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Unify: How the workload can help you
This page provides an overview of the Quantexa Unify workload for Microsoft Fabric and how it can help you in your data projects. Overview of the Quantexa Unify workload The Quantexa Unify workload brings a critical data transformation component into the Microsoft Fabric ecosystem: Entity Resolution. The Unify workload is built on the industry-leading AI-driven Entity Resolution component of Quantexa’s Decision Intelligence Platform. As a result, the workload empowers data teams by enhancing data quality and usability, eliminating data silos, and allowing you to connect data at scale. How can the Unify workload help you? The Unify workload delivers best-in-class Entity Resolution, providing deeper contextualization and refinement of your datasets compared to traditional record-matching methods. It also simplifies data management and allows you to integrate and update data from multiple sources continuously. Entity Resolution through the Unify workload quickly and easily elevates the data on which you base your data analysis and real-world decision-making. This helps you unlock deeper insights and make smarter decisions with ease. For more information on Entity Resolution in the Unify workload, see Unify: What the workload does. Why should I use Unify instead of other Entity Resolution tools? By using the Quantexa Unify workload, you will benefit from Quantexa's industry-leading Entity Resolution capabilities. Additionally, key features of the Unify workload include the following: No-code interface that allows users of all types to benefit from the workload. Automated data mapping. Advanced Entity matching, including the ability to adjust the ‘strictness’ of Entity matching between Iterations. End-to-end Entity Resolution processing that can complete in under one hour. Scalable for high-volume datasets and many multiples of datasets. Outputs data into tables that you can use to build Semantic Models or to enhance your data analytics, for example within Power BI and other applications. Outputs deduplicated, AI-ready data that can be used, for example, for Machine Learning and AI models in Fabric. Helps you identify quality issues through Power BI reports. Seamless integration into your Fabric project. Low-friction sign-up process with minimal onboarding requirements. Supports team collaboration within a single platform. In short, the Quantexa Unify workload helps you easily and quickly create a trusted, connected, and contextualized data foundation. IMPORTANT: While the Unify workload can function in Fabric on a F1 or F2 capacity, Quantexa recommends using a F8 capacity or greater. How the Unify workload fits into the Fabric ecosystem When you first add the Unify workload, you are provided with a Demo version of the workload that only allows you to use the Data Sources that Quantexa provides. On requesting a Full User license, you are then provided with full access to the workload. This allows you to use your own Data Sources and run the full workload within your Fabric tenant. An example workflow that shows how Unify fits into the Fabric ecosystem is as follows: You have Data Sources that include customer and supplier information. Therefore, before using the Unify workload, you use OneLake to connect and centralize access to your Data Sources. You connect multiple Data Sources within Fabric. Although your Data Sources contain customer and supplier information, there is no customer key or unique ID to indicate which references are to the same individuals or companies. Therefore, you use the Quantexa Unify workload to match references to the same individuals and companies across your Data Sources and create a unique ID for each individual and company. This is your ‘resolved’ data. Following on from the Unify workload, you could use your resolved data in the following ways: Data warehouse specialist: To aggregate your data in a Fabric Data Factory flow. Power BI engineer: To combine data from your Data Sources into visualizations in Power BI. Data scientist: To develop a machine learning model using Fabric Notebooks. The preceding example are just three in a vast range of scenarios of how you can use your resolved data downstream from the Unify workload. Next steps If you are working with datasets of any size that would benefit from Entity Resolution, try the Quantexa Unify workload. You can test out or purchase the workload in the following ways: Demo version: This is a free preview open to all Fabric users that allows you to test out some of the workload’s key features. In this preview, you can only use the Data Sources that Quantexa provides. To access the Demo version of Unify, also known as the Public Preview version, click here. NOTE: To access the link, ensure you are logged into your Fabric account in your browser. Full User and Trial versions: The Full User version provides you with full access to the Unify workload, including allowing you to use your own Data Sources. You can access the Full User version directly through a license subscription. Additionally, you can also gain temporary access to the Full User version of Unify through a Trial version. This allows you to explore all the workload features on a temporary, unpaid license. To purchase the Full User version or access the Trial version of Unify, contact UnifyAndFabric@Quantexa.com. To find out more about Entity Resolution and the Unify workload process, see Unify: What the workload does.🎥 Webinar - Q Labs: An Introduction to Innovation at Quantexa
In this session Dan_Higgins, Chief Product Officer and Cat_Mackay, Product Innovation Manager, introduce Q Labs. Q Labs’ mission is to solve industry problems by accelerating groundbreaking ideas while providing a path from conceptual ideas to production-ready capabilities that drive customer value. Through collaboration opportunities between Quantexa experts, customers, and partners, Q Labs is pushing technology to the limits with bold solutions. During the session, Dan goes into detail about what innovation means at Quantexa and how we’re accelerating the art of the possible. Cat then demos our latest Agentic AI innovation project and how you can get involved with Q Labs. On the agenda: Innovation & Quantexa Welcome Q Labs How to get involved A firsthand look at our latest innovation project – Building trusted AI Agents that enhance decision making in verticalized use cases👀 Have feedback on a Q Labs initiative? Submit your feedback and influence innovation at Quantexa!Unify: Core concepts
This page describes the core concepts underpinning the Quantexa Unify workload. Overview The Unify workload is built on the Entity Resolution component of Quantexa’s industry-leading Decision Intelligence Platform. This provides Unify with best-in-class Entity Resolution capabilities: all available in a few clicks inside your Fabric tenant. The concepts listed on this page are fundamental to understanding the Unify workload’s capabilities and Quantexa’s Entity Resolution features within the workload. Core concepts The following sub-sections describe core concepts underpinning the Unify workload. Entity An Entity is the representation of a real-world person or object, such as a customer or bank account. Quantexa distinguishes Entities from their real-world counterparts to make clear that Entities are simply compiled from data points found in the Data Sources you provide. Entity Type An Entity Type is a category of Entity. Entity Resolution in the Unify workload recognizes the following Entity Types: Individual Business Telephone Address Account Entity Group An Entity Group provides further refinement of Entities within an Entity Type. For example, a Telephone Entity may contain a landline entry as well as a mobile phone entry. Both the landline and mobile phone numbers are each Entity Groups within the Telephone Entity Type. Quantexa provides several predefined Entity Groups within the Unify workload. Entity Resolution Entity Resolution is the process of identifying Entities within your Data Sources by finding various and likely disparate occurrences of that Entity across the available data. Based on your use case and data quality, you can adjust the strictness threshold for matching, known as the Matching Level, that the workload uses for Entity Resolution. The Matching Level impacts the level of Overlinking or Underlinking. These concepts are explained below. REMEMBER: You can view metrics about the resolved Entities in, for example, PowerBI, or using the workload’s output tables or Delta Lake files in OneLake. → Matching Level A Matching Level is a strictness threshold for matching that Unify refers to when deciding whether to resolve Entity references. You must specify the Matching Level that the workload should apply to an Iteration. For each Iteration, you can choose one of the following three Matching Level options: Default: The standard Matching Level that applies to most use cases, striking a balance between Overlinking and Underlinking. Overlinking and Underlinking are explained below. Fuzzy: A looser Matching Level that casts a wider net. It enables more matches to be found, but may result in some Overlinking. Strict: A stricter Matching Level that only resolves Entity references where there is strong confidence that the match is correct. It ensures no incorrect matches are made, but may result in some Underlinking. For further information on Matching Levels, see Unify: A closer look at selected key features. → Overlinking Overlinking occurs when multiple references are incorrectly linked to the same Entity, even though they refer to different real-world Entities. An Overlinked Entity is an Entity that is incorrectly resolved with one or more other Entities. Overlinking is typically caused by similarities between the records of different Entities, such as two separate customers having the same name and even address. → Underlinking Underlinking occurs when two or more references to the same real-world Entity are not linked in the dataset. An Underlinked Entity is an Entity that is only partially resolved. Underlinking is typically caused by missing or incorrectly entered data, such as one customer being listed multiple times in one database under different names or addresses, and with no other data to connect those references. Project A Project is one instance of your Unify workload. It is a collection of Data Sources you have uploaded that you can then use for various Iterations. Version History All changes to a Project, such as the upload of new Data Sources, are automatically recorded in the Version History. You can view the history of your changes by clicking Version History under your workload's Home tab. Data Source A Data Source is a Lakehouse Table in OneLake, which you can create from a file you upload or from another source in OneLake. You upload your Data Sources to a Project within your Unify workload. You can upload multiple Data Sources to your Project. However, you can only upload one Data Source at a time. In the Demo version of Unify, you cannot use your own Data Sources. Instead, Quantexa provides example customer Data Sources for the following two fictional product brands: Contoso Northwind Each one contains example data such as names, addresses, and telephone numbers for customers of the brand, but each file has a different schema and columns, reflecting the diversity and messy data typically encountered in an organization. In Full User and Trial versions of Unify, you may use your own Data Sources. These may contain your organization’s internal data or external data from third parties, such as corporate registries or watchlists. Data Mapping The Data Mapping process is an automatic process that runs when you upload a Data Source to your Project. It does the following: Analyzes the uploaded Data Source’s contents. Uses an inference engine to determine the appropriate data schema. For example, a field containing names is mapped to the Individual Entity Type. From this, it then maps the component parts of the field to the appropriate Entity Groups within that Entity Type, such as Forename or Surname. Applies the necessary parsing, cleansing, and standardization of your raw input data. For further information on this, see the definition for Parsing, Cleansing and Standardization on this page. After the mapping process is complete, a Data Mapping panel lets you view and refine the results of the process. You can also view data quality metrics for the raw input data. For further information on Data Mapping, see Unify: A closer look at selected key features. For guidance on reviewing and editing the initial Data Mapping output, see Unify: Step-by-step guide to using the workload. Parsing, Cleansing, and Standardization The Unify workload parses, cleanses, and standardizes your Data Source data automatically as part of the Data Mapping process. It uses Quantexa’s Machine Learning model to do so. Parsing splits source data into its component parts. For example, parsing a raw full name data entry of Michael Greene creates a Forename = Michael and Surname = Greene . Cleansing manipulates the raw data to prepare it for optimal Entity Resolution. For example, removing generic terms such as Ltd or Organization , and removing punctuation and default values. It also converts all data to uppercase. Standardization replaces different presentations of the same data with a single version for consistent formatting. For example, a dataset may contain USA , AMERICA , UNITED STATES , or UNITED STATES OF AMERICA in the country field. Standardization converts all of these to US . The main purpose of parsing, cleansing, and standardization is to create consistent data that facilitates linking through Entity Resolution. Iteration An Iteration is the execution of Entity Resolution for a Project at a specific version. You can select a different set of Data Sources for each Iteration, which may help you identify the Data Sources that provide the highest quality of Entity data. An Iteration execution submits a series of automatic background jobs to do the following: Resolve and build Entities. Generate the resulting Entity data as Lakehouse tables, which you can view in OneLake or Power BI. These typically include tables for the different Entity Types, and a table containing links between the records and the resulting Entities to show how the Entities have been built from your Data Sources. You can also view a Semantic Model showing the relationships between the output tables. When executing an Iteration, you can select the Matching Level you want to use when resolving Entities. For further details, see the definition for Matching Levels in this document. For further information on Iterations, see Unify: A closer look at some key features. Semantic Model The Semantic Model output by an Iteration shows the relationships between the input and output tables of that Iteration. For further information on Semantic Models in Microsoft Fabric, see Power BI Semantic Models in Microsoft Fabric. For further information on Semantic Models and other automated Unify outputs, see Unify: A closer look at some key features. Next Steps For a guide to using the Unify workload, see Unify: Step-by-step guide to using the workload. For an applied example of the step-by-step guide, see Unify: Example workflow.Unify: A closer look at selected key features
This page provides further detail and guidance on some key features of the Unify workload. Overview The features and capabilities explained on this page are those you will encounter as you use the Unify workload. For a step-by-step walkthrough on setting up and using the Unify workload, see Unify: Step-by-step guide to using the workload. Features The following sub-sections provide further detail on some of Unify's key features. Data Mapping For a definition of Data Mapping, see Unify: Core concepts. Data Mapping is an integral part of Quantexa’s Entity Resolution solution. Quantexa’s Data Mapping process in the Unify workload focuses on mapping your Data Sources to pre-defined Entity Type and Entity Group fields. In the context of the Unify workload, Data Mapping seeks to answer some initial questions about your Data Source such as the following: What source fields match the Unify Entity attribute fields? Which should they be mapped to? For source fields that do not directly match Unify’s pre-defined Data Mapping fields, what are the most suitable matches? If there are no suitable matches, why? What Entity Types and Entity Groups are being populated by the source data? To what percentage are these fields being populated? As noted in the step-by-step walkthrough, while you may edit the Data Mapping process output, the process itself runs automatically on loading a Data Source. This saves significant time and manual effort. However, to ensure accurate Data Mapping in Unify, your data must be in a suitable format and have some logical structure for the mapping process to read it effectively. Iterations For a definition of Iteration, see Unify: Core concepts. Running an Iteration serves two purposes: Conducting Entity Resolution on the Data Sources you include for that Iteration. Comparing Entity Resolution outputs across multiple Iterations that use different Data Sources, or different combinations of Data Sources. In addition to comparisons on the data content, an Iteration can help you compare data quality, Entity Resolution metrics and field population rates between your Data Sources. The first scenario is straightforward, and thanks to Quantexa’s Entity Resolution features within the Unify workload, you can use the workload to build a trusted data foundation directly. The second scenario would be more complex without the Unify workload, as it would require a significant investment of time and resources to conduct a true comparison. However, with the Unify workload, the complex is made simple. You simply run multiple iterations using the straightforward step-by-step process. Matching Levels For a definition of Matching Level, see Unify: Core concepts. The availability of Matching Levels helps you tailor Unify’s Data Mapping and Entity Resolution processes to your Project’s needs. As a reminder, there are three available Matching Levels within the Quantexa Unify workload: Default, Fuzzy, and Strict. The following are example use cases for Fuzzy and Strict Matching Levels. Fuzzy: You can use a Fuzzy Matching Level in a scenario like matching customers to a watchlist in the Financial Crime arena. Due to the seriousness of the matter, you want to ensure you find all possible matches. Even where there is Overlinking, you are happy to manually review the matches to find the correct ones. Strict: You can use a Strict Matching Level in a scenario like generating a master set of customers in Master Data Management. As the output may be used to trigger automatic action, such as contacting customers, and you are unlikely to review the matches, you want to ensure that all generated matches are correct. Even where there is Underlinking, you are happy to have a smaller scope of matches given the reputational and practical consequences of any incorrect matches. The following factors can help you decide which Matching Level to choose at the Data Mapping stage and for each Iteration: The quality of your Data Source. The completeness of your Data Source. Your particular use case. For example, if you are planning to use the Entity Resolution output to execute automated tasks without reviewing all matches, it may be better to use a Strict matching level. For cases where you want to ensure you have all possible matches, even with overlinking, you may want to use a Fuzzy matching level. If you are not sure which Matching Level to use, you can opt for the Default Matching Level, as this strikes a balance between Overlinking and Underlinking. Automated output After completing an Iteration, the Unify workload automatically outputs the results of the Entity Resolution process into the following: Iteration summary The summary shown for an Iteration after Entity Resolution is a bar-chart in the top-right corner. The bar chart shows a comparison between the total number of input Records against the total number of resolved Entities for each Entity Type. Power BI Report The automatic report shows summaries of key information for Entity Types, such as Entity size, Entities by Address and Entities by Business and Individual counts. Entity Resolution records tables and Entities tables Records tables show the records that triggered the resolution of a particular Entity. For example, the workload outputs multiple tables showing the relevant records for a particular Entity. Each record table covers a specific Entity type, such as Individual or Address. Entities tables show the Entities the source data has resolved to. For example, you may input two Data Source tables, and after Entity Resolution, the workload outputs multiple additional tables showing the resolved Entities. Each table covers a specific Entity type, such as Individual or Address.Entity Resolution records and Entities tables. Semantic Model An Iteration’s Semantic Model shows the relationships between the tables described in the preceding point and your input Data Source tables, within an Iteration. For further information on Semantic Models in Microsoft Fabric, see Power BI Semantic Models in Microsoft Fabric. Additionally, using the automatic outputs, you can optionally create other outputs within the broader Fabric suite, including the following: Other types of Power BI reports Power BI is a functionality provided by Microsoft Fabric, and not by the Unify workload. Power BI reports are typically based on one Semantic Model and can feature visualizations such as charts, graphs and tables to provide data insights. They can help you explore your data – and the output of Unify – further. For more information on Power BI reports, see Reports in Power BI. Notebooks Power Query (M script) with Dataflow Gen2 Next steps For a guide to using the Unify workload, see Unify: Step-by-step guide to using the workload. For an applied example of the step-by-step guide, see Unify: Example workflow.Unify: How the workload can help you
This page provides an overview of the Quantexa Unify workload for Microsoft Fabric and how it can help you in your data projects. Overview of the Quantexa Unify workload The Quantexa Unify workload brings a critical data transformation component into the Microsoft Fabric ecosystem: Entity Resolution. The Unify workload is built on the industry-leading AI-driven Entity Resolution component of Quantexa’s Decision Intelligence Platform. As a result, the workload empowers data teams by enhancing data quality and usability, eliminating data silos, and allowing you to connect data at scale. How can the Unify workload help you? The Unify workload delivers best-in-class Entity Resolution, providing deeper contextualization and refinement of your datasets compared to traditional record-matching methods. It also simplifies data management and allows you to integrate and update data from multiple sources continuously. Entity Resolution through the Unify workload quickly and easily elevates the data on which you base your data analysis and real-world decision-making. This helps you unlock deeper insights and make smarter decisions with ease. For more information on Entity Resolution in the Unify workload, see What the Unify workload does. Why should I use Unify instead of other Entity Resolution tools? By using the Quantexa Unify workload, you will benefit from Quantexa's industry-leading Entity Resolution capabilities: Industry Recognition By using the Quantexa Unify workload, you will benefit from Quantexa’s industry-leadingEntity Resolution capabilities. Additionally, key features of the Unify workload include the following: No-code interface that allows users of all types to benefit from the workload. Automated data mapping. Advanced Entity matching, including the ability to adjust the ‘strictness’ of Entity matching between Iterations. End-to-end Entity Resolution processing that can complete in under one hour. Scalable for high-volume datasets and many multiples of datasets. Outputs data into tables that you can use to build Semantic Models or to enhance your data analytics, for example within Power BI and other applications. Outputs deduplicated, AI-ready data that can be used, for example, for Machine Learning and AI models in Fabric. Helps you identify quality issues through Power BI reports. Seamless integration into your Fabric project. Low-friction sign-up process with minimal onboarding requirements. Supports team collaboration within a single platform. In short, the Quantexa Unify workload helps you easily and quickly create a trusted, connected, and contextualized data foundation. How the Unify workload fits into the Fabric ecosystem When you first add the Unify workload, you are provided with a Demo version of the workload that only allows you to use the Data Sources that Quantexa provides. On requesting a Full User license, you are then provided with full access to the workload. This allows you to use your own Data Sources and run the full workload within your Fabric tenant. An example workflow that shows how Unify fits into the Fabric ecosystem is as follows: You have Data Sources that include customer and supplier information. Therefore, before using the Unify workload, you use OneLake to connect and centralize access to your Data Sources. You connect multiple Data Sources within Fabric. Although your Data Sources contain customer and supplier information, there is no customer key or unique ID to indicate which references are to the same individuals or companies. Therefore, you use the Quantexa Unify workload to match references to the same individuals and companies across your Data Sources and create a unique ID for each individual and company. This is your ‘resolved’ data. Following on from the Unify workload, you could use your resolved data in the following ways: Data warehouse specialist: To aggregate your data in a Fabric Data Factory flow. Power BI engineer: To combine data from your Data Sources into visualizations in Power BI. Data scientist: To develop a machine learning model using Fabric Notebooks. The preceding example are just three in a vast range of scenarios of how you can use your resolved data downstream from the Unify workload. Next steps If you are working with datasets of any size that would benefit from Entity Resolution, try the Quantexa Unify workload. You can test out or purchase the workload in the following ways: Demo Version: This is a free preview open to all Fabric users that allows you to test out some of the workload’s key features. In this preview, you can only use the Data Sources that Quantexa provides. Click here to access the Demo Version of Unify. Full User and Trial: The Full User version provides you with full access to the Unify workload, including allowing you to use your own Data Sources. You can access the Full User version directly through a license subscription. Additionally, you can also gain temporary access to the Full User version of Unify through a Trial version. This allows you to explore all the workload features on a temporary, unpaid license. To purchase the Full User version or access the Trial version of Unify, contact UnifyAndFabric@Quantexa.com. NOTE: To access the link, ensure you are logged into your Fabric account in your browser. To find out more about the Unify workload, see What the Unify workload does.A post containing sensitive data
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