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Top 8 Process Automation Tools

Control-MCamunda PlatformPega BPMIBM BPMBizagiActiveBatch Workload AutomationAppianBonita
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    The File Transfer component is quite valuable. The integration with products such as Informatica and SAP are very valuable to us as well. Rather than having to build our own interface into those products, we can use the ones that come out of the box. The integration with databases is valuable as well. We use database jobs quite a bit.
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    Easy to use and easy to integrate into the products and applications we provide for our customers. The flexibility is great.
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    The initial setup is pretty straightforward. The solution is operating well overall.
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    IBM BPM is stable. It has an elaborated way to explore the IBM BPM processes.
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    The initial setup is super simple.I like the business process management engine. It's very detailed, and you can probably map any of the corporate workflow processes you come across in it compared to some of the other solutions out there. I can probably say that it has very good support to work in tandem with other RP solutions in the market. The software is still very user-friendly and integral, and they have pretty good online resources. The automation feature is pretty good, so is the integration feature.
  7. ActiveBatch can automate predictable, repeatable processes very well. There is no real trick to what ActiveBatch does. ActiveBatch does exactly what you would expect a scheduling piece of software to do. It does it in a timely manner and does it with very little outside interference and fanfare. It runs when it is supposed to, and I don't have to jump through a bunch of hoops to double check it.
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  9. The solution's most valuable features are the regular periodic and quarterly updates, they are very useful updates. They keep improving the solution more often, and that helps the platform or code always be up to date with the latest features.
  10. One of the most valuable features is you can create without coding, it is a low code platform.Flexible and drag-and-drop type of UI is very valuable. The integrations are also very good. You can build workflows very quickly, which is my favorite activity. By using the GUI, you can build the entire mechanism, notifications, and all this kind of stuff.

Advice From The Community

Read answers to top Process Automation questions. 543,089 professionals have gotten help from our community of experts.
Anonymous User
Hi peers,  We're considering to offer developers free creation of automation pipelines.  The idea is to get workflow requirements from a customer and build the workflow according to these requirements so what is left for them is to integrate them into their own resources and to execute. Do you see a high demand for anything like that? Please let us know your use cases. Thanks
author avatarRick Murray

I can see where it would be beneficial for some, but if they have the automation tool, I think it would best for them to receive some training on how to do it themselves.

"give a man a fish you feed him for a day. Teach a man to fish and you feed him for a lifetime"

author avatarLinda NamayanjaPMP,CBPA,ITIL
Real User

Yes there definately high demand for workflow automation..its the way to go!

author avatarL'GHOUL Youcef
Real User

Yes indeed. There is a high demande for workflow automation, in order to boost productivity. Also, you will get a good ROI.

Process Automation Articles

Shibu Babuchandran
Regional Manager/ Service Delivery at ASPL Info Services
Sep 14 2021
What Is AIOps? AIOps is the practice of applying analytics and machine learning to big data to automate and improve IT operations. These new learning systems can analyze massive amounts of network and machine data to find patterns not always identified by human operators. These patterns can both… (more)
The Essential Guide to AIOps

What Is AIOps?

AIOps is the practice of applying analytics and machine learning to big data to automate and improve IT operations. These new learning systems can analyze massive amounts of network and machine data to find patterns not always identified by human operators. These patterns can both identify the cause of existing problems and predict future impacts. The ultimate goal of AIOps is to automate routine practices in order to increase accuracy and speed of issue recognition, enabling IT staff to more effectively meet increasing demands.

History and Beginnings

The term AIOps was coined by Gartner in 2016. In the Market Guide for AIOps Platforms, Gartner describes AIOps platforms as “software systems that combine big data and artificial intelligence (AI) or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management and automation.”

AIOps Today

Ops teams are being asked to do more than ever before. In a common practice that can sometimes even feel laughable, old tools and systems never seem to die. Yet the same ops teams are under constant pressure to support more new projects and

technologies, very often with flat or declining staffing. To top it off, increased change frequencies and higher throughput in systems often mean the data these monitoring tools produce is almost impossible to digest.

To combat these challenges, AIOps:

•Brings together data from multiple sources: Conventional IT operations methods, tools and solutions aggregate and average data in simplistic ways that compromise data fidelity (as an example, consider the aggregation technique known as “averages of averages”). They weren’t designed for the volume, variety and velocity of data generated by today’s complex and connected IT environments. A fundamental tenet of an AIOps platform is its ability to capture large data sets of any type while maintaining full data fidelity for comprehensive analysis. An analyst should always be able to drill down to the source data that feeds any aggregated conclusions.

•Simplifies data analysis: One of the big differentiators for AIOps platforms is their ability to correlate these massive, diverse data sets. The best analysis is only possible with all of the best data. The platform then applies automated analysis on that data to identify the cause(s) of existing issues and predict future issues by examining intersections between seemingly disparate streams from many sources.

•Automates response: Identifying and predicting issues is important, but AIOps platforms have the most impact when they also notify the correct personnel, automatically remediate the issue once identified or, ideally, execute commands to prevent the issue altogether. Common remedies such as restarting a component or cleaning up a full disk can be handled automatically so that the staff is only involved once typical solutions have been exhausted.

Key Business Benefits of AIOps

By automating IT operations functions to enhance and improve system performance, AIOps can provide significant business benefits to an organization. For example:

•Avoiding downtime improves both customer and employee satisfaction and confidence.

•Bringing together data sources that had previously been siloed allows more complete analysis and insight.

•Accelerating root-cause analysis and remediation saves time, money and resources.

•Increasing the speed and consistency of incident response improves service delivery.

•Finding and fixing complicated issues more quickly improves IT’s capacity to support growth.

•Proactively identifying and preventing errors empowers IT teams to focus on higher-value analysis and optimization.

•Proactive response improves forecasting for system and application growth to meet future demand.

•Adding “slack” to an overwhelmed system by handling mundane work, allowing humans to focus on higher-order problems, yielding higher productivity and better morale.

Data Is Vital for AIOps

Data is the foundation for any successful automated solution. You need both historical and real-time data to understand the past and predict what’s most likely to happen in the future. To achieve a broad picture of events, organizations must access a range of historical and streaming data types of both human- and machine-generated data.

Better data from more sources will yield analytics algorithms better able to find correlations too difficult for humans to isolate, allowing the resulting automation tasks to be better curated. For example, it’s not hard in most semi-modern monitoring systems to automate some sort of response. However, if response times slow down an application, AIOps would help ensure the correct automated response and not just the “knee-jerk” response that’s statically connected. Adding more capacity to a service may in fact make a slowdown worse if the bottleneck isn’t related to capacity. And it certainly can result in unintended and unnecessary costs in cloud environments. Thus, having the right data to make more complete decisions results in better outcomes.

For total visibility, it’s necessary to access data in one place across all of your IT silos. It’s important to understand the underlying data supporting your services and applications — defining KPIs that determine health and performance status. As you move beyond data aggregation, search and visualizations to monitor and troubleshoot your IT, machine learning become the key to achieving predictive analysis and automation.

Key AIOps Use Cases

According to Gartner, there are five primary use cases for AIOps:

1. Performance analysis

2. Anomaly detection

3. Event correlation and analysis

4. IT service management

5. Automation

1. Performance analysis:

It has become increasingly difficult for IT professionals to analyze their data using traditional IT methods, even as those methods have incorporated machine learning technology. The volume and variety of data are just too large. AIOps helps address the problem of increasing volume and complexity of data by applying more sophisticated techniques to analyze bigger data sets to identify accurate service levels, often preventing performance problems before they happen.

2. Anomaly detection:

Machine learning is especially efficient at identifying data outliers — that is, events and activities in a data set that stand out enough from historical data to suggest a potential problem. These outliers are called anomalous events. Anomaly detection can identify problems even when they haven’t been seen before, and without explicit alert configuration for every condition.

Anomaly detection relies on algorithms. A trending algorithm monitors a single key performance indicator (KPI) by comparing its current behavior to its past. If the score grows anomalously large, the algorithm raises an alert. A cohesive algorithm looks at a group of KPIs expected to behave similarly and raises alerts if the behavior of one or more changes. This approach provides more insight than simply monitoring raw metrics and can act as a bellwether for the health of components and services.

AIOps makes anomaly detection faster and more effective. Once a behavior has been identified, AIOps can monitor and detect significant deviations between the actual value of the KPI of interest versus what the machine learning model predicts. Accurate anomaly detection is vital in complex systems as failures often exist in ways that are not always immediately clear to the IT professionals supporting them.

3. Event correlation and analysis:

The ability to see through an “event storm” of multiple, related warnings to identify the

underlying cause of events. The reality of most complex systems is that something is always “red” or alerting. It’s inevitable. The problem with traditional IT tools, however, is that they don’t provide insights into the problem, just a storm of warnings. This creates a phenomenon known as “alert fatigue”; teams see a particular alert that turns out to be trivial so often that they ignore the alert even on the occasions when it’s important.

AIOps automatically groups notable events based on their similarity. Think of this as drawing a circle around events that belong together, regardless of their source or format. This grouping of similar events reduces the burden on IT teams and reduces unnecessary event traffic and noise. AIOps focuses on key event groups and performs rule-based actions such as consolidating duplicate events, suppressing alerts or closing notable events. This enables teams to compare information more effectively to identify the cause of the issue.

4. IT service management (ITSM): A general term for everything involved in designing, building, delivering, supporting and managing IT services within an organization. ITSM

encompasses the policies, processes and procedures of delivering IT services to end-users within an organization. AIOps provides benefits to ITSM by letting IT professionals manage their services as a whole rather than as individual components. They can then use those whole units to define the system thresholds and automated responses to align with their ITSM framework, helping IT departments run more efficiently.

AIOps for ITSM can help IT departments to manage the whole service from a business perspective rather than managing components individually. For example, if one server in a pool of three machines encounters problems during a normal-load

period, the risk to the overall service may be considered low, and the server can be taken offline without any user-facing impact. Conversely, if the same thing were to happen during a high-

load period, an automated decision could be taken to add new capacity before taking any poor-performing systems offline.

In addition, AIOps for ITSM can help:

• Manage infrastructure performance in a multi-cloud environment more consistently

• Make more accurate predictions for capacity planning

• Maximize storage resource availability by automatically adjusting capacity based on forecasting needs.

• Improve resource utilization based on historical data and predictions

• Manage connected devices across a complex network

5. Automation: Legacy tools often require manually cobbling information together from multiple sources before it’s possible to understand, troubleshoot and resolve incidents. AIOps provides a significant advantage — automatically collecting and correlating data from multiple sources into complete services, increasing the speed and accuracy of identifying necessary relationships. Once an organization has a good handle on correlating and analyzing data streams, the next step is to automate responses to abnormal conditions.

An AIOps approach automates these functions across an organization’s IT operations, taking simple actions that responders would otherwise be forced to take themselves. Take for example a server that tends to run out of disk space every few weeks during high-volume periods due to known-issue logging. In a typical situation, a responder would be tasked with logging in, checking for normal behavior, cleaning up the excessive logs, freeing up disk space and confirming nominal performance has resumed. These steps could be automated so that an incident is created and responders are notified only if normal responses have already been tried and have not remedied the situation. These actions can range from the simple, like restarting a server or taking a server out of load-balancer pools, to more sophisticated, like backing out a recent change or rebuilding a server (container or otherwise).

AIOps automation can also be applied to:

•Servers, OS and networks: Collect all logs, metrics, configurations and messages to search, correlate, alert and report across multiple servers.

•Containers: Collect, search and correlate container data with other infrastructure data for better service context, monitoring and reporting.

•Cloud monitoring: Monitor performance, usage and availability of cloud infrastructure.

•Virtualization monitoring: Gain visibility across the virtual stack, make faster event correlations, and search transactions spanning virtual and physical components.

•Storage monitoring: Understand storage systems in context with corresponding app performance, server response times and virtualization overhead.

•Application monitoring: Identify application service levels and suggest or automate response to maintain defined service level objectives.

AIOps and the Shift to Proactive IT

One of the primary benefits of AIOps is its ability to help IT departments predict and prevent incidents before they happen, rather than waiting to fix them after they do. AIOps, specifically the application of machine learning to all of the data monitored by an IT organization, is designed to help you make that shift today.

By reducing the manual tasks associated with detecting, troubleshooting and resolving incidents, your team not only saves time but adds critical “slack” to the system. This slack allows you to spend time on higher-value tasks focused on increasing the quality of customer service. Your customer experience is maintained and improved by consistently maintaining uptime.

AIOps can have a significant impact in improving key IT KPIs, including:

• Increasing mean time between failures (MTBF)

• Decreasing mean time to detect (MTTD)

• Decreasing mean time to investigate (MTTI)

• Decreasing mean time to resolution (MTTR)

IT organizations who have implemented a proactive monitoring approach with AIOps have seen significant improvement in a variety of IT metrics, including:

How to Get Started With AIOps

The best way to get started with AIOps is an incremental approach. As with most new technology initiatives, a plan is key. Here are some important considerations to get you started.

Choose Inspiring Examples

If you’re evaluating AIOps solutions, platforms and vendors for your organization, you’ve got a big task ahead of you. The most challenging aspect may not be the evaluation process itself, but gaining the support and executive buy-in you need to conduct the evaluation.

If you choose inspiring examples of other, similar organizations that have benefited from AIOps — and have metrics to prove it — you’ll have a much easier time getting the go-ahead. A good partner can help you do that.

Consider People and Process

It’s obvious that technology plays an important role in AIOps, but it’s just as important to make a plan to address people and process.

For example, if an AIOps solution identifies a problem that’s about to happen and pages a support team to intervene, a responder might ignore the warning because nothing has actually happened yet. This can undermine trust in the AIOps solution before it has a chance to be proven in operation.

It’s also important to give IT teams the time to work on building, maintaining and improving systems. This vital work can’t be assigned as a side project or entry-level job if you expect meaningful change. Put your best people on it. Make it a high priority so other work can’t infringe on it. AIOps practices are iterative and must be refined over time; this can only be done with a mature and consistent focus on improvement.

You’ll also need to re-examine and adjust previously manual processes that had multiple levels of manager approval, like restarting a server. This requires trust in both technology and team practices. Building trust takes time. Start with simple wins to build cultural acceptance of automation. For example, be prepared to build historical reports that show previous incidents were correctly handled by a consistent, simple activity (such as a restart or disk cleanup) and offer to automate those tasks on similar future issues. Choose a solution that allows for “automation compromise” by inserting approval gates for certain activities. Over time, those gates should be removed to improve speed as analytics proves its value in selecting correct automation tasks.

Finally, include in your plans a campaign to reassure staff that AIOps is not intended to replace people with robots. Show them how AIOps can free up key resources to work on higher-value activities — limiting the unplanned work your teams have to endure each day.

The Bottom Line: Now Is the Time for AIOps

If you’re an IT and networking professional, you’ve been told over and over that data is your company’s most important asset, and that big data will transform your world forever. Machine learning and artificial intelligence will be transformative and AIOps provides a concrete way to leverage its potential for IT. From improving responsiveness to streamlining complex operations to increasing productivity of your entire IT staff, AIOps is a practical, readily available way to help you grow and scale your IT operations to meet future challenges. Perhaps most important, AIOps can solidify IT’s role as a strategic enabler of business growth.

Evgeny BelenkyGreat article, @Shibu Babuchandran! Thank you for sharing your knowledge with… more »
Tjeerd Saijoen
CEO at Rufusforyou
Sep 03 2021
ICT is getting more and more complex: today I have several systems in Chicago, several more in Amsterdam and if you need to protect your environment you will need to check on-premises, the cloud at Amazon, and the cloud at Microsoft Azure.  Why is Performance related to security? For the… (more)

ICT is getting more and more complex: today I have several systems in Chicago, several more in Amsterdam and if you need to protect your environment you will need to check on-premises, the cloud at Amazon, and the cloud at Microsoft Azure. 

Why is Performance related to security?

For the following reasons: 

Today we need more than one tool to protect our environment. You need anti-spoofing, antivirus, firewalls, protection against DDOS, etc. All these tools can slow performance and if you experience performance slowdowns, it affects both your end-users and your business.

This can affect your profits. For example, if I sell airline tickets online and without performance problems, I can sell 10.000 an hour but due to performance slowdowns, I sell only 7.500. That is a loss of profit, and the planes will leave with empty seats.

If a hacker attacks our systems a performance tool capable of detecting unusual behavior will alert us because most of the time CPU usage will go up and transaction times will go down.

Are Security and Performance enough or do we need more?

If we take security and performance seriously, we need more. What do we need and why?

Automation is the key: if a hacker tries to penetrate your systems, you'll get alerts from your security and performance tools. Now you’ll need to do something and if you'll need to do this manually an event will be sent to your service tool and a ticket will be created. Your helpdesk team will then start processing the ticket. Before this process is finished a hacker could be able to break into your system.

Now we have an automation tool. It is possible to automate everything. Some policies we activate in our automation tool need to block, for example, a part of the network or require a system restart after a policy becomes activated. 

Because of this, you have a lot of work to separate action rules in. For example, golden rules, requiring a restart and, thus, need to be scheduled in your change management unless they require immediate action. Silver ones require direct action, but with a review of a technical engineer before action has been taken. Bronze ones result in automated action.

Now we have several tools to improve performance and secure our environment. 

What is the fiscal impact? A lot, if we calculate on average a minimum of 3 agents or licenses are needed (often costing around 70$ per server). This equals 210$ per server per month. 

You’ll probably need one engineer to keep this running and several engineers to check the monitors. If I compare this amount with some vendors, it can easily become much more. 

Besides the capacity those agents use are taken from my server, you’ll need more CPU resources, more memory, and more disk space.

Is it possible to reduce this? Yes, by using integrated software: we have 2 agents integrated with performance and security running in a SaaS delivery model for our customers, reducing the price and checking all kinds of environments on security, performance, networks, and automation.

If your systems are blocked with ransomware, it will be a lot more expensive. So proactive joint with automation can protect your systems better - never for 100% but it will come close.

Shibu BabuchandranVery good insights about correlation for security with performance.
Johann DelaunayInteresting positioning and way of thinking, thank you very much for the… more »
Shibu Babuchandran
Regional Manager/ Service Delivery at ASPL Info Services
Aug 31 2021
Future of NOC transformation unifies IT teams NOC transformation could lead to unified IT operations with cross-domain teams, but not all enterprises need radical change when smaller upgrades and modernization do the job. In the technology world, it can be easy to throw around the word… (more)
Future of NOC

Future of NOC transformation unifies IT teams

NOC transformation could lead to unified IT operations with cross-domain teams, but not all enterprises need radical change when smaller upgrades and modernization do the job.

In the technology world, it can be easy to throw around the word transformation and lose the nuances of what it entails.

Consider the networking industry. Remote work requires enterprises to rethink VPN strategies and management. Network automation means network practitioners have to shift from manual tasks and trust automated processes. Advances in security and visibility result in more collaboration with security teams. Like a chain reaction, each of these developments influences other areas, spurring more change, such as Network Operations Center (NOC) transformation.

Transformation occurs in varying increments and levels, depending on enterprise strategies, risk and motivation -- and the same applies to NOCs. Some companies don't have -- or need -- NOCs, some are gradually modernizing their NOCs and some are pursuing full-blown NOC transformation.

The role of traditional NOCs

For years, organizations have used NOCs to maintain an operational view of the network and the services running across it. NOC technicians and analysts follow certain best practices to monitor network performance, handle service desk tickets, triage and troubleshoot, and, if needed, escalate problems.

But many businesses don't function the same way they did five years ago -- or even one year ago -- and various factors are reshaping network operations strategies and priorities. The global pandemic is one obvious stimulant. But progress in server virtualization, IoT, cloud, containers and microservices has also sparked NOC transformation.

As technology has evolved, network traffic flows have changed, and application support is more complicated. As a result, network operations need to be more proactive and implement comprehensive visibility tools for their environments.

For example, end-users recognize one-third of all IT service problems before NOC technicians or other teams are alerted, which means one-third of all problems can impede business productivity before IT is aware of them. Remote work has exacerbated many of those management concerns, prompting network technicians to retool so they can achieve visibility into home office networks. Those tools include remote desktop access, endpoint transaction monitoring and laptop agents that generate test traffic to gauge latency and dropped packets, he said.

As operating models change, network teams should shift from tactical tasks -- in which they simply deploy, fix and maintain operations -- to strategic tasks that enable innovation and automation.

NOC Transformation doesn't look the same for every organization.

Virtualization and automation drive NOC modernization

Many NOC upgrades aren't radical transformations; rather, they're part of business strategies to virtualize, consolidate or modernize networks. Network teams undertake these upgrades to meet their goals of reduced downtime, improved end-user satisfaction and increased innovation within IT.

Within networking, teams are prioritizing modernization in the following areas:

  1. network security
  2. network virtualization
  3. network automation
  4. network operations optimization

With network operations optimization, teams look at how they can improve service-level agreement compliance and accelerate their mean time to resolution. In some cases, NOC teams troubleshoot issues that are originally perceived to be network problems, which they later discover to be security incidents. That time-lapse could be critical in the event of a breach or attack -- and could be shortened if network teams worked with security teams.

"Over the last four or five years, network operations teams -- whether they're in a NOC or a cross-domain team -- are trying to work more closely with security,"

Also, enterprises shift their network operations strategies to prioritize integrated network and security management, noting how networking and security are "increasingly bonded." As the integration of the two previously siloed departments strengthens, so too does IT innovation.

NOC transformation with unified operations

Enterprises that are focused on IT innovation and optimizing network operations could pursue a more transformational operations strategy. Perhaps the most ambitious NOC transformation is one that eliminates the standalone NOC and security operations center in favor of a unified operations center that includes networking, security, cloud and applications teams.

The goal of this unified approach is to streamline operations so all applications and services are highly resilient and avoid long downtimes, he said. Cross-domain teams collaborate to prevent trouble proactively instead of reacting to issues, helping enterprises achieve the innovation they desire.

Operations teams, however, need IT leadership guidance if they want to implement a unified operations approach. Different teams might not always get along, but the initiative is more likely to succeed with leadership support.

Another important factor to consider is data, which could be an asset or obstacle to a unified IT operations approach.

"[Networking and security] might have their own data repositories they guard jealously and don't want to share. If they do share, they might find their data conflicts with each other.

A way to address that issue is to have a common data set. Enterprises can implement a fabric that centrally distributes traffic to the individual tools each team uses. Those tools clean data from the same fabric, so teams can collaborate better and share data. The teams can also share an analysis tool -- with clear processes on how to use it -- to provide common views, reports and dashboards.

NOC transformation is not for everyone

Moving away from a standalone NOC to a unified operations approach can help streamline IT operations and improve overall service delivery. But independent NOCs are still an established and reliable way to monitor operations -- and moving away from them is a disruptive strategy that might not be for every organization.

"NOC transformation isn't going to be for everyone, and it isn't necessarily a best practice to go from a traditional NOC to something like an integrated cross-domain operation center" .

Find out what your peers are saying about BMC, Camunda, Pega and others in Process Automation. Updated: October 2021.
543,089 professionals have used our research since 2012.