Name: What’s New: The latest Spotfire® enhancements that unlock the true power of visual data science
Uploaded: 2025-05-08T16:16:10.745Z
Duration: 1 h 16 s
Description: What’s New: The latest Spotfire® enhancements that unlock the true power of visual data science

Transcript for "What’s New: The latest Spotfire® enhancements that unlock the true power of visual data science": Hello, everyone, and welcome to today's webinar. We're about to kick it off now. So today's webinar is called what's new, the latest Spotfire enhancements that unlock the true power of visual data science. We're thrilled to have you with us. I'm Jean-Philippe Richard-Charman, and I'll be one of your speakers today as well as as your host for this session. Now before we get started, I just wanted to cover a few housekeeping items to ensure you have the best experience. The webinar will last approximately forty five minutes with a q and a segment that will take place at the end of the presentation. If you have any questions during the presentation, please use the q and a panel, which is located on the right side of your screen, and we'll address as many questions as possible during the q and a segment at the end. Additionally, we've made a few assets linked to today's webinar available in the doc section, which you can see right next to the q and a tab on the right hand side of your screen. So please do feel free to access these documents. After today's session, a recording of today's webinar will be made available on demand, and we'll also email you a link to that on demand version shortly after the after the event. Now with that, let's dive in. I'm excited to introduce our main presenter, Niklas Amberntsson, our director of product management here at Spotfire. Now just to quickly talk you through today's agenda, we'll be discussing our Spotfire packaging and then diving into what's new in Spotfire 14 dot five, our upcoming release. And we've got a few demos planned for you as well during today's session. And then as mentioned, we'll touch on, the q and a part of, this session after the presentation. Now let's dive in. Spotfire is the visual data science platform that makes smart people smarter by combining advanced analytics and industry specific visualizations to solve complex business problems. Spotfire has been in the BI and analytics space for a long time. However, the types of use cases that we address are not the traditional BI use cases. Visual data science is at the intersection of visualizations and advanced analytics, a differentiated approach to what has previously been available in the market. Now Spotfire continues to be the only vendor in the data science space that approaches complex problem solving with a visual first approach. We continue to reimagine the space with our continuous approach to innovation. And with that, as a lot of you are aware, comes our new packaging. Spotfire offers a comprehensive visual data science suite divided into five key components. Spotfire analytics. Now this is the core of Spotfire for analyzing data and building analytic applications using interactive visualizations and advanced analytics. Spotfire Data Science, our new product offering, is a robust platform that combines machine learning, modling, and advanced data science capabilities tailored to various industries. Spotfire enterprise for the enterprise organizations that need to scale easily and at demand, designed for organizations to publish and share analytic applications securely and widely. Now in terms of Spotfire Enterprise, we've got two key add ons. So one of them being Spotfire Enterprise advanced data services. This enables logical data views across disparate sources for effective analysis and Spotfire Enterprise data streams. This is the real time data streaming integration that enhances Spotfire Enterprise with live data feeds. Now let's dive a bit deeper into our different offerings. Spotfire Analytics is at the core of our platform, offering a seamless interactive visual analytics experience with such capabilities as interactive visual analytics, where you can explore disparate data sources seamlessly within a unified analytics environment, drill down and navigate across linked data tables using fully interactive interactive visualizations to increase speed to insight. Visual data wrangling, where you're able to wrangle and fix data while analyzing it with easy and nondestructive data blending, shaping, and cleansing directly within the visual analytics environment. Powerful geo analytics, whereby you're able to connect seamlessly to spatial data, including leading databases, real time data, GIS data, map services, and spatial files. And lastly, AI power recommendations, whereby we're suggesting the most powerful, insightful visualizations matched with an advanced analytics algorithms, helping you with uncovering hidden patterns in your data. Now this really allows businesses to make faster data driven decisions with minimal manual effort. Next, we have Spotfire data science, which extends our analytics capabilities with machine learning and predictive modling and enables our key industry customers to solve their industry specific mission critical challenges and drive speed to insight like never before. Spotfire data science includes all the functionalities of Spotfire analytics and more with data understanding and preparation, modling and prediction, process improvement, notably statistical process control and reliability analysis. And last but definitely not least, Spotfire data science includes industry specific solutions with specialized visualizations and connectivity for industries such as energy and manufacturing. Next, Spotfire Enterprise. Spotfire Enterprise fosters collaboration while ensuring security and governance. Organizations are able to scale their analytics operations while maintaining security and efficiency. You're able to publish and share insights across the organization, built in security and governance controls, ensure data and and analysis access aligns with privacy regulations and internal policies. Through integration and extensibility, Spotfire connects with diverse data sources, third party tools, and APIs for tailored solutions. Automation keeps analysis up to date and distributes them on a schedule or in response to key events, ensuring your stakeholders always have the latest insights. Additionally, automated report distribution ensures that stakeholders receive the latest insights at scheduled intervals or at triggered events. Now as an addition to Spotfire enterprise, we have the Spotfire enterprise advanced data services add on, which empowers users to work seamlessly across diverse data landscapes with data virtualization, delivering a unified view without data replication. Its intuitive interface supports data wrangling and transformation, enabling users to cleanse and prepare data visually. With data modling, users can define relationships across sources for simplified, powerful analysis. Data caching boosts performance by reducing query times, while built in data quality tools ensure accuracy and consistency. Now robust security and governance features safeguard your data with enforced access controls and policy management. Additionally, our other add on to Spotfire enterprise, the data streams add on, enables real time intelligence by ingesting streaming data from sources like sensors, financial feeds, and social media as it's generated. With live data visualization, you can interact with up to the moment dashboards to track KPIs, detect anomalies, and monitor trends instantly. Powerful data transformation and enrichment tools cleanse and enhance streaming data before analysis, while historical data integration adds essential context for deeper insights. Automated alerts and notifications additionally ensure timely, proactive action when critical thresholds are met. Now Spotfire's latest offering simplification and the introduction of our new product, Spotfire Data Science, offers you an unparalleled visual data science experience, whether through interactive visualizations, machine learning, or enterprise level collaboration and automation. Now with that, I'd like to hand over to Niklas to walk us through the latest that you can expect from Spotfire in our upcoming 14 dot five release. Thank you, Jean-Philippe. So just sharing my screen here. So what's new in Spotfire fourteen dot five? So in all three of the main Spotfire products, we are delivering new capabilities. So in Spotfire analytics and thus available to all customers, custom symbols make it easier to understand maps and enables more use cases for both maps and scatter plots. Improved Box Plots enables a deeper understanding of distribution patterns in complex data. And every user benefits from a general modrnization of the user experience, such as new web user interfaces making work more productive and from broadened support for single sign on and higher security when connecting to data sources using OAuth. Also, a range of improvements to action mods enable organizations to leverage more advanced analytics to further optimize their business processes. The new product, Spotfire Data Science, includes all capabilities of Spotfire Analytics. But in addition, professionals in oil and gas that buy Spotfire Data Science can make better decisions faster when estimating the value of their land leases, placing wells, or planning drilling operations. Manufacturing engineers that have Spotfire data science can increase yield and quality of their manufacturing process by analyzing distribution patterns and identifying problems early to save costs and reduce downtime. Professionals in any field can more easily address challenges such as analyzing missing data and identifying patterns in time series data. And for Spotfire Enterprise, there is also a range of improvements in governance, sharing, and automation. With recent capabilities in automation services, organizations can leverage automated analytics more broadly, more timely, and with personalized content. For organizations deploying Spotfire widely with thousands or tens of thousands of users, it's now easier than ever to assign licenses to users, to see how many licenses you have used, and to generate license reports. So let's now dive deeper into what's new in Spotfire Analytics and then the other products. For quite some time now, we have been developing a completely new experience for authoring visualizations. We designed it to unify and simplify how users create and customize visualizations across the Windows and web clients. With one consistent design across all visualization types, it's easier than ever to configure and understand your visualizations. But if you still don't find the configuration setting you're looking for, the new user experience is searchable and is also very efficient since you can multi select and configure several visualizations in one go. The new experience has been available as a preview for some time but is now enabled by default. And while this new user experience is still evolving, the existing properties dialogue remains available and can be used side by side with a new experience, ensuring a smooth transition for all users. So we are not replacing the old visualizations properties dialogue at this point in time. As a direct result of the new visualization properties experience, users in the web client can now create and edit box plots to visualize data distributions and quickly identify outliers. They can develop heat maps to recognize patterns within extensive data sets and generate summary tables that provide clear and concise overviews of complex data. And for the fans of the box plots, Spotfire 14.5 introduces multiple y axis scale support, which is a highly requested feature that enhances readability in Trellis mod for the box plots. So previously, all Trellis panels share the same y axis scale, which could make it difficult to compare distributions when data ranges varied significantly. Now each panel automatically adjusts its vertical axis scale, ensuring that the box plot remains clear and easy to interpret. Spotfire 14.5 introduces custom symbols, custom shapes, that gives you the flexibility to tailor visualizations to your industry's or processes' needs. So whether you're working with maps or scatter plots, this feature makes your data clearer and more intuitive. You can import custom symbols in formats like JPEG, PNG, or SVG and use them as markers in maps and scatter plots. This is very useful for industries where the specific icon, such as machinery or equipment icons or process symbols or scientific symbols of different kinds, are important for the end users' understanding of the data. So custom symbols obviously help solving industry specific use cases. But in an enterprise context where you have hundreds of users that add symbols to the maps and create new symbols, then governance is also critical. So therefore Spotfire allows you to build and share collections or custom shapes through the Spotfire library so you can maintain consistent visual standards. You can import a set of images to create a collection of markers. You can name the markers. You can set keywords to make it easy to search in the Spotfire library and choose how the coloring applies to each of the markers. And when the collection of shapes is updated in the Spotfire library, the update will propagate to all analyses it is used in when those analyses are opened. So this takes us to our first demo. So let me just get off camera here so I can concentrate on the flow. So this is about custom markers. And in this case, it's a number of geohydraulic symbols that indicate various properties in a region in The United States. So these symbols are familiar to geology professionals and make it easier for them to interpret the map. But since I'm not a geology professional, I'll use another dataset to show how the shapes are easily configured. So this is data about boats and about wind turbines that are close to the Gothenburg Harbor. If I want to have a boat marker for the boats, then I can just click on the, shape picker like this, and then we see the normal default shapes on top. Then we have the geohydraulic features we looked at at the other map. Those are not useful for me here. There's also some other symbols that have been added, and here's the shape collection that actually includes boats. So I'll select boat, for the markers that are boats. For the wind turbine, I can also look for do we have a marker for that? I can search. Yep. There is a wind turbine marker here. And I can actually also configure this to some extent here. So you can see that this little cross here is actually the anchor point of this marker shape. I can put it in the middle of the wind turbine instead, and you see that the position on the map changes a little bit. But I think that for a wind turbine, it makes sense to have it at the base of the actual wind turbine. This makes, this feature, of course, makes Spotfire maps even more versatile and broadens the use cases that Spotfire maps can serve. But while we're at it here, let's also look a little bit more into the new visualization properties experience. So here we see a couple of different visualizations. And as I'm selecting different visualizations, you can see that the contents of the visualization properties panel is changing dependent on what I configured. What shows up in the visualization properties panel here is only those aspects that I actually have configured for each visualization. If I want to add more configuration, to this particular visualization, I can click add, and I get to select from what's supported by this particular visualization. In addition to that, I can actually multiselect visualizations. For example, we see we have the line by field here. Maybe I want to remove that from the legend. And, actually, I want to clean up all the legends in one go here. Right? So I would not like to display the data table, in this case. And for the color by. Actually, I don't wanna show the title and actually don't show the axis selector either. And then we have a very nice and clean legend here. And while we're at this, why don't we add grid lines to all visualizations in one go? I think this illustrates that working with the new visualization properties experience can be very efficient. I estimate that I saved around 50 clicks or so compared to if I have done this using the old visualization properties dialogue for each of the visualizations. Going back to the presentation and a feature that is not exactly new in 14.5 but that we thought important enough to bring up here. The data canvas shows how data tables relate to each other in the new relations view. So data table relations in Spotfire are used in order to configure how two or more data tables relate with respect to marking, filtering, and read down. So that means that you are able to analyze data in several data tables in combination without having to physically join them. And the relation between the data tables is specified by selecting columns used as keys between the data tables. You can now also use more than one column when specifying the relation, which is helpful when analyzing data across several data tables that do not have any single column that represents the type of relation you want to configure. And not the least, this feature makes it a lot easier to understand how an analysis is working when you're new to it. There are also improvements for data access. We've broadened support for OAuth2 OIDC when connecting to data from Snowflake, Oracle and WMS map layer services. The Google BigQuery connector now supports private endpoints as well. Also, you can now directly load data in Parquet format from local drives and also save to Parquet format. Parquet is a modrn and popular format for data, which is built for efficient data storage and retrieval. When loading data from data connections, there are also improvements in how the users can be prompted. Now data connection prompt supports multiple values. It supports optional prompts that the user can skip if they want and also prompt groups, which gives more flexibility for the user to dynamically change their selections. Action mods enable developers to create mods that end users themselves can use in their analysis in order to automate analytical tasks that save time and or let them use advanced analytics that they would otherwise not be able to use or have the time and energy to use. So action mods can make use of analytics more repeatable and enable using analytics with higher quality by making it easier for end users. So while a developer is required to create custom action mods, once that is done, the action mod can be shared through your library just like visualization mods. End users can then easily use the action mods when analyzing data and building their analyses. In Spotfire 14.5, there is a range of new improvements that has been added to action mods that make it possible to create more powerful and easier to use actions. This includes running data functions that are stored in the library, meaning that action mods can easily combine running a data function with creating and configuring visualizations that show results of running the data function and also performing other tasks. This means that action mods can package data preparation, data science, and data visualization into a reusable mod that end users can leverage. Action mods can now create and configure visualization mods in the same way as native visualizations. There are new input types that are supported, enum, data column, data column lists, and data view, and all these make action mods much more versatile and easier to use. So now we're going to look at the demo for action mods. So in this demo of action mods, we will see an example of how an action mod can be used to help end users automate routine tasks. So in this case, it's about using quality test data to find root causes for returned products. This will involve a set of analytical actions, such as data transformation of the raw data into a format that is suitable for analysis, running a data function, creating a set of visualizations to analyze the data, and tagging rows in the data. All these things can be done manually from the user interface, but with action mods, much of the work can be automated, which helps the end users use the functionality. The dataset here, as you see on the screen, is quality test data for electronic power supplies that our company manufacturers. The data is in a tall skinny format with multiple rows per device. So you can see the unit ID here. So if we check, it's, 13 measurements for each of, for each device. We also got a list of some serial number for units that have been returned, and it's standard procedure for us to check if we can find out anything interesting, anything that is in common with devices that are returned. So some questions that we want answers are, is there anything that the returned units have in common between themselves? Do the returned units differ from other normal units? And are there other units that have similar test results as the ones that were returned? So in order to use this action, I'll use the actions fly out. And here we have the analyze defects action mod, and it supports four different actions. The first one is prepare data for analysis, which we will trigger. And this prompts me I have to provide the column that contains the serial number. I have to provide the column that provides the measurement value and the column that identifies the test. Then I run it, and that generates a new data table that has been pivoted. So we can now see here that by marking one line in this new data table, we actually that actually corresponds to 13 rows in the original data table. So sport the action mod created also a data relation in the background. When this is done, I can go on and use the next step in this action mod, which is analyze defective units. I run this action, and that then prompts me to provide a list of serial numbers, comma separated serial numbers for the defective units. And I fortunately, I already copied that into my clipboard. I also needs to identify the serial number column, and I press run. And what happens then is that the action mod triggers data functions, data function that analyzes the data. Just have to wait for it to finish here. And then the once the data function finishes, the action mod, in addition to that, generates a set of visualizations. And here in these visualizations, I can see in the parallel corner plot here, I can see that there are the the the return ones are marked, and I can see that they seem to have some things in common. Right? So they have low values of test 12, low values for test eight, high values of test seven. I can see the similarity score that has been calculated by the data function, and we can see that all all the defects the returned power supplies have a high similarity score, but there are actually lots of different there there are lots of other power supplies that also have are similar to, to the defect ones. Of course, this is even if I'm using an action mod, this is still normal Spotfire. Right? So I can still do whatever analysis I wanna do on my own. So I'll select to use the AI powered insights here to compare data, the marked data with the unmarked data, and find that there are relationships here as I we actually could spot with t eight, t seven, and t 12, and, a few others. So I do have one other action now, which is actually, find similar units. So I will actually trigger this action, and that means that I can select a few columns here, to narrow down the number of, to to narrow down the similarities between the defect and the nondefect power supply. So I press okay here, and then I can choose the algorithm. I'm sticking with cosine similarity and run again. The similarity data function executes, and we can see that, actually, the similarity score is now changed. So when we only looked at t 12, t eight, and t seven, then there was actually a more clear result here when it comes to the similarity between the defect and returned power supplies and the others. So there are now only a few of powers of the nonreturned power supplies that are similar to the ones. So I can actually mark I can actually mark these, all the ones that are here. And then I can actually add another action, which is tag mark for recall. So I'll do that, and that actually tags those units that are similar to the returned power supplies, but have not yet been returned. So maybe we want to recall them, and I can easily copy the serial numbers of those from this little list mod here. So as you saw, with a couple of clicks, I was able to spot some insights about the return devices. I was able to find other devices that have similar test results and perhaps should be recalled. But underneath the hood and driven by the Actionmod, hundreds of analytical operations took place. The data was prepared using data transformations and creating data relationships. Data functions calculated similarity. Visualizations were created and configured and the data was enriched through my selection and insights using the tags. And we could easily get a list of devices we may want to recall. So hopefully, you agree that this can be a great help in automating routine tasks while still letting the user deviate into his own analysis and explore own IDs at any point in time. So let's now look into what's new in the new product, Spotfire Data Science. This product includes everything in Spotfire Analytics, but also includes a set of add ons and built in data functions that implement industry and process specific analytic solutions. So let's start by looking into energy visualizations and analytics, and the add ons that, and and built in data functions that can be used independently or together. In energy, we are initially addressing well placement and drilling operations as well as estimating production value of leased land. So the well log chart is a highly specialized visualization for analyzing physical, chemical, and electrical properties of rock and fluid mixtures over the depth alongside a view of the geology. It's often used in oil and gas in order to optimize reservoir production. The well log arranges the measurements in a way that is familiar and expected by oil and gas engineers, and they compare the measurements alongside the geology track on the right end of the screenshot here that uses industry standard textures representing the geology type at a given depth. The 3 d surface and line chart is used to visualize wells and formations and typically used to evaluate production and plan additional drilling to optimize the production. This chart makes it easy to visualize the geometry of the wells, including the perforations of the well that are made to let the fluid seep into the well. The well spacing or as it's sometimes called in the industry, the gun barrel diagram is used for analyzing the spacing between wells, which is useful in order to evaluate if additional drilling of wells will help to improve production. The well spacing diagram visualizes the horizontal sections of the well and the field circles that you see in the screenshot here are the wells as they are spaced at a specific intersection when looking at them along the drilling direction. So think of it like looking into a pipe from one of its ends or like looking into the barrel of a gun. The lines you see going left and right, that is the formation tops, the rock formation tops, which indicates the top of geological layers. There are interactive features that allow engineers to determine precise distances between the well locations. So you can see the distances listed here in, on the screenshot. The previous visualizations all was used to look at multiple wells and focused on their relations. The wellbore diagram instead visualizes a single wellbore and its most important completions components such as the casing diameter, guns, perforations, and plugs. It's often used for diagnosing issues with production in the well and it visualize measurements such as concentrations or temperatures. These are measures that are taken by diagnostic equipment at various positions of the well. The wellbore diagram helps production engineers, drilling engineers, and well completion engineers with a job such as deciding what corrective actions they wanna take or if they need additional diagnostic operations before deciding what to do. Switching to manufacturing, there are also add ons that help manufacturing engineers, particularly in semiconductor engineering, to improve yield and quality. Spotfire's map charts are extremely flexible and versatile and may be used to create so called wafer maps used in semiconductor manufacturing to represent characteristics of the produced wafers of components as they go through stages of the production process. However, creating a wafer map using a map chart is a nontrivial task since it requires quite elaborate configuration of the map chart. So this is a great example of how an action mod can use help users quicker and easier use more advanced analytical functionality than they would otherwise be able to do. Wafers can be divided into zones to make it easy to understand failure patterns. So the wafer zone profile action provides options to analyze both radial and angular zones. Radial zones make it easier to detect issues that are more common on the edges of the wafer or in the center, which, for example, could be due to the edge losing heat faster than the center due to a thermal processing step. On the other hand, variations in failure rates between angular zones can indicate asymmetry in a gas flow, for instance. There are also more general add ons, so these are actions. Time series analysis is common in most industries, for example, in finance, utilities, and, of course, in manufacturing and energy. And we'll have a variety of time series preprocessing data functions and actions, including time series normalization, allowing individual series to be normalized to a common scale, time series imputation with a variety of imputation methods, and time series resampling, increasing or decreasing the frequency of time intervals for a given series. The resulting series will be regular and equally distanced. And time series smoothing. It reduces noise in time series data caused by outlying values to easily see the underlying trends. And, of course, nearly everyone that works with data has to deal with missing data. This first release of the missing data summarization action, include uses a data function designed to reveal missing data patterns across rows or columns and also identifies invariant columns. There are also built in data functions into Spotfire. So Spotfire adds a number of data functions built in out of the box that addresses analytic challenges in energy and manufacturing. These first data functions are focused on time series, geo analytics, and missing data analysis. These built in data functions are accessible and configurable to Spotfire users, but they can be executed by any user with the right to execute data functions. But it's only the Spotfire data science users that can configure them. There's a lot to be said about each of these different data functions, but this is subject to other webinars and videos. I will not speak more about these now in the interest of time. And about data science add ons, so these energy and manufacturing specific visualizations and actions that we talked about earlier, that we just talked about, these are examples of what we call data science add ons. Data science add ons are fully supported plug in modules that are maintained and updated as any other product functionality. And this relates to perhaps the most important new capability in Spotfire data science. End users of Spotfire data science can browse these add ons from within the product, download, and use them. They are maintained with new versions for bug fixes or patches in the same way as other product functionality. The add ons are visualizations or actions and are commonly highly specialized for the use case. So, in fact, the energy and manufacturing visualizations and actions we just talked about, they are data science add ons. This means that new innovations can be added to Spotfire fourteen point five during its lifetime, meaning that users get access to new capabilities more quickly without having to upgrade to the next full version of Spotfire. This means that the value of Spotfire data science grows over time as more and more capabilities are made available, and they are made accessible to end users without IT effort to upgrade the home Spotfire system. I will make just a very short demo of the data science add ons. So this is how it looks in the product. When you want to browse for add ons, you click the add ons in the Faison data fly out. And here, you now see a list of different data science add ons that you can choose to add to the page of your analysis. You can view details about it. You can learn more, or you can save it as a library item or save it as a local file. And in this case, I already configured a well log visualization here, and let's look a little bit more into that. So this plot displays measurements in tracks. So in this case, there are four tracks. There are three tracks with measurements, as you can see here, the different curves. And there is one track with formation and geology information about whether this is mudstone or sealstone or limestone, etcetera. For configuring this, there is actually a custom configuration UI that is organized so that you can select to configure, the settings on the track level. So in this case, perhaps we want to say that the CNLS and RHOB is the most important. So we want to have a bigger portion of the visualization area, maybe 50% like that, used for that. So I can see in with higher resolution on CNLS and RHOB. Or if I want to configure a specific track, the CNLS, for example, we can configure this as well. So we can select to have a curve bound color field, for example, to the RHB. We can configure solid fill or gradient fill and so on, but I guess we are okay like this. And we can now see that there is a field between the curves in this track. As you see, this well log chart, like many of the other, data science add on, are highly specialized visualization for their purpose. So then moving on to Spotfire Enterprise. So enterprise organizations using Spotfire can look forward to important new capabilities that make the governance of licenses easier, make analytics more efficient for organization, and make it easier for admins to stay on top of the operations and react quicker when issues occur. Spotfire Enterprise is about governance sharing and automation to make analytics usable and valuable for the large organizations. And the important improvements in 14.5 are improvements in automation services that make it possible to automate the creation and sharing of personalized reports or data for individual users at a large scale. There is a new license management user interface that makes it a lot easier to work with designing product licenses to users and to understand how many product licenses that are in use. There are also great improvements for monitoring and understanding the status of the Spotfire system itself with new UIs for monitoring information links and schedule updates, meaning it's easier to be more proactive if one sees signs or problems. And the new notifications for scheduled updates and automation services jobs may make it easier to react to problems. Together, these capabilities makes it easier to improve uptime of the Spotfire system. So and not exactly new in Spotfire 14.5, but very recent, there are automation services, improvements that provide new tools for companies to make analytics available more broadly, more timely, with finer detail, and with personalized content. So this is made possible by several enhancements. The new more flexible scheduling of automation services tasks means that you can make reports be produced exactly when needed, whether it's biweekly, on the third Thursday of the month, or other. The ability to execute iron Python scripts in automation services tasks provides an unparalleled opportunity for customization, and the ability to repeat a set of tasks in automation services lets you filter data and produce personalized PDFs as an example. Together, these improvements makes it possible to automate edits to any number of analyses in the Spotfire library if needed if you want to update them. The new license management UI in the Spotfire server reflects the new license modl, and you can easily assign the desired product license for each user group. There is no fiddling around with multiple choices of feature licenses. Just select the product license you want the user group to have. When you have selected the product license for the group, you can, if you want, disable some of the features that are part of that product license, but you are not able to accidentally add features that belong to another product. The Spotfire service license UI even tells you how many users you have assigned to each product license, making it easier than ever to make sure you are compliant with your contracts. And, yes, if you're running on the old license modl with Spotfire analyst, Spotfire server, and so on, the UI supports this as well. The new deployment report is a single click experience to export a summary of license related information about your Spotfire system. It's the license assignment, license usage, license feature usage, and library usage, and some system related characteristics such as the processor instances, etcetera, in use. There is no personal information or otherwise sensitive information contained in this report. In an enterprise environment where there are hundreds or thousands of users that create new analyses, they copy each other's analyses, it may happen that the library storage size grows very quickly. It's important then to prune the library periodically to increase visibility, reduce clutter, improve backup, import and export performance, and save storage space in general. The delete library items command now supports deleting library items defined by library search query. Library search in Spotfire is very versatile, and this new feature makes it easy to delete items that have not been accessed or modified for a certain amount of time, items that depend on an obsolete data source or items in a specific folder, items above a certain size, or combinations of any support and search criteria. This makes it easier than ever to prune the library with precision. And also note that it's possible to automate and schedule pruning jobs with search queries, for example, through using cron jobs on the server. In any enterprise environment, the load on the system varies with the user's activity and working schedules. At peak loads for the Spotfire system, it can be used useful to proactively monitor jobs, such as schedule updates and information services jobs in order to intervene before problems occur or reduce the impact of problems. This is now easier through the new server user interfaces for scheduled updates monitoring, information linked monitoring, and also for monitoring the information services process itself. But, of course, sometimes things go wrong when you least expect it. In Spotfire fourteen dot five, it's possible to configure email or HTTP notifications when scheduling updates or automation services jobs state to change, such as if they fail, which has been a highly requested feature from many administrators for quite some time. This makes it easier for administrators to react quickly to this type of problems. Perfect. Well, thank you very much for that insightful presentation, Niklas, and thank you to everyone who joined us today. Now before we get on to our q and a segment, a few things that we wanted to share. So we have two great series great webinar series that are being added to on a regular basis. So whether you're looking to find out more about Spotfire or looking to learn about the latest in terms of what's new, don't hesitate to register to the full series. Now a recording of today's webinar will be available soon, so please do keep an eye on your inbox for the link. Now if you're interested in learning more, feel free to visit our website at spotify.com or contact us dire us directly. There are lots of ways to interact with us, whether it is by our socials, through our community. Additionally, our blog site has lots of great content where we share the latest on visual data science, dive into Spotfire data science in more detail. And last but not least, if there are any enhancements that you would like to see or have ideas that you like to share with us, don't hesitate to visit our ideas portal. Now onto our q and a segment. And we've received quite a few different questions, throughout the course of the webinar. But just to kick us off, the initial question, is Spotfire Analytics the new name for what used to be called desktop? So I can start with that one, and if you'd like to add on Niklas. Yes. So Spotfire Analytics does replace Spotfire desktop as well as Spotfire analyst and Spotfire business author. Not sure if you wanted to add on to that, Niklas. I have nothing to add to that. That's perfectly correct. Perfect. Our next question, is Spotfire enterprise does Spotfire enterprise wrap up all the different components, for example, web player, server, and ter? I think that would be not free of Niklas. Yeah. And, that that is correct. So Spotfire Enterprise actually contains all the traditional Spotfire services. So the web player, automation services, the ter service, the Python service, the R service, and the server itself. So customers that use Spotfire server we'll, we we we'll we'll be using Spotfire enterprise with the new packaging. Perfect. Is there a diagram to explain the different products for a layperson? So I'm not sure if you saw, the start of our presentation whereby we were, showcasing the different packaging. But, yes, there is a a slight diagram, and we can reach out to you directly and share that with you, outside of this webinar. So I'll make note of that, and I'll get in touch with you directly. Our next question is, this specific person has, is a few versions behind in terms of Spotfire. Are there process diagrams or spaghetti diagrams available now to visualize sequence of events data? Yes. So I don't know exactly what the visualization needs are here. I would actually suggest that, Ingun actually reaches out to us and speaks to Arnova Rand, who is our visualizations product manager, if there is, either anything that can be done today or if there is something in road map for that. Perfect. Yeah. I'll, put you directly in contact with Arnau, and he can take it from there for you. Our next question, does Spotfire provide a tool to create icons? No. Spotfire does not provide a tool to create icons, and, unfortunately, I do not myself have experience of using open source tools that may be used, for this. But, I mean, certainly, we can we can give examples to that. I just can't do it right now. No problem. Does Spotfire Data Science remove the need to download these mods from the community? So I can start off with that. So as you've seen from Niklas demos specific to Spotfire Data Science, you're able to access the add ons and access the visualization logs directly within your instance of Spotfire Data Science. Now these visualizations at the moment are very specific to, both energy and high-tech manufacturing. So it is done through a direct integration with our community, and you're able to add those visualizations directly into your analysis within a couple of clicks. Now within our community, there are still quite a few different visualizations and different mods, different data functions still available that you can still access through, the community. And if you're using Spotfire Analytics or Spotfire Enterprise, you will still be able to access the exchange in community in our community site to access these mods and data functions as well. Not sure if you there was anything else that you wanted to add there, Niklas. No. I think that's a complete answer. Perfect. Is 14 dot five going to be an LTS version? So just to start this off, no. No. 14 dot five is not a long term, support version. It is one of our innovation releases, and we will be sharing some additional information in due course as to when you can expect our next LTS version? We actually do share, our plans, on the documentation site for Spotfire. So if you Google for Spotfire LTS releases, for example, you can see our current plans. Okay. Are there any performance monitoring capabilities added for r, and Python services? For example, detailed reports of successful or failed jobs. Yeah. I I'm not aware of any such improvements, but, of course, there there might be something that have gone on the Maredo. But, no, I don't think there is any such improvements. No problem. And our last question before we end the webinar, are are and now nodes as web player and Python? Yes. And they actually have been, for for a while. So they they were definitely services. Perfect. Thank you very much. We're coming to the end of our webinar now. I just wanted to thank you all. Obviously, if there are any questions that we didn't get to, we will be in touch with you directly. Now with that, just wanted to thank you once again for joining us. And as mentioned, a link to the on demand, version of this webinar will be sent to you by email. Thank you again, and have a great rest of your day.