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.FAQ-style chatbot with BloX: use case of AI Assistant
Introduction While Sisense AI features (Simply Ask and the newer Dashboard Assistant) support free-text questions, outcomes can vary depending on factors such as data model quality, business terminology, and user familiarity. In practice, this can result in inconsistent questions, ambiguous phrasing, or less predictable results, especially for less technical users or in environments with less than ideal data models. This use case focuses on how Sisense BloX was used to create a guided FAQ-style interface that triggers the AI chatbot automatically, providing a more controlled, consistent, and user-friendly experience. This solution was implemented for a financial technology company to support users with a wide range of recurring business questions related to multi-asset trading and order management. What the solution does This solution uses BloX to create a guided AI chatbot experience. Instead of typing questions manually, users select a question from a dropdown of predefined FAQs and submit it with a button click. BloX then automatically opens the AI chat window (Simply Ask or Dashboard Assistant), populates the question, and submits it to the chatbot. Questions can be defined directly in the BloX code or sourced dynamically from a data model, which allows the team to manage and update the list of supported questions over time. From the user’s perspective, the experience feels like interacting with an FAQ. Under the hood, the AI chatbot handles the analysis and response. Why it’s useful Lower barrier to entry for AI features By guiding users through predefined, curated questions, the solution reduces ambiguity and removes the need to worry about phrasing, terminology, or syntax. This results in more consistent, predictable, and accurate answers, making AI insights accessible to a broader audience, including users with varying technical backgrounds and less mature or optimized data models. Fewer widgets and dashboards to maintain Not every user needs answers to every possible question. By centralizing common questions into a single guided AI experience, the team avoids creating and maintaining excessive widgets and dashboards for individual analysis, improving performance and reducing long-term maintenance effort. Attachments FAQswithSimplyAskOrAIAssistant.dash.txt (example dashboard using the Sample ECommerce cube) BloXActionsForAI-FAQs.zip (BloX actions' scripts) BloXTemplatesForAI-FAQs.zip (BloX templates for the FAQ widgets, also included in the .dash file above). Note: Remove the .txt extension before importing the dashboard (.dash) file.
Data prep essentials for AI-driven analytics - part 3
Discover how to improve data quality for AI and machine learning. In Part 3 of our Data Preparation series, learn how to fix missing values, remove duplicates, correct data types, and standardize formats—with SQL and Python scripts to help you clean your data for accurate, AI-ready analytics.
Have you heard about Sisense Intelligence?
Sisense Intelligence is a new suite of AI-powered tools that help product managers and developers build smarter, more intuitive analytics experiences. Features like natural language dashboards, automated insights, forecasting, and data explanations accelerate every stage of the analytics journey. Designed for seamless integration, it empowers teams to deliver fast, user-friendly insights at scale.
Use dashboard API to get data in python dataframe
I have a widget which displays users with points and I want to fetch top 10 users from that widget.Also I need to select current month "01/2025" from the filter.Let me know how this can be done But I'm getting weired error.Please help! I'm an admin so I have a token from my profile section. import requests url = "https://bp.sisense.com/api/v1/dashboards/4577fc4415dd7d003339bfdc/widgets/4577c7716dd7d554441bjae" # authentication headers = { "Content-Type": "application/json", "Authorization": "" } # Send the request response = requests.get(url, headers=headers) response # Check if the request was successful data = response.json() data Error: {'error': {'code': 5002, 'message': 'Invalid token.', 'status': 401, 'httpMessage': 'unauthorized'}}
