Global Product Manager at a pharma/biotech company with 501-1,000 employees
Real User
Top 20
May 4, 2026
My main use case for Seeq is process monitoring, which includes easy visualization of time series data, low to no-code custom functionality and scripting capabilities, and automated data extraction pipelines. These features are useful for process manufacturing and batch or experimental monitoring.
My main use case for Seeq is overall equipment effectiveness and process analytics. When looking at a line that is being historized, I leverage the data from the data historian in Seeq to create a root cause downtime. For example, with three different lines, each line had specific alarm codes that could potentially reference a machine upstream or downstream. By coalescing the different alarm categories, I could pinpoint if the alarm was triggered from an upstream event and therefore causing a bottleneck or if it was a blocked or starved condition because of a different machine failure that was causing the stoppage. This is an example of leveraging Seeq in a way that is difficult without just leveraging the raw data. I have also used it for process monitoring and reporting on batches and average conditions within those batches, which saves an engineer's time to consolidate and compile data. Instead, they can leverage Seeq and have it automatically compile those reports in a very streamlined way with clear KPIs and outcomes from those batches without the need to dig in and recreate those reports every time in PowerPoint. There was one use case where the average alarms over an hour running calculation was provided, and if that running calculation ever triggered above a certain percentage, say 5%, then an alarm could be triggered and a notification could be triggered that indicated the issue was outside the normal bounds. This allowed the operators and engineering staff to take action and actually address what those issues were that were a little bit in the noise of just the standard number of alarms over a certain period of time. This resulted in a net savings of approximately one million dollars of extra product per year.
Machinery Engineer Intern at a university with 10,001+ employees
Real User
Top 10
May 4, 2026
I used Seeq to develop a thermodynamic analysis on the performance of a centrifugal compressor in the Baton Rouge refinery for ExxonMobil.I used Seeq to track the data that was collected by the transmitters throughout the facility, and then perform quick and easy calculations in order to calculate volumetric flow and track the pressure drop and other such parameters.
ENGINEER at a computer software company with 201-500 employees
Real User
Top 10
Apr 30, 2026
I am a data architect focused on serial times data, specifically the extract to data of OT to IT. The main use case for Seeq is to evaluate many technologies such as Seeq, Databricks, and SAS Viya. The project and use case were about energy optimization in Peru related to a company called COES, focusing on maximum demand and saving energy for the company. I proposed to use Seeq because it is easily connected to the AVEVA PI System due to its time series data, making it easier and more flexible to see the data workflow. I have another case where I analyze thermographical images of some energy devices. However, I am unsure if Seeq is the perfect software for image processing, so I tried using YOLO, and then the results are input to Seeq.
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.
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.
Lead Engineer at a healthcare company with 10,001+ employees
Real User
Top 10
Jul 31, 2024
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.
I primarily use Seeq to monitor various process tags and notify me when they are outside the expected parameters. The software allows me to analyze substantial amounts of data, which I find quite interesting and valuable.
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.
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.
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.
I first used Seeq for a customer who was starting up their facility. We used it to diagnose problems such as water utilization and tuning control loops, along with various other process issues.
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.
Supply Chain Analyst at a manufacturing company with 10,001+ employees
Real User
Top 20
May 21, 2024
I generally use it for time series data analysis. Seeq is famous for doing time series analysis, like statistical process control, quality control, and different types of analyses like regression analysis or correlation relationships. All these different kinds of analysis can be done in Seeq, which is very efficient.
Seeq is a powerful platform designed to enhance the connectivity and visualization of industrial data, facilitating seamless workflows and intuitive user interactions. It excels in handling large datasets while enabling predictive models and real-time monitoring without requiring extensive coding skills.Seeq's ease of use and user-friendly features make it a go-to choice for professionals seeking effective time series data analysis. It supports a wide array of industries including oil and gas...
My main use case for Seeq is process monitoring, which includes easy visualization of time series data, low to no-code custom functionality and scripting capabilities, and automated data extraction pipelines. These features are useful for process manufacturing and batch or experimental monitoring.
My main use case for Seeq is overall equipment effectiveness and process analytics. When looking at a line that is being historized, I leverage the data from the data historian in Seeq to create a root cause downtime. For example, with three different lines, each line had specific alarm codes that could potentially reference a machine upstream or downstream. By coalescing the different alarm categories, I could pinpoint if the alarm was triggered from an upstream event and therefore causing a bottleneck or if it was a blocked or starved condition because of a different machine failure that was causing the stoppage. This is an example of leveraging Seeq in a way that is difficult without just leveraging the raw data. I have also used it for process monitoring and reporting on batches and average conditions within those batches, which saves an engineer's time to consolidate and compile data. Instead, they can leverage Seeq and have it automatically compile those reports in a very streamlined way with clear KPIs and outcomes from those batches without the need to dig in and recreate those reports every time in PowerPoint. There was one use case where the average alarms over an hour running calculation was provided, and if that running calculation ever triggered above a certain percentage, say 5%, then an alarm could be triggered and a notification could be triggered that indicated the issue was outside the normal bounds. This allowed the operators and engineering staff to take action and actually address what those issues were that were a little bit in the noise of just the standard number of alarms over a certain period of time. This resulted in a net savings of approximately one million dollars of extra product per year.
I used Seeq to develop a thermodynamic analysis on the performance of a centrifugal compressor in the Baton Rouge refinery for ExxonMobil.I used Seeq to track the data that was collected by the transmitters throughout the facility, and then perform quick and easy calculations in order to calculate volumetric flow and track the pressure drop and other such parameters.
I am a data architect focused on serial times data, specifically the extract to data of OT to IT. The main use case for Seeq is to evaluate many technologies such as Seeq, Databricks, and SAS Viya. The project and use case were about energy optimization in Peru related to a company called COES, focusing on maximum demand and saving energy for the company. I proposed to use Seeq because it is easily connected to the AVEVA PI System due to its time series data, making it easier and more flexible to see the data workflow. I have another case where I analyze thermographical images of some energy devices. However, I am unsure if Seeq is the perfect software for image processing, so I tried using YOLO, and then the results are input to Seeq.
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.
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 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.
I primarily use Seeq to monitor various process tags and notify me when they are outside the expected parameters. The software allows me to analyze substantial amounts of data, which I find quite interesting and valuable.
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.
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.
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.
I first used Seeq for a customer who was starting up their facility. We used it to diagnose problems such as water utilization and tuning control loops, along with various other process issues.
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.
I generally use it for time series data analysis. Seeq is famous for doing time series analysis, like statistical process control, quality control, and different types of analyses like regression analysis or correlation relationships. All these different kinds of analysis can be done in Seeq, which is very efficient.
I use the solution for trend visualization and real-time data. We use Seeq to connect various pipelines and create dashboards.