seven out of ten.

I chose a 7 out of 10 because it is just the latest technology, and I have only been working with it for two months. I need to explore more to know it better; I think it would be a nine out of ten with more daily use.

I advise others looking into using Control-M to go for it because this will help save time. It will help from a business perspective and be very helpful day-to-day. This will not only save your time, but it will also help you know which jobs are failing. There is no need for me to go and investigate every job that is failing; all these results will be in a business format, and I can see them in Python, which will be most helpful.

Public Cloud

Amazon Web Services (AWS)

My main use case for Control-M when I worked with it was as a workflow architecture and workload automation platform where I used it to design, schedule, and monitor jobs. I managed complex IT processes, bringing business processes across multiple cloud environments—not only cloud solutions but also on-premises, multi-cloud solutions such as public cloud, private cloud, or hybrid environments. I used it for batch job scheduling and automation when I was familiar with it.

To manage the integration and sequencing of data processing across diverse data platforms and cloud environments, using Control-M depends on whether you can operate out of the box or with plugins and APIs. Most of the time, these API connections or custom integrations are utilized, which involves plugins or native cloud tools, and this also applies to application integrations or CI/CD automation.

My organization is planning to use Control-M to support AI or ML workloads. Control-M's AI integration, including generative artificial intelligence, is beneficial for building and running managing data pipelines and application workloads using workflow automations. This AI workflow creation acts as a natural language tool that translates simple text into comprehensive job workflows. We also have execution insights, RCA integration, and various troubleshooting aids with the RCA agent.

Control-M handles compliance requirements such as SOX, audit trails, or regulatory reporting effectively. Audit automation features in Control-M include an audit mechanism configurable with this configuration manager. We can set the system to prompt users for mandatory text justification when they perform sensitive actions or modify automation workflows, achieving SOX requirements by centralizing jobs and automating batch scheduling with workflows that satisfy IT general controls around change management, job scheduling integrity, and segregation of duties in SOX.

Control-M has positively impacted my organization. I worked on this, and the use cases for Control-M are very high. The automations, such as job run scheduling, clearly dominate, and no other solution can beat it. That's how Control-M finds its place.

Specific measurable outcomes in Control-M relate to project management. The control phase gives distinct measurable parameters in project management and workload automations. By implementing Control-M, a workload automation software, I am able to track and validate job behavior effectively. No other solution can beat it in making batch window performance measurable.

The best features that Control-M offers include agents for job scheduling, cloud and data pipelines through integrations, self-service management, and security. We also have many other features, such as multi-cloud support and active-active capabilities where normally we have active-passive configurations.

The active-active feature impacts my work positively; it allows both servers to manage workloads when going to cloud or on-prem. In contrast to active-passive, where one server manages the total load while the other stands by, the active-active setup is useful. I attended an event that highlighted active-active as a valuable feature.

Regarding Control-M's AI capabilities, I believe its accuracy and reliability of output are exceptional. The accuracy is assured due to its integration and other factors; the time processing is also good. The AI agents, such as fabric agents, demonstrate solid performance.

I found working with those different environments presented challenges; I faced many issues while modifying the jobs in Control-M. Frequently, several phases presented operational issues such as synchronization issues, and sometimes there were significant time challenges, which I think have decreased.

Regarding enhancements, when I attended an event, I noticed many areas for Control-M improvement, including performance tuning and job design best practices, such as resource management utilizing check config utility. While there are existing options such as true enterprise manager parameters and optimization tools, I believe we can manage these aspects better.

I am not much familiar with Control-M, but I have worked with Control-M a long time ago, and I do have some knowledge on Control-M.

Control-M is stable.

Control-M performs well when it comes to scalability.

Regarding customer support, I can say it is very good. Although I do not remember exact details since it has been a while, insights from the events I attended regarding Control-M were excellent.

We did not previously use another solution; we have used Control-M itself.

I have seen a return on investment as evidenced by money saved and time saved.

Concerning pricing, I believe the pricing is reasonable; when compared with others, Control-M's audit trails feature is included in the standard pricing plan. As I remember, it costs about $2,400 per month, and this is useful.

Before choosing Control-M, we did not evaluate other options. We compared Control-M with other platforms and concluded it was the best choice at that time when I worked there earlier.

On a scale of one to ten, I would rate Control-M around eight.

I chose that number because when I compare the integration of Control-M, which includes connections with AWS, Azure, and SAP, the integration looks good, with no delays or issues. The processes for job running and scheduling are perfect.

My advice to others looking into using Control-M is that the key feature is its fabric agents. I recommend investing in training and doing due diligence on business prefixes, job names, and organizing folders by domain. There are many aspects such as runtimes, retry strategies, and leveraging API automation to consider. Based on this, I suggest these as key advice for others.

Private Cloud

My main use case for Control-M involves building workflows, creating automation use cases, and implementing automation on application site flows without much manual intervention.

A specific example of how I use Control-M for automation is connecting application A to application B using a workflow and determining how reading the logs ensures that the connections are happening in time.

My organization is not currently using Control-M to support AI or ML workloads, but we require addressing workloads from the external world, specifically cloud-based tasks.

My organization uses features of Control-M for addressing compliance requirements such as SOX, audit trails, and regulatory reporting, but another team handles that aspect.

Control-M has positively impacted my organization because we have consistently relied on it for integration and monitoring. I have been using this product for over 20 years for my customers, so it is truly helpful.

Specific outcomes show that efficiency has improved significantly over the years, and the improved manageability of customers' applications is partly because of Control-M. The efficiency we have achieved using Control-M is notable.

I see more of a traditional use case usage that we can do with Control-M.

Regarding the traditional use cases I mentioned, I rely on all three fields: scheduling, integrating, and monitoring. Scheduling is very traditional for us, integrating occurs whenever we get a new product, and for monitoring, we mostly use third-party free products and services or open versions for the observability part.

Regarding Control-M's AI capabilities, I feel it has really good governance and security-wise; we use internal resources and not internet-facing ones, so I am not very clear about that since most of my use cases are internal.

Regarding Control-M's AI capabilities, I find it is 95 plus percent accurate because we require proper testing before deploying to production; otherwise, we find ourselves in a difficult situation.

Control-M can be improved by integrating or leveraging more from the Argentic AI use cases or the upcoming AI landscape.

I have been using Control-M for about two decades.

Control-M is stable.

Control-M's scalability is evident; we have used it for a few applications and have extended its use, finding it completely scalable based on our understanding.

The customer support for Control-M is really good.

Previously, we used a very old traditional solution without a product, and that was the reason we moved to Control-M.

I have seen a return on investment; it is more efficient, and we have been able to reduce some operational resources by implementing Control-M.

My experience with pricing, setup cost, and licensing for Control-M has been positive; we are using the traditional model and are happy with that aspect.

On a scale of 1 to 10, I rate Control-M a nine.

I give it a nine because it has helped me and my organization, and we are able to depend on the product.

Control-M is deployed in my organization entirely on-premises, within the network, not exposing it to the internet.

My advice to others looking into using Control-M is that it is really good for traditional on-premises architecture, and I do not see anyone who can compete with it.

On-premises

I work in the SAP BW project technology, and we use Control-M to schedule the process chains, which are jobs, to run at a scheduled time or to run after one process is completed.

We have a process chain which runs five times per day and on weekends, it runs seven times, and Control-M helped to schedule such jobs.

There is one more process chain which needs to be scheduled after another event is completed, and Control-M helps make it easier for the job to run.

I check the schedule and take help of Control-M in moving the jobs from one event to another event.

We are already using it for the BW jobs.

We use Control-M for scheduling the jobs of process chains to run at a specific time, helping in analyzing the issues and then taking action against the issues followed.

We include the audit trails and regulatory reporting, and it helps the user to check the live data.

By checking the logs and number of runs, we now schedule more runs to fetch the latest data from the source system to the BW, so users will have a kind of live data.

Users were able to see the data very late, and based on these frequent runs with Control-M, now the users are able to check almost the live data, which is very useful.

The features such as checking if a past schedule has run or not, when the next schedule is, and how many times it is scheduled in the future run stand out to me the most.

We faced an issue of locked process chains, and Control-M made the job easier by giving a quick overview of how many times it was scheduled.

I feel that Control-M is very secure and it does not share the data to a public source; it's the best.

I'll give it a whole 10 on 10.

I don't have anything in mind as of now; it works best for me.

I feel that Control-M is a little bit difficult when it comes to a fresher, but once you get used to it, then it becomes very easy.

I have been using Control-M for more than four years.

Control-M is pretty much stable.

The scalability is to 100 level, 100X.

It's 10 on 10.

10 on 10.

We stick to Control-M.

We do it, and we do take that use and make it involved in our process.

We only use Control-M.

Control-M is on-premises. I recommend using Control-M; it is so much reliable and it is better in terms of scalability, complexity, and ease of use.

Public Cloud

Microsoft Azure

My use case with Control-M is to handle scheduling and automation processes.

Control-M's best features are its reliability and user-friendly interface. Control-M has improved our organization by streamlining our workflow and increasing efficiency.

The areas that have room for improvement in Control-M are the user interface enhancements.

I have been using Control-M for more than 10 years in our organization.

I rate the stability of Control-M as an eight out of ten.

Regarding scalability, I rate it a nine out of ten.

I rate the technical support of Control-M as a seven out of ten.

I find it very difficult to quantify the return on investment I have seen with Control-M.

My thoughts on the pricing of Control-M lean towards it being expensive; I would rate it a ten out of ten as very expensive.

I compare Control-M favorably with other solutions and vendors in the market.

I find it easy to integrate Control-M with technologies for our DataOps and DevOps. Control-M is being used extensively across multiple departments in our organization. Approximately 100 users utilize Control-M in our organization. Control-M does require maintenance, and I would say it is complex in some aspects. My relationship with BMC is more transactional than transformative. My advice for others looking to implement Control-M would be to focus on improving their documentation and consider the challenges with add-on products like Workflow Insight, which does not have RBAC, making implementation difficult in organizations like ours.

Hybrid Cloud

Other

Control-M is used as a job scheduler. Initially, when started, it was more or less used as a batch scheduler. It has evolved over time so much that even the name has been changed to Control-M Workload Automation, which completely justifies its name. It is not just a scheduler anymore; it does many more things than just a scheduler.

Control-M, especially, is quick to the market with all the new products that are coming up, be it the integrations or the capabilities that are emerging. Any new technology that comes up in any stream such as AWS or Azure or GCP is addressed quickly. Control-M is very fast to the market when compared to other schedulers or other vendors where they develop these integrations and the rapid release of these integrations. The target is around three to five integration releases within a month, which is best in the market. The support that is received when a case is raised is really quick and very helpful when compared to others.

The best features of Control-M are highlighted by its GUI, which is a game-changer because it is so user-friendly. Any person who is logging into Control-M for the first time will know what each option or the parameter is. It is so self-explained, eye-catching, and very easy to use. Currently, Control-M is moving away from the thick client to the web client, which also maintains the same user-friendliness. Another key feature is that it keeps up to the market standards with respect to security, compliance, and everything. All the capabilities are available, and it is just a drag and drop of each to create jobs. In this DevOps world, integration with the DevOps pipelines is possible where job creation can be automated as well.

It is very easy to integrate technologies for DataOps and DevOps processes as things change, not only for DevOps processes but for any other tools in the market. There are more than 100 plus integrations that are already built within Control-M where you can just drag and drop to create and have a centralized view of all these jobs, be it ETL jobs, data lake jobs, or ADF jobs. Adding these dependencies and having a centralized view is something that Control-M thrives on. If any issues are faced during this process, the support model and documentation around it are very clear and abstract.

Control-M has helped businesses positively, especially when started as a scheduler without exploring most of the modules that were available. Over the last eight years, the first benefit was that when integrating with a DevOps process to maintain version control, a client had an in-built macro or PowerShell script which was incompatible when the version was upgraded. Standardizing it using the Workflow Change Manager, which promotes jobs between environments, was suggested. Control-M's Application Integrator helps to create custom job types rather than using in-built job types, which helped develop around 150 or 200 jobs with that approach. Control-M also offers a conversion tool that allows conversion of jobs from other tools to Control-M without requiring costly professional services.

There are areas for improvement, especially with the transition from the thick client to the web GUI. While Control-M's main game-changer is its GUI, the current web interface is less user-friendly than the thick client. A Windows client option should be maintained for flexibility, as it caters to users who prefer different interfaces.

Control-M has been used for 15 years.

Control-M as a tool is very stable. However, stability can be affected by how the environment is set up, including network stability, storage, and database factors. Control-M itself is robust, and it would receive a rating of 10.

Control-M is a scalable solution, and its scalability would receive a rating of 10.

When a case is raised, the response that is received is really quick and very helpful when compared to others.

Positive

Conversion of jobs from other scheduling tools to Control-M has been done. Jobs have been converted from Dollar Universe and TWS, IWS Maestro as they call it, to Control-M. There is working experience on Maestro and Dollar Universe as well.

Deploying Control-M is straightforward; as long as you know how to deploy it, the first attempt may be more difficult compared to subsequent ones. Documentation and support are readily available, making the overall process easy.

Compared to other vendors, Control-M is quite costly. However, all good things come with a good cost. The features, speed to the market, and quality justify the higher expense. Control-M may need to rethink how to make it more cost-effective because while many clients appreciate the tool and its features, the current economic climate and desire for cost optimization lead some clients to consider other options that may solely meet their scheduling requirements at a lower cost.

Control-M does require maintenance, especially for the VMs in both on-prem and Azure environments, which need to be patched regularly. A separate team handles automated patching, and there is a move away from on-prem to have everything on a single cloud instance.

Maintenance is easy overall; applying patches does not take much time. The technical aspect of upgrading or patching is minimal, but the process around it can take longer. Gathering concurrence from job owners for downtime and executing the patching process usually takes time, even though the actual installation is quick.

Control-M would be recommended if you are looking for a scheduling or workload automation solution and are not overly concerned with cost but want to utilize features to enhance your estate and maintain a centralized view. This review has been given an overall rating of 9.

Other

Microsoft Azure