What advice do you have for others considering Seeq?

I would advise others looking into using Seeq to consider their use cases and build an internal repertoire of commonly used features. It is beneficial to build camaraderie among your user base so they can exchange techniques and formulae. One of Seeq's powerful features is its ability to facilitate collaboration across your team. I gave this product a rating of eight out of ten.

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.

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.

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.

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.

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.

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.

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 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.

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

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.

In terms of data integration, Seeq is outstanding. You can connect to any data source you want, making it easy to achieve connectivity. Seeq is a cloud-deployed solution with appropriate mechanisms to ensure security. One key aspect is that data flows only one way: into Seeq. Data does not come out of Seeq, so you don't have to worry about Seeq altering your data, stopping motors, or closing valves. I recommend the solution. If you are considering buying it, my advice is to get your hands on it and test drive it. However, it's important to understand that no software is perfect. If you're looking for something that meets your every dream, you'll probably not find that. But if you're looking for something complete, let me explain. Some data analytics solutions are only part of a solution. For example, they may only do predictive modeling, connect data sources, visualize data, or align things in time. Some people think those features by themselves constitute an entire data analytics package. Seeq is a complete solution. It's built for collaborative engineering, allowing others to see the work you did and share it easily. You can deploy it across the enterprise so that everyone sees the results. Overall, I rate the solution a ten out of ten.

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.

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.

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.