Seeq Reviews

My main use case for Seeq is real-time process data analysis, as we manufacture glass and try to analyze all the process signals to optimize cycle times and the quality of our glass.

We use Seeq's Workbench to analyze those signals and optimize the process by creating different conditions and rules to visualize them in a table-type format, and we also have it in a graph in signal format with its recorded date and time. We configure it so that at the end of each shift, each line receives its report on how production has gone, and we have a series of alerts configured for when the signals reach a configured trigger.

We are now trying to use Seeq to do data analytics on the best process parameters for our different recipes, and for that, we use Seeq's DataLab, which is a Jupyter environment with Python code. This is a very powerful tool that we are exploring.

I consider the best features that Seeq offers to be the fast visualization and integration with our current systems, as we connect it via OPC. At the moment we create the tag on our PI server, we already have all the rules configured and can visualize as many elements and variables as we want. At the user level, it is very easy to see the signal waves and quickly understand what has happened in production without needing much knowledge of the tool.

The fast visualization has impacted users' daily work by allowing us to create real-time dashboards for operators to have on the line since they are the ones who use this tool every day and can correct deviations in the process just-in-time, which is the point where I want to highlight Seeq's potential.

I think there is a lot of training material for new users, including many videos and resources that allow you to go in depth to create very powerful visualizations.

Seeq has positively impacted our organization by helping us have all the data on the same platform, analyze process data at an organizational level, and identify points where we can improve our processes. Identifying certain patterns that cause certain quality defects is a concrete example of an improvement identified thanks to Seeq.

I think Seeq could improve by including a section that has SQL-type visualizations to visualize tables of data and not reference them to the timestamp, as that has caused us some problems at times.

The features I feel are missing for the platform to be perfect include being able to use tags or variables recorded in the same time span.

I have been using Seeq for a year and a half.

I consider Seeq to be a stable platform since we have not had any issues with data loss or maintenance.

I rate the scalability of Seeq as very easy.

In what I have experienced, Seeq's customer support is very willing to collaborate with us.

I have used several tools before Seeq, but none as powerful as Seeq. I have used all the Power BI tools with real-time Data Lake and different visualization environments with Python code.

I have seen a return on investment with Seeq, with time savings for our process supervisors of around two hours a day since they are more efficient and have all the information in a single place. They do not have to look for information in Excel, Power BI, or other more rudimentary platforms, and the time it takes to implement new panels with new signals from an engineering point of view has also improved.

My advice to other professionals considering implementing Seeq is that they should definitely go for it, because among the tools on the market, it is very easy to integrate, easy to scale, and easy to visualize in different environments. I give this review a rating of nine out of ten.

Private Cloud

My main use case for Seeq involves monitoring data trends and studying historian data to conduct modeling.

A specific example of how I use Seeq for monitoring and modeling is through data monitoring, which is quite important because Seeq has many functions to filter out data that is not needed, such as plant stoppage or abnormal conditions. It performs excellently in terms of filtering. Based on the formula, I can also create many predictive models to determine how the predictive line looks when certain parameters are increased. Additionally, Seeq can generate very good reports based on trends with explanations. These three aspects are what I focus on primarily with data monitoring.

Regarding modeling, because of the filtering features, it helps me quickly develop a historian model, producing a good trend line for modeling. For example, as a process control engineer, whenever I implement automated systems, I have to monitor the trend to see whether the scripting I put in aligns with the actual scenario. Seeq can present several trends on a single page for quick comparisons, and the timeline is quickly accessible, allowing me to see values such as the average, minimum, and maximum efficiently.

In my opinion, the best feature Seeq offers is the filtering. Previously, when using traditional tools such as Excel, extracting data caused the system to lag, and filtering required setting specific conditions, often more than one.

Seeq stands out for me in filtering compared to traditional tools such as Excel because it is easier and more powerful.

Seeq has positively impacted my organization by making work more effective. It used to take months to collect data, model, and filter, but now Seeq saves time, allowing us to complete tasks in a week or less. Additionally, we used to utilize many tools to conduct monitoring and alert generating, but now it is just a single tool that everyone can access quickly and easily.

In terms of needed improvements, for machine monitoring, we used to create our own software to conduct monitoring. Previously, this involved modeling in Excel, generating models, and using that software to detect and report. Machine alerts have sometimes happened for a month, which can be critical. With Seeq, we can generate weekly reports for our customers or stakeholders to highlight important notes and necessary actions, especially in terms of maintenance. One quick example would be that we have saved a significant amount of money in machine downtime due to these alerts, and we also extended maintenance periods by monitoring machine conditions and determining safety margins to extend maintenance during critical supply periods effectively.

As a beginner with Seeq, I think one improvement would be to have more simplified formulas. While there are already AI agents to provide guidance, the formulas can still be a bit confusing. Additionally, it would be beneficial to have more training sessions for the company—not just formal certificates, but ongoing certification at different levels to enhance user quality and skills. This is similar to how Aspen software values certification, which can enhance acceptance in the job market.

I have been using Seeq for about six years.

Seeq does experience occasional breaks in data extractions, but I find the service provided is quick enough to cover any downtime. Since we are using an on-premises cloud, the services offered are more direct, which is beneficial. However, improvements in downtime could still be made, although I consider the service sufficient.

Regarding Seeq's scalability, when users and data grow, the stability is not as great as it was initially, which we monitor. Nevertheless, the communication between customers and Seeq is good as they strive to improve stability. I would say it is not as stable as it began, but they are working on enhancements.

The customer support from Seeq is responsive and helpful when I have issues or questions. They provide hotlines for users to contact monthly, as well as urgent lines for quick technical support. The addition of the AI feature for reaching support conveniently is also a great recent enhancement.

Before using Seeq, we previously utilized solutions such as Google Sheets and Excel for data extraction, filtering, and basic modeling associated with control systems. We switched to Seeq because it offered quicker data filtering and generating alerts that added significant value for our team, making it easier to respond to machinery issues. Initially, we were testing the waters with Seeq, but we found it very effective for reducing workload and improving efficiency, which is why we made the switch.

My advice to others looking into using Seeq is that you need at least a basic understanding of how to handle data before diving in. While they provide basic training, I find that the two to three-day training is quite intensive, and without prior data knowledge, it can be challenging. Additionally, understanding the background cloud system, service provider, and contract details is crucial for building the needed infrastructure to connect with Seeq. I would rate my overall experience with Seeq a seven out of ten.

On-premises

Other

In the initial six to eight months, I used it for process analysis, like identifying the different tags through a historian and then making some use cases out of them. This is a very simple yet powerful tool that can connect to the historian in real time, and then we can perform some formula-based calculations. It is very useful.

So, I started with basic process analysis and then went into the predictive part, which is also very useful. I've used the prediction part to create different inferences in my industry. 

Some lab results and analyses take time, like eight hours or seven days. So, to create real-time inferences for such signals, Seeq was very useful through its predictive capabilities.

The basic process analysis could be done anywhere on any software or platform nowadays. But the techy part of Seeq is its strongest point. 

Time-series analysis can be done in any computer software, but having the predictive part included in the package is a plus point, according to me.

I have a couple of suggestions for the organization, and I've told them as well.

Nowadays, time-series analysis for manufacturing units is a primary need. What they're looking for is the incorporation of artificial intelligence and machine learning into these tools and then getting some insights out of it, which is closed-loop. Till now, we have been facing open-loop solutions. But how to apply these insights or inferences into my manufacturing unit, which is running 24/7?

Being an engineer and a person who comes from the industry, I would rather believe in first principles than a data science model. To break the ice, they need to come up with more of the predictive part of it using machine learning techniques, which can be closed-loop solutions, which can help operators and automation engineers to apply these insights into the units rather than keeping it open-loop.

I have been using it for a year now.

Stability issues happen multiple times because it may be the on-premise tool issue. It all depends on the server's capability. If the hardware is not sufficient and multiple users are using it, it breaks down, the server load increases, and then nobody is able to work on it.

However, for the cloud part, it is not that easy to integrate a cloud solution into a manufacturing unit because our OT network has so much automation. OT-to-IT integration itself is a big question. So, sometimes, some organizations believe in going to the cloud, and some may not be willing to.

In terms of software stability, I'd rate it maybe seven out of ten.

Considering the right person is using it, and they're trained, it is very scalable. On the scalability part, I'll give it around nine out of ten.

A certain amount of training is required to use the tool. It's not like you can just directly jump on it and create some models or even do basic process analysis without taking training. So, preliminary training is required to use this tool. And to master it, more rigorous use and a couple of use cases development are required to go ahead with the flow.

For the deployment part, currently they have closed on-premise support. Now they're only offering cloud solutions. But previously, they used to provide both on-premise support and cloud. 

When I worked on this tool, we had the facility of on-premise support. 

But it requires a certain amount of hardware and infrastructure to be developed. Sometimes it becomes messy and clumsy to establish this hardware and connect it with your historian. I think they might have stopped this on-premise support because it becomes very difficult and technical for a non-technical person to establish and connect these tools with your hardware. 

And also, if the number of users increases, the server gets loaded and everything stops. So that is an issue for the hardware. But they have stopped the on-premise support and totally went into the cloud. So, that problem might not be happening right now.

It has a moderate cost compared to other tools and solutions out there in the manufacturing sector.

Overall, I would rate it an eight out of ten. The reason why I'm cutting points is purely because they're still not applying deep learning techniques for the predictive part of the solution. They have to integrate deep learning techniques to solve some of the more sophisticated issues in the industry.

My recommendation depends on the goal of an organization or a person. What they want to achieve through this tool. If they're just going for routine process analysis, they might not be very enthusiastic about it. 

But when it comes to technology integration with automation and developing various solutions in the company, if that maturity is present in the organization, they'll definitely take it seriously.

In that aspect, I'd recommend they should use it and explore the capabilities of the tool. However, I'll advise you that it's not a one-stop solution for all analyses. You have to use other tools and solutions to get a holistic approach or direction.

We use Seeq to convert real-time data into graphs for monitoring and surveillance. It helps us track real-time data and visualize it effectively. In the oil and gas industry, we use Seeq for process optimization. We analyze real-time data to understand downtime and other issues from the previous day at specific wells. This data helps us make informed decisions and plan future actions based on past performance.

Seeq helps with decision-making by providing real-time data. One benefit we've seen is with real-time data. For example, a few weeks ago, we had a planned event for a well, but we couldn’t perform it because the healthy temperature was too low. With Seeq, we could monitor the temperature and pressure in real-time, which helped us avoid potential issues that could have occurred if we hadn't used Seeq.

I like Seeq because it’s very useful for production surveillance. It provides detailed insights, like the ability to view data down to the minute, which is helpful for tracking when a well goes offline. This feature is particularly beneficial for our needs. However, we don’t use Seeq for predictive analytics; we handle that with other software.

Regarding AI, I wish Seeq had AI capabilities. For instance, if Seeq could analyze graphs and offer recommendations, it would be advantageous. Currently, there is no AI feature in Seeq.

In terms of improvements, it would be great if Seeq could enhance its data export features and possibly integrate more functionalities to reduce the need for multiple software tools. I would like a specific feature related to predictive analysis, which Seeq currently lacks.

I’ve been using Seeq for about eight to nine months now.

Regarding stability, I've noticed that Seeq can sometimes be slow, even when I'm using a good Wi-Fi connection. I’ve reported this issue, and it seems the problem might be with the software rather than my connection.

As for scalability, I haven't had any experience with increasing capacity or adding new users, as our team size has remained the same. We haven’t tried scaling Seeq yet, so I can’t provide feedback.

About ten to twelve people in my team and a few others in the organization use Seeq.

I haven’t contacted technical support for Seeq so far, but it might be useful in the future.

I wasn’t involved in the initial deployment; our IT team handled that. As for onboarding, I found it to be straightforward. Initially, it was a bit challenging, but it was easy to learn once I got used to it.

We have an IT team that handles the maintenance. We use Seeq across multiple locations and departments.

Regarding other data and analytics services, we use Kappa for analysis and Seeq for surveillance monitoring. The two are pretty different, so I can’t compare them directly. Seeq’s features are specific to monitoring, while Kappa is used for analysis.

I would recommend Seeq to others in the oil and gas industry for production optimization. It’s essential for that purpose. Overall, I would rate Seeq around an eight or nine. If I had to choose a whole number, I’d go with nine.

We used real-time sensor data to predict equipment failures and other issues. The data came from a fracturing process, including lubrication systems, and was processed in Seeq. Seeq has three apps: one for visualization, Workbench (a point-and-click tool), and DataLab, which is Python-oriented. I used all three to build use cases.

For process optimization, predicting equipment failure helps save costs by preventing downtime. This directly impacts production and reduces non-production time. Seeq is cloud-based and connects to various data warehouses like Amazon, Microsoft Azure, and others. You can integrate it with around 200 data providers, which gives flexibility for data storage. However, Seeq doesn’t store data; it requires connection to external databases.

What I liked most about Seeq was its user-friendly design. The Workbench app was made for everyone, not just data scientists. Engineers and business analysts could use it to build analytics solutions quickly without needing to code. Seeq offers many examples and training resources, like online help, chat support, and office hours. They even provide consulting when needed. I appreciated the range of functions available, especially how Seeq handles time series data. You can apply functions and see results immediately, without altering the original data, which allows you to undo steps and go back if needed.

Seeq's ability to set up predictive models and monitor variables, setting alarms for thresholds, was particularly useful. You could easily apply solutions to different processes, such as monitoring pumps or generators. The platform’s flexibility extends to data conversion and filtering, and it has an outstanding feature where it doesn’t modify the raw data during analysis.

In terms of decision-making, Seeq’s daily training sessions helped users maximize its capabilities. Less than 10% of users had certification at the time, but there were efforts to increase that number. Seeq also offered consulting to help users develop their projects.

As for improvements, I felt there should be a more efficient way to address repetitive customer questions, perhaps using chatbot technology to streamline responses. In terms of functionality, I noticed that certain machine learning methodologies, like Principal Component Analysis (PCA), were missing from the Workbench. While it was possible to perform these analyses in DataLab, adding such features to Workbench would enhance user experience and allow for more complex calculations without needing to switch environments.

Last year, I worked with Seeq in the oil and gas industry, specifically for electric mains. 

Overall, I rate the solution a nine out of ten. 

We don't directly use Seeq. If users face issues, like not being able to see data, we help them and provide access. We do those types of support activities.

Since it was onboarded recently, I don't have much knowledge, but I've done basic things like viewing data using the workbench analysis. 

Also, I've worked on adding new data sources, new AF connections, and PI connections. I've also worked on enabling the data sources and their databases.  

It's an easy tool. A beginner can easily learn to use Seeq for the first time. They can complete the training, and once they complete the training, they'll have an idea of how to use Seeq.

Improvement-wise, I think it's okay. Sometimes we face a performance issue in Seeq, like the server load getting increased. That is one of the major issues we've observed recently. 

We work with the Seeq support team to find the cause. We create a performance dashboard to identify why the Seeq cloud load is increasing. We try to work on the cause.

I have nearly one year of experience with Seeq. We onboarded this application recently, about a year ago.

It's used all over the region. Approximately a thousand-plus users use it. Not just a thousand, it will be more. More than a thousand.  

I have experience in PI and AtDoIt. Including that, I've been supporting Seeq for nearly a year.

So, I currently support three applications, one of which is Seeq. 

The initial setup is very easy. I've done installations and upgrades of Seeq in the remote agent service. We have nearly ten remote agent servers based on the business unit. 

Based on the region, we have different, like, nearly ten remote agent servers. I have installed and upgraded Seeq. This activity is very easy. We have an EXE file, and we can run it accordingly. It's the easiest way to install Seeq.

If we need to add new data sources, like if we try to add a new connection to Seeq, then we need to go with the step-by-step process. First, we need to enable the firewall, then establish the connection, then edit the link. There's a step-by-step process we do.

Every three months, we do a Seeq key change activity. This is one of the activities we are performing every three months - the Seeq agent key change.

And since the password should be set to a three-month expiry period in our organization, we do that activity as well. Apart from that, these are the things I currently take care of.

I have knowledge in adding data sources and monitoring the admin page. I check for disconnections and if any occur, I investigate what's wrong with the server.

It's deployed on a private cloud.

It's the easiest way [tool] to view the data. The things that are in PI will be displayed over Seeq. If something's not working in Seeq, first of all, we need to check whether there's anything wrong in PI. 

Most of the issues we face in Seeq, like if the data is not updating or it throws some error, we first need to check the value of the tag that is updating in PI. 

Most of the time, there might be an issue at the PI end. So, based on that, we troubleshoot the issues accordingly.

Overall, I would rate it an eight out of ten. I feel we do have different tools. I don't know specifically what's unique to Seeq, but we have other tools like PI and AtDoIt where we can see the data. We can switch back, extend the dates, and view historical data in other applications as well.

Apart from that, it's the easiest way. We can just pull the tags and go to whichever date we need to view the data. It is a user-friendly tool. Seeq is a good application where we can easily access it.

Private Cloud

I'm a process engineer in the oil and gas field. I use the solution for daily monitoring, surveillance, and time series analysis, where I can build prediction models and root-cause failure models. We're building a model with Seeq.

Seeq has a lot of beneficial features for process engineers like us. The solution's most valuable feature is the prediction model. We can see the picture only for a particular time if we highlight certain conditions, a certain timeline, or some boundary or threshold.

Suppose you want to do a failure analysis to see what went wrong. We'll give a threshold to parameters, highlighting my parameters, what went wrong, and how it went wrong. I can build a relatively small model or deep analysis to show the parameters or contributors for a failure analysis.

Seeq is evolving. We only included time series analysis in the first version. Going forward, we introduced many complex Seeq tables. You give the temperature and pressure, and the solution will give you the enthalpy of steam. It would be wonderful if a time series model could be built with the thermodynamic package. We have Aspen Hysys for thermodynamic package dissimulation. When we interpret both, it will be magical.

I have been using Seeq for five years.

The solution does not break down, but there will be a delay in buffering or caching if you are caching data for more than ten years. That usually happens due to the size and capacity.

I rate the solution’s stability an eight out of ten.

I'll create a model for one piece of equipment and scale it up to hundreds of pieces of equipment.

I rate the solution a nine out of ten for scalability.

The solution's technical support is very good. Customers have a weekly support call with Seeq. You can also contact Seeq support engineers, who will help you with whatever you need. Also, you can write your doubts on the Seeq forum. Experts or anyone well-versed in Seeq will answer your doubts.

Positive

The solution's initial setup is very simple. You'll have a virtual server either in the cloud or on-premise, and the installation takes just half an hour because it's a web-based application.

Seeq is a tool that does not store any of your data. It will only cache the data and show it on display. Whether it is an on-premise or cloud version, you cannot hamper any data on Seeq security-wise. Just like Google searches for information from the website, Seeq will buffer the information from the historian and display trends or values. The solution does not have much of a data breach or safety issue.

Seeq is not a tool that requires a programming background. It's just a point-and-click tool that is for everyone. The idea behind Seeq is democratizing data analytics for everyone. You don't need to have any rigorous programming knowledge. Instead, basic knowledge of how the tools work is fine.

There is something called Data Lab in Seeq, where an advanced Python algorithm is used to deploy AI and ML models. It has already been developed, but not on a very large scale. We don't know its potential yet. It has the capacity, but no one has explored its maximum potential because we are limited in our use cases. We are exploring it, and it can be deployed for a big-scale project for advanced predictions.

I would recommend the solution to other users. If you want a feature, you can discuss it with the developer team. If it is a useful feature, it will be applied to everyone. They will develop this as a feature, which will be included in the subsequent releases of Seeq. Seeq is evolving by hearing a lot from the audience.

Overall, I rate the solution a nine out of ten.

The solution is used for time series data analysis. It is very useful for the oil and gas industry, where we use compressors, pumps, and gas turbines and need to monitor them remotely. Seeq is useful in such cases. I support more than 100 pieces of equipment for a customer in the oil and natural gas industry.

There are ten gas turbine generators for power generation. We have to monitor them. A gas turbine has 150 to 200 parameters to monitor. We have to monitor vibration parameters, lube oil system, and auxiliary systems and notify the organization if we notice any abnormalities.

We use Seeq to analyze the parameters daily, weekly, or monthly. If we notice any deviation, we must analyze it. For example, if we see an increase in the bearing temperature, we immediately discuss it with the on-site team. The team checks whether the lube oil is contaminated. If there is contamination, the team takes action accordingly. It could also be a bearing issue. We must run the machines without any damage to avoid catastrophic failures.

Seeq is a useful product. The systems are not operated manually in the oil and gas industry. They are operated remotely. Usually, if pumps are used, there will be two or three pumps. Two pumps would run, and one would be on standby. If a pump needs to run for 500 hours, we can calculate it using Seeq. Once the 500 hours is up, the pump stops, and the standby pump automatically starts running. It is one of the biggest advantages of the solution.

We built a detector dashboard. We set the upper and lower limits and check if things are operating within that limit. For example, if the upper limit is 75 and the lower limit is 50, and the trend is between these limits, the dashboard must show it as green. If it is below or above the operating levels, it must show a red color.

There are 100 parameters. If such a dashboard is built, it will be very useful. We only have to validate and analyze the parameters in red. We need not check the green ones. It is a revolutionary concept.

We can see increasing trends in vibration parameters using predictive analysis. We can easily find out how many days the trend will hit the upper or lower limits. Since we have time, we can inform the on-site team that monitors it. For example, we can inform the team that the lube oil tank level will drop to the lower limit within a month. So, the team gets one month’s time to top up the level easily. Such predictive analytics is very useful to the industry.

The tool is user-friendly. It is very easy to use. We can learn it in a month. However, we must understand where to use the tool. For example, if there is a mechanical engineer, they might know about pumps. They must also know what parameters must be analyzed for the pumps. Seeq is just a medium. We must do the analysis ourselves. Seeq is a friendly and useful tool. It has no issues.

We face a bit of an issue if there are any server issues or upgrades from Seeq.

I have been using the solution for more than three years.

Stability issues are rare. If the server is good, we have no issues. It is related to the PI Server. It is one of the data-collecting servers. Our customers’ servers are interconnected with the PI Server. I rate the product’s stability an eight out of ten. I have not faced many issues in the past three years. It is one of the best tools for static analysis.

The product’s scalability is good. The tool increases the functionality and productivity of the products.

The support team is proactive. If we face any issues, the support persons respond immediately. If there is a data gap issue, it is related to PI connectivity. We communicate it immediately to the support persons, and they respond within one to six hours and take the necessary actions.

Neutral

Users need basic training to use the tool. They must know how to add tags and how to build things. I am a consultant for an oil and gas customer. They have Seeq, and they give us remote access to their server.

We provide support. I will recommend the solution to others. It provides more options for scatter plots. They are very useful. We only analyze x and y in scatter plots. If there is a flow, I can add two or three tags to correlate them simultaneously. These options are very useful.

Overall, I rate the tool an eight out of ten.

I represent the consulting part of our company. We support multiple customers in India, Southeast Asia, US, and Europe. Our daily requirements involve building data science algorithm applications using Seeq Data Lab, building workflows using Seeq Workbench, and developing dashboards on top of Organizer topics.

The impact of Seeq's predictive analytics features really depends on the industry. In oil and gas, for example, it's all about assets and equipment. It's very important to keep assets up and running because they operate 24/7, 365 days a year. Even an hour of downtime can result in a million-dollar loss.

Therefore, building predictive maintenance algorithms is key. The outcome of these models is predictive maintenance, and the impact is significant. We're not just talking about one asset but a fleet of assets, maybe ten, twelve, fifty, or even a hundred, depending on the organization. This helps the organization plan and schedule maintenance activities, providing tremendous value on top of existing calendar-based or preventive maintenance practices.

AI-based capabilities in Seeq:

We have used Seeq Data Lab for developing AI algorithms. There's no out-of-the-box AI available, and I believe no other similar platform has that either. I've even evaluated TrendMiner, the closest solution I know to Seeq, and even that doesn't have it.

AI is great in Seeq. You have a good feature that allows you to convert a Python algorithm built in Seeq Data Lab into a user-friendly interface, essentially turning it into a vertical application. For the end user, it's just an application; they don't have to worry about the Python code. The workflow and how they've thought through the consumption of Python code is pretty impressive.

I've worked with Seeq, Cognite, Azure, AWS, DataRobot, and many other platforms. With Seeq, I've realized that it's very easy to use. You hardly need any training, maybe three hours is more than sufficient. The best part is that the entire data science workflow is automated in Seeq. You don't even have to be a data scientist; a production engineer can focus on production and still leverage Seeq.

For example, we have a customer, an Oil and Gas company in India, where I've been involved since day one with installation, implementation, and use case development. It's been four or five years now, and every quarter we go on-site to develop new use cases. We started with ten users and now have more than one hundred and fifty, all of whom are very appreciative and are developing use cases on their own.

Compared to any other platform, Seeq is the best and most adaptive. If someone focuses on Seeq, they can very easily get their hands on it and start utilizing it for their daily workflows. I've seen more than ten customers who have completely replaced Excel with Seeq.

Seeq takes care of security very well. Since it has moved to a completely SaaS model, it has to address all cybersecurity points. Seeq adheres to SOC 2 Type II security standards when it comes to the cloud.

Data integration and security:

Regarding data integration, Seeq is built to handle time series data very well. However, that doesn't cover the entire manufacturing analytics journey. There's still a lot of non-time series, unstructured data, which is where Seeq has some limitations. 

But Seeq's philosophy has always been focused on time series data, although customers might compare it to other solutions. So, for data integration, Seeq can do what it does very well, but there are other opportunities for development in this area.

Seeq Organizer, which is used for dashboarding, has some limitations. 

In Seeq Organizer, we've realized that process engineers want dashboards with more drag-and-drop features, like Power BI. Seeq has limitations in terms of the variety of widgets and visualizations you can use. You're limited to a few types like line charts, bar charts, and pie charts (which was introduced recently). Power BI offers a wider range of dashboarding options.

It's not that Seeq is solely a dashboarding tool; it can connect to Power BI and Tableau. But customers who purchase Seeq and expect its dashboarding feature to be competitive with Power BI and Grafana might be disappointed. So, dashboarding is where I see a lot of room for improvement in Seeq.

I have experience with this solution. It has been more than four years. 

Seeq is a US company, and my company is the exclusive partner for India and global implementation partner for Seeq in Southeast Asia. I lead the entire data science group, and we've been using it extensively. 

In fact, we have supported Seeq on various product development projects for more than four years.

With the earlier on-premise version. But since it's now completely SaaS, I hope those issues are resolved. I'm still exploring the SaaS version, as customers in India and Southeast Asia are not always comfortable with cloud and SaaS due to government regulations.

With the on-premise version, we experienced difficulties with reliability and availability for almost a year, possibly due to hardware limitations. Seeq was unavailable multiple times each month. So, we had to restart the service or involve Seeq's system reliability engineers to resolve the issue. However, I don't think that's a challenge with the SaaS version.

I would rate the stability a seven out of ten, with one being the worst and ten being the best. 

Seeq has good scalability, thanks to a feature called asset trees. It's easy to scale up, but that's more of a sales pitch/jargon than reality. Replicating an asset is as simple as clicking a button, but the moment Seeq data models are involved, it becomes a pain because you have to create copies of your Datalab file for each asset.

So, scalability is fifty-fifty. Practical customer expectations involve scaling up data science algorithms, not just data analytics workflows. When we use Python (Seeq Data Lab) and try to scale it within Seeq, we face computational and replication challenges. It's not just clicking a button; there's a lot more effort involved. We initially anticipated a 40% reduction in effort, but that wasn't the case.

But sometimes, we have realized that maybe 100% of the effort is required even if we scale up.

Looking at the entire landscape of similar software solutions available in the market, I'll still rate the scalability of Seeq a nine out of ten. 

The customer service and support are very powerful in terms of response time and knowledge. It's just that with Seeq SaaS and recent organizational restructuring, everything has to go through the Seeq customer support call, which makes it a bit difficult. I only get a response once they attend to my queries. 

Lately, it's been taking one or two days to get support access, whereas earlier it was more transparent, and I could directly reach out to system reliability engineers via email for immediate support within a couple of hours. But now, they are more structured and process-oriented, which could be one of the reasons. So it takes a little bit longer for them to respond.

Positive

With Seeq SaaS, it's a lot easier to setup and deploy. Seeq itself handles setting up the instance, and I just receive the link for the dedicated customer. The only thing I need to worry about is connecting to the historians and data sources. 

Seeq has many connectors, so it's relatively easy, although it can still be cumbersome due to dependencies on the customer side. Compared to other solution providers, Seeq is much easier.

The pricing is average. Seeq has changed its strategy. Most likely, it's based on the number of sites, assets, or tags, and it varies depending on the customer. There's no standard pricing.

I would recommend it to other people. 

My recommendation:

So, typically, when there are historians, the first thing is the limitation on the number of licenses. For example, if I'm a control engineer, I have no visibility of what's happening on the quality side because quality is measured by a different team, and the systems are themselves different.

You have LIMS. Now, a control engineer who is sitting in the control room has no visibility until they get feedback from the quality control group. That feedback usually happens through WhatsApp, phone communication, or physical communication. 

With Seeq, you can monitor and trend different data streams from different sources on a single screen. There is lot of value right here. Even though it can not be quantified in terms of cost savings. This integrated visibility adds significant value for the end consumers operating the plant.  

Data Integration and Cleaning:

Next is the data processing capabilities, like data cleaning. Even if you are a data scientist, you may not be aware of all the algorithms available in the market. When it comes to time series analytics, it’s different. It's no longer just AI and machine learning; you need knowledge of time series data, how sensor data looks, and the applicable algorithms. Seeq offers automated, point-and-click solutions for these workflows. You don’t need to know data science or data preprocessing algorithms. Just click, select the parameter, and you’re done.

Faster Time to Value:

These are a couple of points where I see a lot of value. Customers often try to set up their own digitalization groups and build everything on their own instead of buying Seeq. They might try to develop or reinvent the wheel, which never happens. Everything remains in Python. If that effort is spent on Seeq, they can start developing and realizing value in the first month, not in the span of years and weeks.

Overall rating:

Overall, I would rate it a seven out of ten. And the reason is, Seeq was great five years back when there was no competition and digitalization was just emerging. Now, other companies are developing products like Seeq, and some features could be better and more efficient. 

Seeq needs to stay competitive by understanding customer expectations, which will keep changing. Seeq needs to conduct surveys and incorporate critical features and customer expectations into their product development roadmap.

Public Cloud

My main use case for Seeq is mostly advanced analytics including machine learning and data science to solve industrial problems such as golden batch analysis, soft sensor modeling, hybrid modeling, and condition-based monitoring.

A quick specific example of how I have used Seeq for golden batch analysis is that Seeq has some of the most handy and fantastic features for analytics. They call it Capsules, which is used to create a condition in a matter of a few clicks. You can create conditions as complex as multiple nested loops. The way we use these conditions is to identify the best historical batches, good batches meaning, out of perhaps two to three years of data, and we identify which were the best batches within certain specifications. We average out those batches, create a standard deviation, and we use a reference profile from Seeq. Seeq really comes with out-of-the-box features and functionalities which can be used readily without even knowing about the mathematics behind it, without even doing the coding. You can quickly build your entire workflow for the analytics in a matter of a few hours.

Using Seeq for workflows like identifying the best batches and creating reference profiles really enables decision-making. Now I know for my current batch what the efficiency is in comparison to the best golden batches from history. If there are any deviations in real-time, Seeq throws out an alert or notification which can be utilized for me to know that there has been a point where I need to decide what needs to be done, if I need to make modifications to any of my process parameters or something of that sort.

I think there are plenty of other use cases such as heat exchangers, compressors, and reactor modeling. Using Seeq for a heat exchanger is the most prominent one across chemicals, oil and gas, and pharmaceutical industries. Using physics-based calculations, such as thermodynamics, to estimate the fouling factor. Once you estimate the fouling factor based on the data, you forecast it to the future and identify at which point in time it is going to go below the minimum design value or minimum acceptable value. If you take the delta difference from the future point where it intersects the minimum value and now, that is what the remaining useful life is. It has been very handy when you are dealing with a fleet of heat exchangers. You now know which heat exchanger needs to be attended to at what point in time because you can prioritize the exchanger performance based on the fouling factor looking into the future.

The best features Seeq offers include a module called Workbench, and Workbench comes with abundant tools, pre-configured tools that you as a process engineer—I mean, you do not need to be a data scientist—can readily use for your analytical workflows. You may have some thought process or some modeling approach in your mind which gets started from cleansing of the data to modeling the data all the way to conditions, machine learning modeling, and dashboards. The way Workbench is structured is a very concise way of putting the tools together. As an engineer, I just need to go from top to bottom, and it helps me to build the model. In my daily workflow, the specific tools or steps in Workbench I find most valuable are Conditions and Capsules.

Seeq is doing what they can and they should. I do not have anything more to add about the needed improvements.

I have been using Seeq for seven years.

As a partner, I cannot share the information about whether I have seen a return on investment.

I am satisfied with Seeq. I have looked into many other platforms, and Seeq has been one of the most useful and very easy to adopt and consume by the users, by plant people. Being a data scientist, I can love products which could be more complex, but at the shop floor, the process engineers need something that is useful for them. Even before that, they need something that they can use without solving complex mathematical problems.

Public Cloud

Other