Seeq Reviews

My main use case for Seeq is process optimization, any coding tool for Python, and any work that is related to time series data. We upload the data into Seeq and do the machine learning, visualization, and many other analytical tasks.

I have more to add about my use case for Seeq because it is consolidated to one place. It provides visualization, machine learning tools, any places where we can call the API to pull the data, and a proper way that we can integrate everything in just one place and use it easily.

When I mention the drag and drop and right-click features for visualizing and analyzing large sets of time series data, I see that it has changed my workflow significantly. Before the age of AI and LLM models, this was the very first tool that introduced optimization work to non-IT engineers who had not been able to code it, bringing them into a more optimization environment. Currently, with the age of generative AI and LLM world, we can accomplish similar tasks with other tools as well, but Seeq remains the core foundation for us. I would say it serves as a complement to other tools. We just need to supplement other data into Seeq and we can leverage it anyway.

In my workflow, Seeq integrates with other tools or systems as we try to incorporate more of Seeq into our daily operation, meaning the operational decision-making.

When we try to optimize or make any adjustment to see the equipment performance, we use Seeq extensively, and then reliability comes into play. So, we need to maintain the reliability of Seeq server much more because we make it up to the same level as our control panel and control system. I would say Seeq is coming to integrate much more and has more influence on our operational decision.

Seeq helps support collaboration among my team or across departments; I appreciate their sharing function and their journal, which allows us to record every comment, every action, and every analytics step that we did. When we share it with our partners and colleagues, they can view those journals and understand how we arrived at those formulas, how we built up the analytic dashboard, and how we came up with operational conclusions. I would say Seeq does a great job on this.

Seeq helps my team make faster and better decisions; for example, in an exchanger optimization problem, when we need to determine the time to clean the exchanger or shut down the plant for cleaning, Seeq is able to build the visualization dashboard to monitor that very quickly. I would say it takes less than an hour to do that. It is a tool for manipulating, analyzing time series data, and visualizing it up to an endpoint, which is the optimization problem to make a business decision. For anything about time series, I think of Seeq as the first choice.

In my opinion, the best feature Seeq offers is the ability to visualize time series data in an efficient way. If we code it by ourselves, it will take a very long time or require a lot of server capacity, and you cannot simply plot a time series of, say, 100,000 records on your own simply because it takes a lot of effort to do that. Analyzing it when we apply any formula or algorithm takes more time to finalize everything. Seeq does this through drag and drop and point and click actions. So, it is much easier to do it by using this tool.

Seeq has positively impacted my organization because I see many people using it, compared to the past five years when we had only PI Vision for visualizing time series data. Manipulating time series data was such a critical task that not many people were familiar with and were afraid to do. This task remains a core critical function for everyone to do it efficiently in order to complete anything. Process optimization and reliability analysis would address all failure modes.

I appreciate the question on how Seeq can be improved. The first thing is their graphical user interface. They invest so much in their backbone, but without a good dashboard or visualization tool, it feels insufficient. They focus too much on technical aspects. Many managers and people just want a simple dashboard that can show everyone in the control room or whatever places that when we finalize the tool, these are the final product. Seeq did not do it as beautifully compared to other applications.

I really want Seeq to improve their graphical user interface to be more user-friendly.

I have been using Seeq for about five years.

In my experience, Seeq is stable on production servers. Recently, we tested a new function on a test server that is still in the development phase, and while I can understand that, I expect a bit more. Testing should be reasonable to production as much as possible, but it is treated differently and lags a lot.

Seeq's scalability is really great. Any similar assets or problems can be copied and pasted from one business unit to another, applying them to another business unit with the same function, just changing the tags.

The customer support from Seeq is very great. They have a ticketing system and on-site support engineers located almost everywhere around the world. I am located in Thailand, and they have a Malaysian support engineer dedicated to my project, who gave me his WhatsApp number so I can reach him whenever I need.

I previously used PI Vision, Excel, or PI add-on tools with Excel. I used Python and R before I met Seeq. Seeq handles all of that in one place, providing every tool that I need. That is the main reason we use Seeq, and I love it a lot.

I have seen a return on investment as I saved a lot of millions of dollars for my business unit since we started using Seeq. Many business decisions, operational troubleshooting, and optimization have been accomplished with Seeq. My organization is small, and while we have improved our efficiency, we have rarely had to lay off employees because of this improvement. I would say there are some recognized results about time saving in our team, but they are not quantifiable numbers.

Before choosing Seeq, I evaluated other options like Power BI. When discussing dashboards, people think about BI solutions like Tableau or Power BI or software like RStudio, which are usually used for data analytics work. However, comparing them with time series data, nothing beats Seeq. Those software handle relational data relatively easily, but the volume is small compared to Seeq, which specializes in time series.

Seeq handles large volumes of data or high data velocity very well. I used to use Python before I started visualizing my time series data, and even with just not many variables, about 10 to 12, my computer worked very slowly compared to Seeq. I think they have a special algorithm to process and select the appropriate data points to visualize and process high volumes. It requires a lot of servers and special tools; it is not just a simple Excel file typically used in our learning. Seeq handles a large volume, and time series data is not simple data; it contains every detail and requires specialized software to manage that.

My advice for others looking into using Seeq is that if you are looking for a finished product for time series, Seeq is a good one that you should try. It is easy to build, and if your team members or engineers are not familiar with coding, Seeq is a good tool to start. You can do machine learning and advanced analysis that you cannot perform simply because you do not know how to code. You can just try, click, drag and drop and see the results, and you will be obsessed with it once you see the results. I would give this product a rating of eight out of ten.

On-premises

Other

My main use case for Seeq was optimizing a cycle time for a centrifuge on starch optimization. At my previous company, we were able to save up to a million dollars per year by applying that technology. On a daily basis, I usually use Seeq for quick reviews about production speeds and dashboards that we made inside Seeq. In the long term, it could be used for strategic decisions and artificial intelligence projects.

I use heat maps on a daily basis. I used to work as a control system engineer, so I defined heat maps and went through the shop floor. Those heat maps were on the maintenance and reliability side. At the reliability room, I had an analyst who goes through all of those screens and could see regionally which site has the most necessity to go through the shop floor and understand how to make those equipments better. Those heat maps helped me take decisions on budgeting for maintenance at each site and also decisions about how I could handle the automation engineers and reliability engineers, and what the amount of those engineers that I need per site.

The best features that Seeq offers are the possibility to go through tags and compare those tags and equipments with each other in different spaces. I was able to make a heat map dividing each part of a plant by equipments and by the most used equipments. With that heat map and those visions inside Seeq, I could compare which factory regionally had the least available time and operational available time, and also compare different types of production sites. Going through those tags, I could understand how to compare the best operational cycle time and the worst operational cycle time. The best feature that Seeq offers is that possibility of going through different sites and comparing them.

The algorithm features could be better. I know that is something really in early stages and we have to be careful about it, but adding some new AI insights and making it possible to go through the algorithms and optimizing them myself could be a good approach.

Support is excellent, and the user interface is friendly. If you are a developer, you might want to go further and not have it be a black box, but adding some scopes for Python development could be beneficial. I know they have some add-ons that do so, but the main idea is keeping it simple for the main people, which is awesome.

I have been using Seeq since 2023, so you can count for three years.

Seeq is stable. There is no downtime or crashes. The only consideration is the normal time to go through a large amount of data, but I have no concerns about it.

Scalability is really great because my company has up to 20,000 people using Seeq, and we do not see instability. Seeq is quite great on customer support, so there is nothing to complain about.

Support is excellent, and the user interface is friendly. If you are a developer, you might want to go further and not have it be a black box, but adding some scopes for Python development could be beneficial. I know they have some add-ons that do so, but the main idea is keeping it simple for the main people, which is awesome. I would rate the customer support a 10.

I used to work with Pi Vision and Pi ProcessBook, and we switched because the time to open the dashboards was really high.

Switching makes collaboration better because we were used to using Pi Vision and Pi tags, but with Seeq, you do not have to go through some configurations that were a pain. Seeq is something that you do not have to download as a separate application. You just go online and are ready to go. This makes the collaboration quite better.

It was quite easy for my team to adopt Seeq because it is a smart manufacturing team. However, the process engineers found it a little bit more difficult to adopt Seeq.

Seeq integrates pretty well. There were no difficulties or seamless experience issues. Seeq really integrates well through all of those data sources.

Seeq is inside our IT structure, so I have no concerns.

I saw a return of $10 million per year. Summing up all the projects that we have implemented during the year, we could see that we have $10 million in savings.

For smart manufacturing applications only, we were able to save up to $10 million a year. That is quite a good number.

The price is right on the spot. I do not see any problem with it.

I did not evaluate other options.

The savings were mainly through process optimization and reduced downtime, but we also have some throughput improvements regarding process optimization. Centrifuge cycle time reduction was achieved, and we also gained better efficiency in chemical reactions because we were able to compare the lead time that we have for each reaction, separating them into tanks. Comparing plants performance across the regions was also something that made us realize those savings.

You have to have a good alignment with leadership and senior leadership because Seeq has to have a good sponsor for it. If you do not have the right sponsor, such as a VP or someone at that level who will push Seeq to be used on a daily basis at the plants, it will not be used because people are used to using Excel and other tools they are accustomed to. The main thing is you have to go through a transformational change in order to have them working on Seeq on a daily basis, so it is really important to have an advocate or a sponsor that goes through those lower positions and convince them of using Seeq.

Although Seeq is an awesome tool, maybe we could have better customization on the Python side, but I understand it is not the main approach for it. It must keep strict for the whole and not just for developers or someone who wants to go further. I would rate Seeq an eight out of ten.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for Seeq was to see the levels of different pieces of equipment and to combine all the levels together. We put different pieces of equipment together using Seeq so that we could have it all on one sheet instead of having to look it up separately.

A specific example of when I used Seeq to combine equipment data was when we were tasked with making a big layout, an overall plant layout of all the different pieces of equipment. We used Seeq to get the levels of all the tanks and all the different things within the whole plant to put it on one sheet.

The best features Seeq offers include showing you a graph of the tank and the different equipment levels, which gives you a good visual.

What stood out to me about the visuals was the ability to compare things. During my internship, there was a point where one of the tanks had started leaking and had to be shut down. I could see the past tank levels and then where it was during the shutdown, and then how it ended up going back to normal levels after they fixed the leak. I appreciated being able to compare old levels to new levels.

Seeq has impacted our organization positively because now with Seeq training, we are able to put it out to more people. Whenever we were interning, only some people were proficient in Seeq, so this allowed more people to learn and utilize Seeq.

I would say the improvement that we made was doing Seeq overview, or the plant overview, using Seeq. I feel Seeq is more updated and newer technology compared to what they were using before, so we were able to do that.

I would say Seeq can be improved by including a feature where instead of just having the tag name, it is actually spelled out because it was difficult whenever we were trying to find certain things. Or if there are ten different tanks, have it so you can tell the difference between them because there were some pieces of equipment where we were not sure if we were pulling the right tags. Maybe having something like that would help. I am pretty sure it is possible, but that was a problem we were having.

I have been using Seeq for three months during the duration of my internship.

I would rate Seeq a seven on a scale of one to ten.

I chose a seven because I feel Seeq is a good program, it was very easy to use, and I just liked it overall, so I feel a seven is a good rating.

My advice to others looking into using Seeq is to watch the videos because they are very helpful to get to know what the different tools are and how to use it to its fullest capacity. My overall review rating for Seeq is seven out of ten.

My main use case for Seeq is incident investigations. Upon unexpected quality results, incident investigations can start to determine where the incident first began occurring. Seeq allows the retroactive examination of many different ingredient load cells as well as equipment settings to help isolate the root cause.

Seeq has positively impacted my organization, and I can share a specific outcome where we have found a new metric on an important piece of equipment to raise the flag when it is underperforming. This prevents large production issues from occurring when we're able to catch the underperformance through the Seeq alert.

The new metric was discovered from a production issue, and investigation using Seeq allowed us to identify this key metric and start monitoring it. It has improved our quality consistency since implementation.

The best features Seeq offers include email alerting, which is extremely helpful. We are able to monitor specific critical metrics in our process and alert the team when one falls out of bounds. This prevents larger issues during production.

The Seeq alerting integrates very well into our workflow, into shared messaging accounts as well as individuals' emails.

I believe Seeq can be improved because report rollouts on similar pieces of equipment can be challenging. If Seeq had an easier way to modify our tag structure, then duplicating reports onto similar equipment throughout our facility would be easier. Improvements around user interface or other functionalities would also be beneficial.

I have been using Seeq for four years.

Seeq is stable for me, and I have no issues with reliability.

Seeq's scalability is functional enough, but if it was easier to duplicate reports on similar tree structures, it would be even more scalable.

Customer support for Seeq has been great, and I have had positive interactions with their support team.

I have seen a return on investment from using Seeq because we are catching production issues in a faster manner than we had before Seeq.

My advice to others looking into using Seeq is to pursue it. There are more opportunities with your own equipment than you know without examining them through the lens of Seeq. I would rate this product a 9 out of 10.

When we started using Seeq, we began with trend monitoring and performance monitoring of compressors, pumps, and turbines. As we grew and Seeq evolved, we transitioned to heat map and detective analysis, and I continue to use these capabilities in my current role.

With compressor curves mapped into Seeq, we obtain data to observe how the compressor behaves, including head performance, efficiency, and current operation status. This provides live monitoring for individual compressors. For axial compressors and small pumps, we monitor performance metrics including head measurements. Heat mapping utilizes the tree map function where we establish variables with set limits, and whenever a variable exceeds the particular limit, we receive color-coded alarms to initiate analysis of equipment issues.

Since recently starting to use Seeq, I appreciate the email notification feature enabled for anomaly detection. Whenever a parameter or variable breaches the limit we establish, it triggers an email notification, which is quite helpful as we can begin analyzing the event and improve reliability by avoiding unplanned downtime.

Seeq's best features are the formula section, as we can equip it with any kind of formula, and the Data Lab where we can write Python scripts and accomplish many tasks. These are the two major features I value most.

The detective dashboard we have built for our day-to-day equipment monitoring is constructed through Data Labs, and with that capability, it is relatively easy to analyze issues. As I mentioned regarding compressor performance, we can write complex formulas in Seeq, which is a significant advantage over other tools.

With the detective dashboards, we have improved our reliability, and the same workflow was conducted in my previous organizations where we diagnosed several issues and averted downtime.

I would appreciate more detailed training beyond what we received during Seeq bootcamp, which is foundational. Although different levels exist, they are not particularly helpful. Additionally, we can improve AI usage in several cases where we are currently using AI.

For improvement, I believe we need to involve AI much more, and there should be detailed technical bootcamp sessions to help new and emerging engineers learn more about Seeq.

I have been using Seeq for the past six or seven years.

Seeq is stable.

Seeq's scalability is good.

I genuinely appreciate Seeq's customer support. I have been stuck on one or two issues, but they were quick to resolve them, and we were able to return to work.

Previously, we used PI Vision and XHQ, which were manual software without functionalities such as formulas and data labs that are available in Seeq. This is why we switched to Seeq.

I do not focus on pricing, setup cost, and licensing in my current position. However, in my previous organization, there was significant discussion around costing. They recently changed their charging method, as many users were deselected due to pricing constraints that were costly.

I cannot provide much detail on this at this time as I am relatively new to my current organization. In my previous organization, I believe the return on investment was well above fifteen to twenty percent increment in equipment availability and uptime.

Seeq is much better than PI Vision. Prior to switching, the same amount of work that takes three to four hours on Seeq would have taken a day or two on PI Vision. This represents significant manpower reduction, with one person able to oversee fifty to sixty assets at once, compared to needing four or five people for the same job with other tools.

I have observed considerable improvement and quality in the interface, but I do not have specific feedback regarding user interface, integrations, or support.

If you have a Seeq license, utilize it to its fullest potential because it offers immense possibilities and enables access to entirely new capabilities.

Seeq is a strong tool for day-to-day use. I rated this review ten out of ten.

Private Cloud

Other

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.

Seeq was very easy to use and very user-friendly, and the manual that was provided online helped quite a bit to understand the fundamentals, at least the basics that I needed in order to use Seeq for my project. The interface made Seeq most user-friendly, but it was also quite high level, so there was no background code that I had to do. Very basic Python knowledge from previous years helped with the idea, but the interface was the most user-friendly.

Seeq made it very easy to navigate to different codes for the transmitters, thereby making it very easy to develop the models that I needed for the analysis on the compressor for my project.

Seeq only needed me to complete the task, so it saved other people's time, and it saved my time by being easy to use and thereby also saving money. This project was not very complicated, but it did have to rely on various different transmitters throughout the facility, so it was pulling quite a bit of data from different areas over a long period of time. Although it was pulling in quite a bit of data, it still worked quite efficiently most of the time, aside from when it slowed down randomly.

Sometimes the software would be a bit slow to respond, and that may be because there were too many users at once. I am not sure exactly what the reason for that could be, but if that was on the end of Seeq and not on the internet connection, which seemed to be quite reliable, then that would be my only complaint.

I only used Seeq during that internship, so for a total of about ten weeks.

Seeq was quite stable when I used it.

I did not use the customer support, but I did use online manuals, and those were very helpful.

I did not evaluate other options before choosing Seeq.

I do not have anything else to add about my main use case with Seeq.

I did not dive deep enough into Seeq to determine the different facets which can be improved, so I will refrain from saying anything that could be incorrect.

Seeq only needed me to complete the task, so it saved other people's time, and it saved my time by being easy to use and thereby also saving money.

I do not believe there are any improvements needed for Seeq that I have not mentioned yet.

On a scale of one to ten, I would rate Seeq an eight. I chose an eight because it allowed me to effectively and efficiently complete my project, and it was quite easy to use, which is all that you can really ask for regarding new software.

My advice would be to look up online any questions you might have about Seeq, and it should be quite easy to find the answers, and a lot of it is quite intuitive. I would rate Seeq an eight overall.

On-premises

Other

As a Process Engineer, my main use case for Seeq is to treat the data from the PI ProcessBook, the signals, in order to do some diagnostic on the problems on the process.

For example, if I have a problem in the temperature and I want to know at a specific moment how the temperature has been reacting, I combine that with the positions and directions of the thermocouples and verify if the burners are working well at the same moment. This allows me to set certain conditions to find the moment that is problematic.

That's principally how I use Seeq, but I also use it to create notifications, such as email notifications, so I am able to know what is going on under specific conditions. This helps us react faster when problems arise.

I think Seeq's best features are that once you know how to use it, it can do pretty deep analysis. The data treatment helps you identify problems, which is very hard to do in traditional ways such as Excel. The things you build are usually easy to modify, and there are features such as notifications, dashboards, and tree maps that help you visualize your data in different forms.

As a Process Engineer, I find the deep data analysis to be the most useful feature because we often try to identify what the regular pattern is, what the problematic patterns are, and what is going on at the same time with which equipment is not running well. With Seeq, we can analyze multiple data at the same time and supervise those conditions.

Of course, when data is processed correctly, all three outcomes—saving time, reducing downtime, and improving efficiency—are achievable. We save time because many datasets are periodic, so we can apply the same conditions once and treat data in the same way for the future and the past. We reduce downtime when we can identify the real problems with equipment, which allows us to implement new measurements quickly. Ultimately, we improve efficiency as well.

I believe that with the traditional way of processing data, which is Excel, many people ignore the power of the data. Most people tend to look back only at the last two or three days of data and then try to take action. However, with Seeq, we can process data over much larger ranges, such as six months, one year, or two years, very quickly. This helps us understand trends and identify which parameters have the most impact on a process very fast and deeply.

I think the new feature that Seeq offers, which is their AI assistant, is pretty interesting, but I find that sometimes the results provided are not exactly what I'm asking for. I feel there is still a lot of manual adjustment needed with Seeq. In the future, if a platform such as this had AI integrated in such a way that I could just show what I want, even with a picture, and have everything ready, that would be even cooler. However, I haven't tried this feature in Seeq and I don't think they have it yet. That's something I would suggest Seeq improve.

I think it would be beneficial if our IT department could give more permissions and link more data to Seeq so we wouldn't have to rely on other platforms to manage information. It would be beneficial to treat all data on the same platform more smoothly. While Seeq is powerful with signal data, it is not very powerful with data that exists in Excel sheets.

I find Seeq useful, and there are many new features I haven't explored yet. I believe they are continuously building more features, so I am confident that one day the platform will be even more powerful in data analysis. However, the downsides are that it still requires a certain level of expertise to use the tools effectively, and it seems to be built for someone more devoted to the applications. I believe it can take some time for a regular process engineer to really get hands-on with it. There are a lot of features I haven't explored, and I feel I would benefit from guidance on those things, as it can be a bit hard to navigate alone.

I have been using Seeq for around two to three years.

I haven't tried any other platform, so I can't really make a comparison, but I rate Seeq at seven point five to eight. So, eight.

I haven't tried any other product, so I can't really make recommendations. However, if your organization decides to go with Seeq, my advice would be to try their AI assistant, as it is pretty interesting and could save you a lot of time.

I rate Seeq an eight out of ten overall.

My main use case for Seeq is data analytics, which I typically use day-to-day.

A quick specific example of how I use Seeq for data analytics in my work is building prediction models for quality parameters that are measured in the lab.

I typically use the Python scripting environment to automate the development of prediction models, which is the most common use case for the Data Lab add-ons we have developed.

Seeq helps me with building those prediction models by making the process much faster, and its pre-processing capabilities are exactly what I need, making it a comprehensive platform for doing everything I need to do in data analytics.

I find Seeq very helpful for identifying problems and being able to zero in on the cause and effect in my analytics process.

The best features Seeq offers include unique capabilities such as the Python scripting environment that allows me to automate a lot of the tasks and makes it much quicker to get the work done.

Seeq has positively impacted my organization by providing us with certifications, giving us the credentials to say that we are experts in using Seeq, specifically in the paper industry, though frankly, it applies to any industry. My ability as a system integrator to market my capabilities is important.

It is difficult to see how Seeq could be improved; I think you could always use more help on the technical side, but I feel it is the best platform in the industry.

I have been using Seeq for about five years.

In my experience, Seeq is generally very stable; I am aware that there have been times with some downtime, but it is very reliable.

I find Seeq as scalable as any application I know of in the industry, handling larger projects or bigger data sets well.

I have had a lot of experience with Seeq's customer support over the years. It has changed, as there used to be partners with dedicated application engineers, which is no longer supported, and the office hours are no longer comprehensive as they once were.

I have seen other data analytics tools and applications before partnering with Seeq, notably those built into distributed control or SCADA systems from Rockwell, Honeywell, or Emerson, but I believe Seeq stands out as a superior platform.

Regarding pricing, setup cost, and licensing, we have not purchased a license since we are a system integrator and a partner. In terms of cost for our customer base, I believe it is competitive with what they are looking at from other offerings in the industry.

My advice for others looking into using Seeq is to fiercely consider it any time they are looking at data analytics; if you are not including Seeq as one of the potential solutions, you are likely to make a bad decision. Always include Seeq as one of your options. I would rate this review ten out of ten.

Public Cloud

Other

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

Other

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.

The best features Seeq offers include a very quick and easy to learn UI, understanding where your data is coming from, performing analytics on that data set, and then being able to have a history of all the calculations that have been applied to a specific data set to collaborate with other engineers. Instead of using something like Excel where you get siloed into the Excel workbook and then you could never explain all the functions without heavy knowledge transfer.

Getting to the data sets using asset creation and asset groups allows for easy build out and then leveraging the Data Lab tool for more automated build outs using Python scripting for things that might be manual. Users can start with the easy to learn UI and analytics and then transition into more powerful analytics and aggregations as they get comfortable with the tool.

As a systems integrator, Seeq positively impacts my organization as it is a part of my business model to understand different technologies that exist in the market and be able to leverage those at my clients. The tool has allowed me to interface with clients that use Seeq or would like Seeq at their business.

Within the organization structure and sometimes when trying to replicate existing worksheets and organizer topics from one Seeq system to another, there is no import or export feature of those tools. I understand that because of the way the data sources may identify the individual tags, it makes that difficult between one Seeq environment and another, but having the ability to map those at some level to be able to migrate effectively would be a very essential tool for going from a development environment using Seeq Analytics Workbench into a production environment that might not have the same underlying mapping. Some way to import and export and then change from one environment to another would be a good feature to have.

I choose a nine because all tools have room for improvement, but this one covers a very large breadth of the analytic need and connects to the majority of data sources that are needed for data analytics, whether it be SQL data sources or historian data sources, and it integrates well within the cloud data space. It definitely fits that niche very well. However, some graphing and tabular functions are still being worked on and are yet to be included fully.

I have been using Seeq for six years.

Seeq's scalability allows for multiple remote agents to connect to a single SaaS instance and because they have a SaaS instance, they are able to scale that very well if it needs more memory or processing power.

The customer support for Seeq is very good as they respond very quickly and will scale based on the size of client and number of requests they receive.

I have previously seen solutions like Ignition, leveraging the data and trying to create charts and graphs, or DataBricks, where you pull in all the raw data and then try to do machine learning on that data. Most often, the solutions are either PI Vision or Excel, where the data is extracted out from the sources and then attempting to do those calculations offline to make the assessments needed and then report back. That is essentially what I have seen mostly in the market. Seeq really stands out in the space.

I evaluated a couple of other options before choosing Seeq, but I cannot remember which ones.

I measure success for my Seeq deployments by ensuring that the data sources are connected properly. From a deployment standpoint, all the servers and data are flowing and users can generate their initial trends. As far as a use case, there is both ROI involved. Typically there is a specific use case that is intended to generate some value for the company and not just present a new way to show the data, whether it be dollars returned or data time saved. Those are typically done on an annual basis and then get rolled into a contract negotiation with Seeq for that value earned. The other KPIs are also user adoption, how many users are actually using the tool and looking to improve how they operate their engineering practice. That is where I have seen the most value is on the engineering level where engineers would have to spend weeks or months compiling and aggregating data from historian and SQL data sources to create a clear presentation of a situation on a line, whereas within Seeq, they could get that down to hours or days if needed.

I would give the same ROI metrics that I have shared already.

My experience with pricing, setup cost, and licensing shows that Seeq is generally a bit pricey, but they also look to the value that they are providing and tend to negotiate based on that. It is not the cheapest. Something like Grafana, which can perform some of the charting and analytics function from historian type data sources is more out of the box and open source, so there is no cost. However, the features and collaboration that you get with the SaaS tool in Seeq are really worth the value if the company is large enough to make use of that.

I typically integrate Seeq with AVEVA PI Historians and AVEVA Asset Framework, but also other historians like Ignition or Aspen as well as Wonderware, and then also SQL data sources like Rockwell Alarms and Events or TrakSYS type SQL data sources.

I would describe the learning curve for new users on Seeq as very steep for general engineers and operators because understanding the charts, graphs, trends, and what you are trying to do with the analytics is not entirely what they focus on day-to-day. However, for somebody who lives with process analytics and data historian trends and wants to do calculations on them and is very comfortable doing that in Excel, the UI is actually very intuitive and would be a very shallow learning curve that they could learn very quickly.

Seeq handles security and user access controls by integrating well with Azure Active Directory to use those groups to map to groups within Seeq to allow controls for specific folders and sharing. As a primarily ad-hoc analytics tool, it allows for great sharing and collaboration across quick understandings, but it also allows for formal governance and control over specific folders so that there can be locked down structures as well, so that the data set has not been manipulated or changed. It does provide both good data access and control on the source data sources. However, once the data is altered within Seeq, say using a calculation, that becomes now a Seeq data point, and some of that governance is no longer applied in the same manner.

It is very easy to integrate Seeq with other enterprise tools or platforms. It is more of a business practice on what to identify Seeq as the source for. If there are competing tools such as statistical software like JMP or others, then it has to be called out specifically what Seeq is doing so that users know which tool to use in certain situations. As far as integrating, it does a little bit of standalone and then works with DataBricks or other things because you can connect to those data sources very easily. Likewise, other tools can connect to Seeq data sources via API.

I manage updates and maintenance for Seeq by noting that they have done a good job of allowing for non-regulated clients a very simple update process that connects both to their Seeq SaaS server as well as the remote agents that connect to the data sources with some ability to do that remotely. Within the regulated environments, it gets a little trickier because you have to then deal with that client's regulated update policies, which are not necessarily Seeq's problem, but Seeq does a good job of managing and working along with that as well.

Seeq's reporting and dashboard capabilities are flexible and useful for my needs.

My advice for others looking into using Seeq is that it can provide a very quick way to improve your engineering and analytics, especially if you already are historizing data into a process data historian because it allows you to look across multiple lines and multiple data sources very easily and gives you the insight that you may have to search for otherwise in a very difficult way.

I would rate Seeq overall as a nine on a scale of one to ten.