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!
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.
