Overview of business intelligence April '19 release
These release notes describe functionality that may not have been released yet. To see when this functionality is planned to release, please review What’s new and planned for business intelligence. Delivery timelines and projected functionality may change or may not ship (see Microsoft policy).
This topic describes the theme areas for the April '19 release.
Power BI makes it very simple to derive insights from transactional and observational data and then get those insights into the hands of every employee to support decision making. This helps organizations create a data culture where every employee can make decisions based on facts, not opinions. Through integration with PowerApps and Microsoft Flow and embedding in applications, we close the loop from insight to action.
We’re making investments in five areas key to driving a data culture:
Simple, fast, intuitive experiences that get every employee motivated to participate.
Support for large-scale, enterprise-wide BI with self-service.
Enabling agile, self-service, big data prep with Azure so business analysts can participate in the synthesis and enrichment of digital signals and so organizations can accumulate data of analytical value in Azure Data Lake Storage.
Pervasive application of AI to make the inherently difficult task of determining what truly matters easier for business users by automatically uncovering hidden insights, and helping business analysts with data preparation.
Enabling solution developers to embed insights within the applications where actions are taken.
Simple, fast, intuitive experiences
We will focus on:
A streamlined content consumption experience. Streamlined viewing, navigation, and collaboration for end users. As Power BI is increasingly used for large-scale enterprise deployments, usability for end users is critical. With a redesign of the consumption interface in Power BI, end users will have a simpler, more intuitive experience. Additional features such as personal user bookmarks, commenting on reports, and improvements to Power BI Home will provide additional capabilities highly requested by end users.
Richer authoring control, ensuring the best experience for your users. With the ability to drill through to another report in the Power BI service, report authors will be able to create separate reports that drill into a specific area. End users can navigate between reports with the filter context passed along with it. In addition, authors will have full control over the filtering experience in Power BI. This includes hiding specific filters from end users, locking filters from being modified as well as control over the look and feel within the report. Formatting options will continue to evolve—from usability additions including PowerPoint-like smart alignment guides on the canvas to powerful expression-based formatting, leveraging the DAX formula language to dynamically format visual properties in the report.
Performance profiling of reports. Report authors will be able to profile their reports created in Power BI Desktop using the Performance Analyzer. This will provide visibility into load time of reports and how that time is being spent, and offer tips to improve to ensure the best experience for end users of the report.
Support for large-scale, enterprise-wide BI with self-service
Enabling enterprise-grade semantic models. Building an enterprise-wide BI solution is more complex than departmental or self-serve use cases and it demands more of the platform. By adding support for the XMLA protocol, the data models in Power BI can be accessed by pretty much any BI tool. XMLA support also brings the community of Application Lifecycle Management tools for SQL Server Analysis Services to Power BI. The Power BI Desktop will have a redesigned relationship view, optimized for models with a large number of tables. In addition, SAP connectors will now enable end users to modify variable selection both in Power BI Desktop and in the Power BI service.
Worldwide scale. Multinationals must operate globally, while also ensuring that local regulations and performance needs are met. With Premium Multi-Geo, we allow capacity to be deployed in any of our nine public geos to ensure data residency.
Agile, self-service big data prep with Azure
Data preparation is the most expensive step in BI, typically accounting for 60-80% of the cost of a typical BI project. Today we have a powerful data preparation tool, Power Query, that’s a shared experience across Excel, Power BI, PowerApps, and Flow. While it’s a great tool for many self-service scenarios, it has constraints in terms of enterprise manageability and scale.
The data preparation logic is bound to a single BI data model, so it’s not reusable.
Scale is limited by a single computer on which the logic was created.
Data is not schematized so applications cannot easily leverage the data.
We are making self-service data preparation more manageable for the enterprise with:
Reusable data-prep. The data preparation logic is elevated as a first-class artifact that can be reused across multiple BI models.
Big-data scale. We are addressing the scale limitations to enable business analysts to ingest, transform, integrate, and enrich big data.
Common Data Model. Business analysts can bring data in Common Data Model (CDM) form within Power BI.
Extensibility through Azure data services. We will land data in Azure Data Lake Storage Gen2 to facilitate collaboration and reuse between business analysts, data engineers, and data scientists.
We’re fueling collaboration across roles by unifying access to data between Power BI and Azure Data Lake Storage. Business analysts can seamlessly operate on data stored in Azure Data Lake Storage with the self-service capabilities in Power BI, while data engineers, data scientists, and other professionals can extend access to insights with advanced analytics and AI from complementary Azure data services like Azure Data Factory, Azure Databricks, and Azure Machine Learning.
For example, data engineers can add, enrich, and orchestrate data; data scientists can build machine learning models; and business analysts can benefit from the work of others and the data available in Azure Data Lake Storage while continuing to use the self-service tools in Power BI to build and share insights broadly.
Pervasive application of AI
AI can aid in data exploration, comb through the data to automatically find patterns, help users understand what the data means, and predict future outcomes to help business drive results.
Power BI has been a pioneer in applying AI through capabilities such as natural language, which enables users to get answers by asking questions in plain English, or Quick Insights, which automatically finds patterns in data. We’re making another major step forward in bringing AI to business intelligence and delivering several new AI features in Power BI:
- Q&A is simpler than ever with a better autosuggest and “Did you mean” feature to catch and correct natural language queries.
- Users can now get capabilities such as image recognition and text analytics directly in Power BI.
- Key driver analysis helps users understand what influences key business metrics.
- Users can create machine learning models directly in Power BI using automated machine learning.
- Users now have seamless integration of Azure Machine Learning within Power BI.
All these new AI capabilities require no code. This enables all Power BI users to discover hidden, actionable insights in their data and drive better business outcomes with easy-to-use AI.
Enabling solution developers
Power BI service apps to enable customers and partners to create, share, market, sell, and grow their business faster. We will enable a Power BI user to become a 'service app creator’ that can create and package analytical content. The resulting package can be deployed to other Power BI tenants through AppSource, or through a self-maintained web service.
Power BI Embedded analytics enables developers of SaaS services and enterprise portals to embed stunning interactive reports and dashboards in a fraction of the time and cost. We will add more security, scalability, and features to support application lifecycle management for data models, to make embedded analytics ready for the enterprise. We will improve the interaction between embedded analytics and the hosting application or web portal. We will enhance capacity management for scaling resources and improve monitoring by integrating with common Azure tools for health, availability, and usage. We will also bring Power BI to Visual Studio to allow developers to easily integrate Power BI code and ship their applications faster.