Can Explanations be Relevant?
We are interested in rolling out Explanations to our clients, but I've hit a block and need some guidance. I think that the source of the issue may be our use of null replacement values in our data models. We don't allow data to join out of a query and instead ensure that all fact data joins to a null-replacement row in our dimensional tables. For example, given a fact table of product PURCHASES, imagine it was possible to sell those products at a discount in some orders. Assume that the fact table then joins to a dimension table of DISCOUNTS. Most purchases do not have a discount, but some do. Without a null-replacement row in the DISCOUNTS table, adding a field from that dimensional table onto a widget will limit the results in the widget to only fact rows with a discount. In our models, we would replace the null discount key in the fact table with a value that joins to a null-replacement row in the DISCOUNTS table. For example, replace null with -999999 in the fact table and have a row in DISCOUNTS with [id] = -999999 and a [description] = "(none)". The problem we're running into with Explanations is that the feature is presenting only the most useless information as being the most probable explanations. For example, when a single discount exists in the data set, then 99.9% of the PURCHASE rows have a "(none)" DISCOUNT, which is deemed this highest scoring field. More generally it seems that Explanations just reports back the most highly probably explanations as those fields where the member in the field is the commonly present in the data anyway, and it's just reflecting the raw change in fact data. For example, here's an image where the drop in revenue is explained by the drop in revenue from web sales (BRestAPI). But web sales constitute a larger over proportion of sales after the drop (89% up from 82% in the prior period). And another example in which an "Initiator Specified Flag" is whether or not an alternate order "owner" has been specified on a order. Specifying an alternat owner is allowed ("Y"), but not a common practice ("N"). Again, we're just seeing a reflection of sales in the explanation. Is there anything we can do to make the explanations discovered by Sisense relevant? The way it's functioning now, it would be better if Sisense DID NOT recommend possible explanations and just let the user Explore Other Fields immediately without having to wait for Sisense to return the list of completely irrelevant fields. I've tested this across many of our clients' data sets and found the same behavior. Hopefully there are options for us to improve the recommendations or maybe prevent it from recommending.Views bookmarking: Use case of a financial technology company
Introduction FlexTrade is a global provider of multi-asset execution and order management systems, supporting trading workflows across asset classes, venues, and strategies. Their platforms generate large volumes of highly detailed data that users rely on for day-to-day analysis and decision-making. Companies like FlexTrade operate in an environment where users need deep, flexible analysis across a wide range of dimensions: asset class, venue, strategy, region, client, trader, time, and more. Pivot tables are a natural fit for this kind of detailed, highly dimensional analysis. However, as the number of dimensions grows, teams quickly hit a trade-off: Putting all dimensions into a single widget becomes expensive to query and difficult to interpret. Creating separate widgets for every dimension (or combination of dimensions) leads to bloated dashboards, slower load times, and a poor user experience. This use case focuses on how BloX was used to solve this problem by introducing view bookmarking, a flexible way for users to switch between different slicing configurations (in this example, a set of four dimensions) within a single widget. It also highlights how BloX can be used not just for custom visualizations, but also for building small, purpose-driven mini apps directly inside a dashboard. What the solution does This solution uses BloX to manage view bookmarks for a pivot table. Instead of permanently adding all dimensions to the widget, BloX acts as a control layer that lets users select dimensions to include in the pivot at a time. Each selected combination can be saved as a view bookmark, representing a specific slicing configuration of the same underlying pivot. With this solution, users can: Select up to four dimensions to apply to the pivot table Save the selected combination as a personal bookmark Load and reuse previously saved bookmarks Delete bookmarks that are no longer needed Up to 20 bookmarks are supported out of the box, and all bookmarks are user-specific, allowing each user to maintain their own set of preferred analytical views. The solution also includes basic validation and error handling, such as preventing empty and duplicate bookmark names. From a technical perspective, BloX dynamically updates the pivot’s metadata. From a user perspective, it feels like switching views within a single widget. This keeps the analysis flexible while the dashboard structure remains simple and performant. Why it’s useful Scales to 10+ dimensions without UI overload Multi-asset trading analysis often requires exploring many dimensions, but not all at the same time. This solution allows FlexTrade users to work with 10+ dimensions while only surfacing the few that matter for the current question, resulting in less visual noise, lower cognitive load, and faster insights. Maintains dashboard performance and keeps dashboards clean and maintainable By avoiding massive pivots with every dimension enabled or dozens of near-duplicate widgets, the solution keeps queries efficient and dashboards responsive, even as analytical depth increases. One widget with dynamic views replaces an entire grid of narrowly focused widgets, resulting in dashboards that are easier to navigate, faster to load, and easier to maintain. Attachments BloX-ViewDimensionBookmarks.dash.txt (example dashboard using the Sample ECommerce cube) BloXActionsForBookmarks.zip (BloX actions' scripts) ViewsBookmarkV2-2025-12-29.json (BloX template for the view bookmark widget, also included in the .dash file above). Note: Remove the .txt extension before importing the dashboard (.dash) file. The BloX widget also includes a script that automatically populates the dropdown menus with the available dimension names and existing bookmarks based on the widget’s metadata. Here is the script: // Dropdown classes used in the BloX code const dropdownClasses = [ "dimensionDropdown", //dropdowns for selecting the four dimensions "bookmarkDropdown" // dropdown for selecting existing bookmarks ]; const valueToDisable = "Select"; // placeholder value to disable widget.on('ready', function() { dimensions = widget.metadata.panels[0].items; dimensionTitles = dimensions .map(i => i.jaql.title); // Add each dimension title to the dimension dropdowns dimensionTitles.forEach(function(title, index) { $('.dimensionDropdown', element).append( '<option value="' + (index + 1) + '">' + title + '</option>' ); }); bookmarks = widget.metadata.panels[1].items; bookmarkTitles = bookmarks .filter(i => !i.disabled) // keep only not disabled .map(i => i.jaql.title); // extract title // Add each existing bookmark title to the bookmark dropdown bookmarkTitles.forEach(function(title) { $('#bookmarkDropdown', element).append( '<option value="' + title + '">' + title + '</option>' ); }); // Disable placeholder values from selection dropdownClasses.forEach(cls => { $(`.${cls}`).each(function () { let $select = $(this); if (!$select.is("select")) { $select = $select.find("select"); } if ($select.length === 0) return; $select.find("option").first().prop("disabled", true); }); }); });Extending pivot widget panel limits in Sisense using user groups
Overview Pivot widgets in Sisense are often used to explore and visualize complex datasets with multiple dimensions and measures. In some scenarios, users need to build very large pivot tables with many rows, columns, values, or filters. However, pivot widgets enforce internal panel item limits that can restrict how many fields and dimensions can be added to each panel. While these limits are useful for protecting performance, load time, and usability in general, they can become a constraint for advanced users working with large datasets or detailed analytical models. At the same time, organizations may want to apply different limits for different groups of users rather than increasing limits globally for everyone. This use case describes a Sisense plugin that automatically increases pivot panel limits as widgets load, with support for both a global default and optional overrides based on user group membership. The challenge By default, pivot widgets in Sisense can reach panel item limits that prevent users from adding additional dimensions or measures. This can affect: Analysts building large exploratory pivot tables Power users working with wide schemas or detailed hierarchies Dashboards that rely on complex pivots with many fields Manually adjusting widget configuration is not scalable, especially when dashboards contain many pivot widgets or when widgets are opened independently outside a dashboard. In addition, organizations often want different limits for different user groups, rather than applying a single global setting. It is important to note that if a very large number of dimensions are used in an individual pivot widget, that widget may have an extended load time. What the solution does The PivotMaxPanelItems plugin automatically sets the panel.metadata.maxitems value for every panel in pivot-type widgets as they load. At a high level, the plugin: Applies only to pivot widgets Updates all pivot panels (rows, columns, values, filters) Works for widgets inside dashboards and for widgets opened directly Supports a configurable default limit for all users Supports optional overrides based on Sisense user group membership The plugin runs on dashboard and widget load events, ensuring that pivot panel limits are applied consistently without requiring manual changes to individual widgets. Role and group-based configuration The plugin can apply different panel limits depending on the user’s Sisense group membership. This allows organizations to: Grant higher limits to advanced users or analysts Keep more conservative limits for general users Control behavior centrally through configuration If a user belongs to multiple configured groups, the plugin applies the first matching group based on the order defined in the configuration file. If no group matches, the default limit is used. This approach provides flexibility while keeping behavior predictable and easy to manage. How it is used Configuration is handled through a simple configuration file included with the plugin. Administrators can define: A default maximum number of items per pivot panel Optional overrides for specific Sisense user groups Once configured and installed, the plugin will likely require minimal ongoing maintenance in most circumstances. It applies automatically whenever pivot widgets are initialized. The full plugin is attached as a Zip file to this article and is available to download. The code is not compressed or obfuscated, and can be modified as needed, or used as example code for similar plugins. The plugin can be installed as a standard plugin by placing the decompressed folder into the plugin folder. The plugin includes a Readme file with further information. Why it’s useful This approach allows organizations to remove artificial constraints on pivot widget design while still maintaining control over performance and usability. Key benefits include: Enabling larger and more flexible pivot tables Reducing manual widget configuration and rework Applying consistent behavior across dashboards and standalone widgets Supporting different usage patterns across user groups Centralized control through a single configuration file The solution is particularly valuable in environments where advanced users need more flexibility without changing defaults for all users. Outcome With the PivotMaxPanelItems plugin in place, pivot widgets can support more dimensions without manually adding widget scripts. Advanced users gain the flexibility they need, while administrators retain control over limits at the group level through simple configuration. By applying limits automatically and consistently at load time, the plugin ensures predictable behavior across dashboards and widgets, supporting scalable group and role-aware analytics and visualization in Sisense. Screenshots Without a plugin, if a panel type includes too many items, the Add Panel Button is hidden With the plugin, the Add Button does not disappear when below the new limit, as many dimensions and fields as needed can be added to the widget.
Last 4 digits formula
I am an ESO user and am trying to create a widget that will show only the final 4 digits of an incident number. The beginning of the incident numbers are not always the same but the last 4 are what I need. In excel I use =right(A2,4). I then use this list in excel with an =if formula (=if(c3-c2=1,"","missing") to show me any incident numbers that were not reported. If there is a way to make this part of the widget also, it would make my job that much easier. Thank you.
Filtering Orders by Grouped Filter Criteria with Optional Nulls
Afternoon SS community, We need a filtering mechanism in our business where users can apply filter groups to retrieve specific orders. Each filter group consists of three fields: location_id, category_id, and client_id. When a filter value is NULL, it should behave as a wildcard and match all values for that field. The goal is for users to select one or more filter groups at a time and retrieve all matching orders — without returning duplicates, even if the same order matches multiple groups. Currently, I can filter by individual fields, but I’m unsure how to structure things to support grouped filters like this without requiring users to select each field individually. The business requirement is to let users select entire filter groups, not individual filters. Sample Data: order_id location_id category_id client_id 1 1 1 1 2 1 1 2 3 2 2 3 4 2 3 4 5 2 1 4 Filter Group Examples: Group 1 location_id: 1 category_id: 1 client_id: 1 Expected Result: Order 1 only Group 2 location_id: 1 category_id: 1 client_id: NULL Expected Result: Orders 1 and 2 Group 3 location_id: 2 category_id: NULL client_id: NULL Expected Result: Orders 3, 4, and 5 How can I design a filtering logic that: Accepts multiple filter groups (with NULLs acting as wildcards) Returns all matching orders Avoids duplicate orders if they match more than one group Any guidance or sample query structure would be greatly appreciated! Thanks in advance.Seeking Best Practice for Live Detail Reporting in Sisense (Replacing SSRS)
Afternoon Sisense community, Our team is looking to replicate the functionality of a crucial SSRS report within Sisense. This report is used by a department to obtain a detailed list of jobs for a specific month. The workflow involves: Running the report for a selected month (typically the current or previous month). Reviewing the output for discrepancies. Updating the source system based on the review. Re-running the report immediately to verify the changes (requiring live data). Current Sisense Implementation & Performance Issue I've attempted to recreate this report's dataset using a Live Model connected to a Redshift SQL View. The view is complex: It contains approximately 50 columns of detailed data. It involves JOINs across 15 different tables to consolidate all necessary dimensions and metrics. The Issue: The performance of this Live Model is unacceptable. Users are accustomed to the SSRS report running a stored procedure and returning the filtered data in under 30 seconds. My Sisense Live Model is timing out. Constraints & Goal Requirement: The data must be live (no ElastiCube, as users need immediate reflection of system changes after updates). Target Performance: Sub-30-second return for monthly filtered data. Request for Guidance Given the high number of columns, multiple joins, and the strict requirement for live data with fast filtering (specifically by month), what would be the recommended best practice for implementing this detailed report in Sisense? Are there specific Sisense configurations, data modeling techniques for live connections that would address this performance bottleneck while meeting the "live" requirement? Thank you for your insights!Dashboard not auto-refreshing for mobile app performance data
Hi everyone, I’m using Sisense to track performance metrics from my mobile entertainment app — like daily active users, watch duration, and session time. Lately, I’ve noticed that some dashboards don’t refresh automatically, and I have to manually reload them to see updated results. I’ve already checked the data connector and refresh schedule, but everything looks fine there. Could this delay be due to caching, or maybe a dashboard setting I’m missing? Would appreciate any suggestions or settings to look into — thanks in advance!
CASE WHEN Statements
We are using CASE WHEN statements for our customers looking to create their own booleans. We use live connection so these cannot be added to the elasticube. We are looking to complete the following: CASE WHEN [Timeline Type] = "emergency department" AND [Vital Systolic Blood Pressure] < 90 THEN 1 ELSE 0 END Additionally, customers would like to know the percentage of time the above is yes. Can this be completed in the function area or do we need to handle this in our transformation pipeline?
How to hide a row in a pivot table?
Hello. I am looking to hide (not filter out) a row based on its content in a pivot table widget. I haven't been able to find a native functionality for that, but I assume there's javascript to do that. Can anyone assist? I've only been able to find column masking, but that isn't helpful.Product Update | Asset Auditor incorporates user access, permissions, and asset sharing
A more user-centric Asset Auditor In this release, we’re excited to introduce a user-focused expansion of the Asset Auditor, including two new dashboards, Users and Users Validation, along with enriched underlying data. You can now easily understand: Who has access and permissions to which data assets How assets are shared across your organization Whether access could be impacting engagement Revealing how their access and permissions connect to your data assets With this release, you get a clearer, more actionable picture of how people and assets interact to empower better oversight. We’ve also made major improvements across the existing dashboards to integrate this new data, elevate insights, and provide more actionable recommendations. Assets can’t deliver value unless users can access and engage with them In Sisense, dashboards and data models are governed by separate access controls. And they don’t operate in silos. They’re shared, cloned, embedded, and repurposed across teams. When someone shares a dashboard, they may not have permission to share the underlying model. The result? Users open dashboards expecting insights, only to find missing charts or blank visuals. They’re unsure whether the data is broken, restricted, or simply unavailable, while the sharer assumes everything is fine. The Asset Auditor gives clear visibility into which users or groups have: Access to dashboards but not to the underlying data models Access to data models but no corresponding dashboard access No access to any dashboards Yet access alone doesn’t guarantee adoption, and adoption issues are often misdiagnosed as access problems. Are dashboards underused because people truly lack access? Or because they simply aren’t engaging with the content? By surfacing these mismatches, you can prevent confusion, improve collaboration, and ensure every shared dashboard delivers the full experience it’s meant to. By detecting both over-permissioning and under-permissioning, you can tighten governance without slowing productivity. Permission drift happens quietly, introducing operational risk long before it becomes visible Do users have the correct permissions? Do some users have too many permissions? Do users have permissions to data models or dashboards that they shouldn’t? Use the Asset Auditor to see whether users have the right level of access: too little to be effective, or too much for their role. Identify misaligned configurations, such as users who maintain data model access for development or testing, but no corresponding dashboard access, which is a strong indicator that permissions no longer reflect the real workflow. By detecting both over-permissioning and under-permissioning, you can tighten governance without slowing productivity. Understand the reach of your dashboards across users and groups The Asset Auditor helps you understand the reach of your dashboards across users and groups, revealing how far each asset spreads and where engagement actually concentrates. Detect and reduce redundancy, find duplicates or overlapping assets shared across teams. Pair these insights with Sisense Usage Analytics to understand not just who can access assets, but who actively engages with them. By bringing these signals together, teams can zero in on whether the problem is permissions, visibility, or user behavior. The Asset Auditor provides much more data and insights beyond users and shares! Check it out and get smarter about how you manage your data assets. If you want to start getting better visibility into what your assets are doing inside your environment, reach out to us for a live demo or a free trial.
