How Decision Systems 2.8.3 integrates with Data Viewer and Explorer
We’re excited to announce that Data Viewer and Explorer are now fully integrated into Decision Systems with version 2.8.3. Why This Matters Data Viewer helps investigators and analysts explore and filter data efficiently during investigations. Data Viewer uses the Explorer API to query data sources and present them in a structured, interactive format. This means: Investigators and analysts can automatically replicate score logic, saving time, minimizing errors, and delivering scale. Score logic is more easily represented, for intuitive filtering of relevant transactions. Complex scores are more easily investigated. How Data Viewer and Explorer work with Scores When an investigator selects a score, Data Viewer and Explorer can display all transactions that triggered that score. Transactions can be shown via queries or transaction IDs. Using queries allows investigators to see the queries in Explorer for deeper investigations beyond the original score. Benefits of integration with Decision Systems Faster implementation: Saves ~0.5 days per score—no longer need to set up Data Viewer and Explorer manually. Reduced maintenance: Queries are automatically sent to Data Viewer and Explorer—no need to manually replicate score logic or update UI queries after tuning. Accuracy and confidence: Automated integration ensures correct transactions are displayed to investigators in Data Viewer and Explorer. Improved user experience: Queries can be explored and amended in Explorer; Data Viewer now supports all scores. Explainer videos Watch these short videos to learn more: 01 Data Viewer and Explorer See how the integration appears in the UI and learn how to configure Data Viewer and Explorer. 02 Data View Integration Setup Understand the data ingestion requirements for using Data Viewer and Explorer effectively.
Achieve a True 360° View with Quantexa Unify for Microsoft Fabric, now GA
Today, we announced the General Availability (GA) of Quantexa Unify, an AI-powered integration within the Microsoft Fabric platform that connects fragmented data at scale with speed and precision. Read our step-by-step guide on how we’re helping users create AI-ready, entity-resolved data in minutes that can deliver true 360 degree views directly into end-user applications for smarter decision-making. An enterprise 360-degree view gives organizations comprehensive visibility of their operations. It involves creating unified views of an organization's data across all departments, systems, and processes, helping integrate, analyze, and act on data holistically for better decision-making, risk management, and customer insights. It underpins industry use cases such as Customer 360 in marketing intelligence, Citizen 360 in government or Patient 360 in healthcare. With OneLake in Microsoft Fabric, organizations have a strong foundation for unifying and delivering data. OneLake collates data across silos and delivers it with ease directly into your data pipelines and business operations, overcoming challenges organizations have faced with traditional Master Data Management approaches. This includes issues like servicing distinct business needs and data acquisition strategies with disparate systems, while maintaining data consistency. However, more data can mean more duplicates, incompletions and inconsistencies which can disrupt pipelines, and prevent accurate data reaching your business when you need it. For this, Quantexa Unify, a native Microsoft Fabric Workload, now GA, adds a simple-to-use entity resolution capability. It is grounded on proven experience in the world’s largest and most demanding organizations as part of the Quantexa Decision Intelligence Platform, fundamental to applications like customer intelligence, risk identification, and event monitoring where accuracy and context matters. Let’s explore how Quantexa Unify for Microsoft Fabric can deliver de-duplicated data, through dashboards, notebooks and data pipelines and platforms, drawn from across your Fabric data estate. Providing accurate, contextual enterprise 360-degree views of individuals, organizations, locations, and other real-world entities in your Fabric tenant, ensuring PII and your data privacy compliance. Run the native workload Add the Unify workload to your Fabric tenant. From the Microsoft Fabric home page, click the Workloads button in the left navigation page. From the Add more Workloads section, click the Quantexa Unify workload to add it to your tenant. Once added, the workload appears on the My Workloads section on the same page. You are good to go! Before you create your project, ensure the Data Sources you want to use with the workload are in a suitable format to upload as lakehouse objects to Fabric. Now select the data sources. Unify will analyze the metadata, and identify the content of the data fields, then automatically map those fields to pre-defined entity resolution models. Now in a few clicks we can organize our Enterprise 360 view through optimizing data mapping and selecting our entity resolution preferences for matching that meets your needs and schemas. We’ll start by mapping data across the data sources in OneLake. Once your Data Sources are mapped, you can now resolve and create matched Entities by running an Iteration where the power of entity resolution is highlighted. In the video, we selected the default option. A sales team or an investigation team may prefer more open-ended “fuzzy” record matching view while a finance team or a marketing team adhering to GDPR may take a “strict” view. The process takes only a few minutes, with most time spent on the computational overheads of setting up the jobs. Whether handling 1 row, a thousand rows or 100 million, execution time grows only marginally, delivering near-constant performance at scale. The Quantexa Platform has been proven at 60 billion+ record volumes. It scales to the size of your OneLake data estate. You - the user - control the capacity (amount of compute), and hence you can control the performance. Power your Microsoft Fabric data pipeline Now take your accurate, deduplicated data into your end-user applications, for example: Power BI for dashboards and analysis Fabric Data Warehouse for data warehouse teams and administering data products Fabric Notebooks for data science and reporting, including Python and R Microsoft Copilot-enabled Fabric data workflows Without entity-resolved data, issues arising from unmatched, disconnected data could lead to: duplicated entries skewing statistics incorrect classifications and categorizations false positives arising from seemingly independent records that were not. Instead, when building your next data product, or using Microsoft’s predict function in a Fabric notebook, or populating your Power BI dashboard, ensure your data is AI-ready with entity resolution and Quantexa Unify. Get Started Today! Find the workload on Microsoft Fabric!
Define Your Upgrade Approach
Having created your Upgrade Roadmap, the next critical decision is to define how you will execute the work. There are two fundamentally different strategies for upgrading your Quantexa platform: the Incremental Upgrade and the Reset Upgrade. This page will guide you in selecting the right approach for your specific context, considering factors like your roadmap's complexity, your solution's health, and your team's capacity. At a Glance: Comparing the Approaches Approach Key Characteristic Best For... Primary Benefit Key Consideration Incremental Upgrading through each major version sequentially ("hopping"). Solutions that are well-aligned with Quantexa best practices and are only a few versions behind. Efficiency. Maximises automation using the Repository Tool. The Repository Tool is less effective on solutions with high technical debt. Reset Creating a new, clean repository on the target version and migrating code selectively. Solutions that are many versions behind or contain significant technical debt. Clean Slate. An opportunity to remove technical debt and realign to best practices. Higher initial manual effort to set up and selectively migrate logic. In-Depth: The Incremental Upgrade This is the most common and efficient approach. It involves upgrading your existing repository through each major version between your current state and your target destination. For example, moving from v2.5 to v2.8 would involve separate, sequential upgrades to v2.6, v2.7, and finally v2.8. This approach makes maximum use of Quantexa's Repository Tool, which is designed to automate much of the migration effort between sequential versions. This is the default and recommended option if you are only upgrading a single major version (e.g., from 2.7 to 2.8). When performing an incremental upgrade across multiple versions, you have two release strategy options: Option 1: Single Go-Live Perform each version "hop" in quick succession within your development environment, but combine them into a single release package. This package goes through one cycle of SIT, UAT, and Production deployment. Benefits: Reduced Testing Overhead: One round of formal SIT/UAT saves significant time and effort. Faster Time-to-Target: Achieves the final goal of deploying the target version in the shortest overall project duration. Developer Efficiency: Developers build deep expertise by performing the "hops" back-to-back, increasing speed. Option 2: Staged Go-Live (Release per Hop) Treat each version "hop" as a mini-project. You develop, test (SIT/UAT), and release each incremental version to Production before starting the next hop. Benefits: Lower Risk per Release: Each deployment is smaller and more contained, simplifying testing and root cause analysis if issues arise. Incremental Value: End-users can benefit from new features and improvements more regularly. Increased Flexibility: Allows your team to address other business priorities between upgrade stages. In-Depth: The Reset Upgrade This approach involves creating a brand-new, empty repository on the target platform version. Your team then selectively migrates or completely rebuilds the required logic (data interfaces, Entity Resolution, Scoring, etc.) from the old repository into the new one. This is a strategic choice to "pay down" technical debt. Benefits: Your implementation is multiple major versions behind the latest Quantexa Platform release. The current solution contains a high degree of technical debt, making an incremental upgrade complex and risky. You need to make significant architectural changes or re-align to modern Quantexa best practices. While this approach requires more upfront manual effort, it ensures that your repository is clean, modern, and optimized for future scalability and smoother upgrades. How to Choose Your Approach Your choice will depend on the specifics of your Quantexa deployment and the goals of your upgrade. If your solution is well-aligned with best practices and your Upgrade Roadmap involves only one or two hops, the Incremental approach is almost always the best choice. If your solution suffers from significant technical debt or your Upgrade Roadmap spans many major versions, the Reset Upgrade approach offers a powerful opportunity to reset for the future, even if the initial effort is higher. Quantexa's Divergence Tool can help provide insights into how far away from best practice the deployment is in certain areas The output of this step is a clear, documented decision on the approach you will take. Action: State your chosen upgrade approach. Example: We will follow an Incremental Upgrade approach with a Single Go-Live release strategy.
