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Today, I’m excited to share that for the second year in a row, Microsoft has been ranked far to the right on full vision among the quadrant leaders in Gartner’s 2017 Magic Quadrant for Business Intelligence and Analytics Platforms – Microsoft’s tenth ranks as the leader of the leader quadrant for a year in a row.
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We’re honored by this recognition for the innovations we’ve made with Microsoft Power BI over the past year as well
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Growth in both vision and execution since the 2016 report, but more importantly we are encouraged by the progress we have made as a community towards the ambitious goal set some time ago in July 2015 when Power BI launched: bringing BI to more people than ever before. in all roles and processes in the organization.
Power BI is a modern Business Intelligence solution that you can rely on. We will continue to deliver innovation and value with precision and speed. Our motivation is “five minutes to delight” – we continually focus on getting a user signed up for Power BI in five seconds and gaining business value from the service in five minutes. Customers around the world are realizing this vision and leveraging the promise of self-service BI and making it a reality at scale.
This customer-centric approach includes regular Power BI development and delivery cycles. Since launch, we’ve delivered a storm of innovation, including weekly service updates and monthly updates to the powerful BI Desktop builder. This proposal is based on very valuable feedback from the community – over 200,000 active community participants who, since July 2015, have submitted over 6,000 suggestions that have directly influenced the evolution of the market. We listen, evaluate and act for our community and we are proud of it. We couldn’t reach these heights without the support and feedback of our loyal users.
With all these exciting innovations behind us and the company’s incredible recognition, we’re not slowing down to continue to deliver on our “Five Minutes to Delight” promise and bring the value of data analytics to even more users. Thank you for being part of our journey. We hope to break more boundaries and push the needle even further in data visualization and discovery.
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If you are interested or want to learn more, I would like to invite you to join us in June in Seattle for the second annual Microsoft Data Intelligence Conference. We invite advanced BI users from around the world to meet with the BI technical team, attend sessions, and connect with other data enthusiasts. Learn more now and register for the Microsoft Data Intelligence Summit website. By clicking Continue to join or log in, you agree to the User Agreement, Privacy Policy and Cookie Policy.
Power BI is a relatively young product that joined the Microsoft family as a data visualization and analysis tool in 2015. Although it briefly advertises the “Ask questions, get answers” function – equipped with natural language processing – the main advantage of Power BI is the ability to visualize, analyze and combine data, e.g. with Dynamics CRM, Salesforce, Excel, Oracle, etc.
In the years 2015-2017, the so-called data-rich products thanks to increased data processing power and stable network technology. To use data, some companies are starting to look for specialists who will solve their problems, modernize IT systems or implement data analysis tools. Gradually, people also realized that the more information there is, the more difficult it is to do it. That’s why we need more “intelligence” to help companies interpret data.
First, advanced BI data cleaning and preparation features, as well as interactive data visualization capabilities, allow users to perform multiple tasks with one tool. This means that the company does not have to invest heavily in various software and resources.
New Power Bi Composite Models: Revolutionary Business Intelligence
Secondly, as a Microsoft product, Power BI can take advantage of other cutting-edge technologies on the Microsoft platform, such as Azure Knowledge Services and AutoML functions. Given that many organizations already have a Microsoft Enterprise subscription, Power BI allows users to sign in with the same credentials they already use at work.
Third, the learning curve of Power BI is not long as the user interface is similar to other Microsoft products such as Excel, Word, etc. What else do you need to know? Drag and drop data sources and visualizations, or add slicers, page filters, or report filters to better understand your data from different perspectives. You can also learn on your own using Microsoft’s many online learning tools.
From the Power BI dashboard, you can ask simple questions about your data and Power BI will try to find answers. For example, in the screenshot below, I typed “average revenue by year” and a line chart appeared.
Mark posts as not useful if you consider them irrelevant or unrelated to the article. This response is private and will not be shared publicly. In December, Microsoft launched a very interesting “Power BI Composite Models” feature in its Power BI reporting and analytics software.
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This is great news because it allows you to extend enterprise data models for unique reporting and analysis purposes while maintaining a direct connection to the underlying model.
In Marco Russo’s talk on this topic, he sums it up this way: “In the absence of a better solution, ‘save to Excel’ is always the best solution.” There is no single architectural solution that will solve this problem once and for all. To the present.” Our Director of Professional Services, John Gamble, is adding a new feature, explaining why he thinks it’s a big deal in the world of data and analytics.
“An assembly model, or to give it its full name Direct Request to Power BI and Analysis Services, has the ability to edit or extend a previously published data model (or cube) hosted in Power BI or Azure Analysis Services for a specific or one-time function while maintaining connection to existing model. Starting with Power BI, you can directly connect to an existing Power BI model or cube, build an amazing report or dashboard, and then – and this is the most important thing – link in the model to a new, completely separate database and write math on the linked model.
This allows you to take advantage of all the processing and aggregation capabilities of the existing Enterprise model and then extend them locally without interfering with the Enterprise model. If you want to report that the translation service for Cube you are connected to is not allowed, you do not need to use the “default” data export to Excel (or other tool). This can result in extracting hundreds of thousands of rows, storing the file locally on your workstation, and then adding the necessary calculations to the raw data. You can then also publish this new extended report back to BI Power for others to use.
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This feature solves many problems that hinder the development of data use in the company. The summarization, analysis, and benefits of using cubes and data models used by tools like Power BI have long been recognized as a way to provide business users with greater access to important corporate databases (for example, finance, customer marketing, etc.). Defining cubes and models along with the infrastructure and data collection processes required for them can take a lot of time and effort.
As we all know, change is constant and it can take time to go through all the development, testing and management stages required to update your company’s reporting platform. It is often not seen as useful if it involves adding two sets of new statistics or perhaps the ability to adapt one set of data to another as a one-off task. Typically in this scenario, IT gets involved and someone extracts a large amount of data, which is usually placed into Excel for further processing. This leads to end-user dissatisfaction as they lose all the power of the cubes and reporting platform; and new data sets become static, stored in a file system or, worse yet, in emails across the organization, creating a GDPR headache as data goes literally everywhere.
Here’s the scenario. A retailer may have a sales data set that describes every value and price of every item sold, along with attributes that define the product, sales channels, store, salesperson, customer, supplier, and sales time. A well-designed dynamic BI data model, or Cube Analytics, will enable them to break down sales metrics and insights across all of these attributes. It is possible that there may be many millions of them
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