Seeq Reviews

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.

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.

I particularly appreciate the conditional analysis feature, which allows me to analyze how equipment behaves under specific conditions. I can easily check performance metrics, such as when a pump runs above a certain RPM, and view all relevant data on a single screen.

The technical support services need improvement. 

I have been using Seeq for just over a year. I started using it when I transitioned to one of the surveillance tools.

The product is stable. However, we encounter occasional network issues affecting performance. I rate the stability a seven out of ten.

The support team has been quite helpful, although identifying the root cause of issues can sometimes be challenging due to the system's complexity.

The deployment process is straightforward as long as you can access the server from which you will retrieve data. Overall, I find it quite easy to set up.

Seeq is accessible to anyone, although some prior experience in data analysis can be beneficial for more complex tasks.

The predictive analytics features have been instrumental in catching excursions early, allowing us to monitor conditions proactively and prevent issues from escalating.

I recommend the solution, but I advise potential users to consider the limitations of its exporting options.

I rate it a seven out of ten. 

I use the solution for trend visualization and real-time data. We use Seeq to connect various pipelines and create dashboards.

The tool's most valuable feature is connectivity and visualization. Seeq is a very good product for analyzing time series data using its time intelligence and time performance features.

Seeq is easy to begin with. If someone explains it to you shortly and clearly, it is very understandable, and anyone can use it.

I have noticed that Seeq sometimes struggles with connectivity issues, leading to data gaps. This is an area that needs improvement. While connecting to data, sometimes, due to connection loss or issues with the tool itself, the tags may not display any trends, resulting in missing information.

I have been using the tool for one and a half years. 

The tool breaks down at times. 

My company has more than 100 users. 

I haven't contacted the tool's technical support yet. 

My company chose the solution because of its scalable time series data. 

The tool's deployment is easy and is completed in three to four hours. Updates are needed every five months, especially if you want to add new features or specifications.

Based on my experience, I recommend using Seeq, especially if you need to visualize and monitor real-time data. I rate the overall product an eight out of ten. 

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.

It's an analytical tool, so it supports all the analytical problems. That is the reason we chose Seeq. It generally supports whatever you need, and that is it. We didn't face any challenges while integrating it. 

There are multiple valuable features, and similar things can be done with different tools. But we found Seeq to be very useful because it can be done easily and with less time. 

For example, we can do SPC with Power BI or Excel, but with Seeq, it can be done more easily. Most importantly, we can get real-time value, which is important.

The analytical part is fine. We can do our analysis. But the dashboard, where we approve the dashboard, is not ideal for someone who uses Seeq only as a tool to see data in a report format. 

Seeq Workbench, Seeq Organizer, and Seeq Data Lab are great platforms where we do our analysis, but if they could add features to Seeq Organizer for publishing reports, that would be helpful. 

Currently, there are many tools on the market that provide aesthetic dashboards, like Power BI or Tableau. These tools make it easy to create reports that are understandable even for those who don't know anything about the data. So, Seeq could work on the reporting format, but the analytical tool is excellent.

I have been using it for two years. 

I haven't found a lot of bugs, but sometimes it crashes/lags. Very rarely, though. It might be an issue with your internet or CPU load. I don't have that much experience with bugs in Seeq.

The usage depends on different projects and plans. There are more than 10 or 20 people are using it.

The customer service and support are good. They reply within 24 hours. 

We can raise a ticket and ask for help. They will help you. I'm happy with the support. 

Positive

The initial setup and installation of Seeq are very easy because they also help you with the installation. 

I have done a couple of installations and upgrades, and it's not that hard. It's very easy and not time-consuming. 

The first thing is to figure out where your data is coming from, whether it's Snowflake or another source. Then, the integration is completely easy.

My advice/recommendation depends on the kind of problem the user wants to solve. If the problem is to find a reporting tool, there are a lot of tools on the market. It completely depends on the problem the user is going to solve. 

I cannot simply suggest if you should use Seeq or not. First, you have to know what you are using it for and why you are using it.

I would recommend it as an advanced analytics tool for processing different manufacturing data. It's easy to learn to use Seeq, but you need to have some knowledge of analytics or process engineering. Otherwise, with anything you learn, you have to give some time to the software.

Overall, I would rate the solution a seven out of ten. It's a great platform, a very powerful platform to work with. We can add a lot of machine learning stuff and customize the data in Seeq. 

But they need to improve in a few areas, like the reporting part and customizing the dashboard. Seeq can improve those in future upgrades. Currently, there are a lot of powerful tools on the market similar to Seeq.

A use case for Seeq is performance analysis of assets like fans, turbines, and blowers. I have used Seeq for analyzing the performance of turbines or blowers by using the performance indicators and creating those from raw data using Seeq formulas. I then created a dashboard so that we could track asset performance and used it as a dashboard for asset performance management.

The best feature Seeq offers is the ability to compare multiple things together on the same frame, in the same plane of analysis, and in the same view. It provides very good control on the capsules, which are patches representing the anomaly or representing the points of interest, and it has very good control for them. It has very good scalability, meaning if I am making a template for one asset and an industry has multiple fans, multiple blowers, or rotary equipment of the same nature, we can create a dashboard and swap assets, so scaling up Seeq and scaling of analysis is perfect. It provides the feature engineering concept as well through formulas, allowing us to get a lot of things very quickly in Seeq.

Seeq has provided visibility to the key performance indicators and visualization of those critical pointers in our dashboards. It is very good at filtering and sorting the times of interest because it mostly deals with time series analysis, which I found is not present in other data visualization packages available in the market, including Tableau and Power BI. Seeq is very good in industrial data evaluation.

Seeq has provided on-time visualization or real-time monitoring of the anomalies or the deviation, process deviation, and quality deviation, which has definitely initiated timely action to take proactive measures for avoiding the breakdowns and also keeping the quality within the acceptable range within the limiters.

I could see a lot of things can be added, particularly the ease of working in other packages and other process visualization packages like Power BI or Tableau. That ease is not present here in Seeq. I would like to suggest that the data flow within Seeq is actually a bit slower than compared to Tableau Live or other packages, and I think this is an area where Seeq has to get improved.

The ease of working is an issue, as the user interface of Seeq looks a little bit more professional with an industrial look, and it can be made a bit more user-friendly if we compare it to the reliability of other packages like Tableau. I have worked with Tableau for data analysis, and it is also a very good data analysis package, but as a new person, the tools are visible and available when I just want to visualize something. That is not the case with Seeq; Seeq is purely industrial. Being an engineer, I know what to use and how to add things, but if other visualizations or features can be made available in Seeq, it could handle other demographic data or transactional data along with the time series data, which I have not used so far, and I think it is not available with ease compared to other visualization tools in the market.

Seeq is mostly designed with the background of thinking that it is being used by high professional process engineers, statisticians, and others, and the training is not so easy from the user perspective. The formula part is a bit confusing, and sometimes even I face some problems. I am very proficient in Microsoft Excel and using the formula packages in Power BI and other tools, but in Seeq, I find that the things are not exactly working all the time. Sometimes it is a reliability issue, or sometimes it is a bit over-complex, which can be improved in terms of applying and using the formulas so that it supports feature engineering.

Seeq can better be used for identification of anomalies or monitoring the process deviation or quality deviation in real time, and it can also be used as a very good professional data-driven dashboard for creating data-driven dashboards and doing in-depth analysis using extensions such as Python. I have not used the Python or Pandas integrated with Seeq, but I have used Seeq Workbench, which is useful in dashboarding.

Adding users in Seeq administration settings was not difficult or hard, although I have not worked as an administrator. As a user, the usage of Seeq for process analysis is straightforward and is something people are expecting. Seeq provides very good process analysis by separating the tools like the identification tool, quantification tool, filtration tool, and cleaning tool. I think it is good and easy for understanding if a person has a mathematical, analytical, or statistical mindset.

I would advise anyone to give Seeq a try because it has a yearly licensing structure, and its ownership cost is not so high. The only thing which makes any analytical tool, including Seeq, less productive in industry is the context of analysis missing in a number of industries. Even at this time, skepticism and a lot of things make it difficult. I would recommend Seeq for any manufacturing industry to have it for real-time analysis inside their plant.

Regarding Seeq, I definitely caught a very good point that is missing: it should have a training version so that people who are not aware of Seeq can get familiar with it. If it does not win much market share, I think a version for new process engineers or new process analysts is needed. If there is a version which may be available, I am not aware of it, but as far as I know, it is not available in the public domain for trial purposes. If that can be added, I think it will create better familiarity with people, and that familiarity can create market share. My overall rating of Seeq is seven out of ten.

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.

The best features of Seeq in my experience include the model that allows you to see the analytics results in real-time, which is the best feature of Seeq for me.

My work with SCADA and PLC in companies requires analyzing the data model quickly. For that reason, I use an API to return the results, and the real-time capability helps make decision-making faster and more accurate.

Another feature of Seeq is that it not only uses time series data but also can work with relational databases and even offline data. Seeq positively impacted my organization by showing the future of many real-time variables of the plant, allowing us to prevent any issues, which is perfect for me since I work in maintenance.

I would like to test the software for free for a longer time. I believe Seeq should offer an on-premise server option. I prefer on-premise deployment because for many companies, data is a valuable asset.

I have worked for approximately eight years, especially using the AVEVA PI System.

Seeq is stable in my experience.

I am unfamiliar with Seeq's customer support because I only tried to test the software and did not use the support. I cannot rate customer support on a scale of one to ten as I have not used the technical support.

I previously tested AVEVA Analytics for predictive analytics before trying Seeq. I evaluated SAS Viya on-premise alongside other options before choosing Seeq.

The return on investment is complicated to measure, particularly in creating the use case. However, using Seeq makes the effort much easier, and in my opinion, the return on investment is not so far off.

Regarding pricing, I would like to start using Seeq with flexible costs since when you work in analytics, you never fully utilize the maximum potential, and it is possible to gradually increase costs.

When I evaluated the technology, Seeq was my first option.

I chose a rating of ten because I evaluated many characteristics, including its ability to integrate various data structures. The code flexibility impressed me because you can use it more easily within Seeq. I actually use Seeq on-premise, but I know that it does not currently offer on-premise solutions, and I would like to use it in production.

I do not remember how much money I have saved, but I actually use the AVEVA PI System. In that software, I use around one hundred devices, such as motors, and applying Seeq makes integration perfect since I can use just a template to analyze one hundred devices. I recommend Seeq to others looking into using it because it is perfect. Seeq is perfect for me.

My overall review rating for Seeq is ten out of ten.