Analytics with Power BI
1. Introduction
Power BI brings the predictive power of advanced analytics to allow users to create predictive models from their data, enabling organizations to make data based decisions across all aspects of their business.
Through machine learning, computers are able to act without being explicitly programmed. Instead, they can teach themselves to grow and change when exposed to new data. Once the work of science fiction, machine learning is rapidly becoming part of our daily lives—through practical speech recognition programs, more effective web searches, and even self-driving cars. Using Azure Machine Learning Studio, users can quickly create predictive models by dragging, dropping, and connecting data modules. Power BI then allows users to visualize the results of their machine-learning algorithm.
1.1 Integration with R
R, a programming language used by statisticians, data scientists, and data analysts, is the most widely used statistical language in the world. R integration in Power BI brings this language into all stages of generating insights. Using the R connector, users can run R scripts directly in Power BI and import the resulting data sets into a Power BI data model.
R in Power Query performs advanced data cleansing and preparation asks, such as outlier detection and missing values completion. R visuals in Power BI allows to visualize data by gaining endless flexibility and advanced analytics depth. Once the visuals are created, it can share the R visuals in reports and on dashboard, where they are interactive and cross filterable.
Power BI users do not need to have a background in working with R to leverage everything that R can do, such as prediction, clustering, association rules, and decision trees. R custom visuals allow users to apply the power of R without writing one line of R. Just import a custom R visual to the report, and drag the data to update the report.
Because R is run directly in the Power BI service, reports using R can be shared with and viewed by anyone—even if they don’t have R installed.
2. Visualizations in Power BI
Data visualizations allow interacting with the data to find business insights. Power BI lets to choose from a list of available visualizations, add a custom visualization to create, or select from our expanding list of available visualizations in the community gallery.
Community-sourced visualizations
Power BI has a visuals gallery with many useful visualizations created by both the community and Microsoft, which can be downloaded and use in the Power BI reports. To add a community-sourced visualization to the report, visit the visuals library on the Power BI site. On the Welcome to Power BI custom visuals page, browse the gallery. Select a visual tile to see more information about that visual, and download the visual to use.
Custom visualization in Power BI allows to create full custom visuals to add to reports or submit to the Power BI community for others to use. A custom visual using Power BI developer tools can be created, which let us design and test a custom visual by writing custom visual Typescript code and creating CSS.
3. Data connection and shaping
DirectQuery
DirectQuery allows building visualization over very large data sets, where it would otherwise be unfeasible to import all of the data. Traditionally, underlying data changes can require a refresh of data. For some reports, the need to display current data can require large data transfers, making re-importing data unfeasible. Users can avoid this issue by using DirectQuery to query live against the data source.
When using DirectQuery, it is important to consider the performance and load the data set, the security, and available supported features. All DirectQuery requests are sent to the source database, so the time required to refresh a visual depends on how long the back-end source takes to respond with the results from the query. Power BI creates queries that are as efficient as possible, but there are conditions under which a generated query may not be efficient enough and the performance may be impacted or the refresh would fail. This situation can be mostly avoided by using columns with a cardinality below 1 million.
Security should also be considered when using DirectQuery in the Power BI service. All users who consume a published report connect to the back-end data source using the credentials entered after publication to the Power BI service. This is the same situation as data that is imported: all users see the same data, irrespective of any security rules defined in the back-end source. Finally, not all Power BI Desktop features are supported in DirectQuery mode, or the feature may have some limitations. In addition, some capabilities in the Power BI service (such as Quick Insights) are not available for data sets using DirectQuery.
Data Analysis Expressions
Data Analysis Expressions (DAX) is a collection of functions, operators, and constants that can be used in a formula or expression to calculate and return one or more values. DAX helps to create new information from data that is already in the model. Learning how to create effective DAX formulas will help get the most out of the data. It is quite easy to create a new Power BI Desktop file and import data into it. It can even create reports that show valuable insights without using any DAX formulas at all.
DAX formulas provide this capability and many other important capabilities as well. Learning how to create effective DAX formulas will help get the most out of the data. When we get the information we need, we can begin to solve real business problems that affect the bottom line. This is the power in Power BI, and DAX will help get there.
Here is an example of DAX in action.
Previous Quarter Sales = CALCULATE (SUM(Sales[SalesAmount]), PREVIOUSQUARTER(Calendar[DateKey]))
This formula will calculate the total sales for the previous quarter, depending on the filters applied in a report. For example, if we put SalesAmount and our new Previous Quarter Sales measure in a chart, and then added Year and QuarterOfYear as slicers, we would get something like this:
Power BI business analytics tools enable users at all levels of an organization to analyze data and share insights. Through dashboards, Power BI provides a 360-degree view of the most important metrics—in one place, updated in real time, and available on all of the devices. With one click, explore data using intuitive tools to quickly find answers and uncover new insights. Power BI allows digging deep into the data, while being productive and creative with what is build. With more than 20 built-in visuals and a gallery of vibrant custom visualizations, Power BI makes it easy to use advanced analytics to effectively communicate the message and address business challenges.
References:
https://powerbi.microsoft.com/en-us/data-analytics/
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