In previous blogs, we showed how businesses were benefiting from Power BI in ways that were not possible previously.
What makes Power BI such a revolutionary analytical solution are technology innovations that were introduced in Excel.
As Gil Raviv notes, Power BI, Excel, and SQL Server Analysis Services share the same analytics engine, Power Pivot, which was introduced as the PowerPivot add-in to Excel 2010. Power Pivot, says Raviv, is “a game-changer in self-service BI.”
High-Performance Engine
Power Pivot is based on an in-memory technology called VertiPaq, which arranges data into columns and compresses it to speed performance. In-memory computing speeds processing by storing data in RAM rather than on slower-performing disks.
As Microsoft explains, Power Pivot is a data modeling tool that works hand in hand with Power Query, data extraction and transformation engine that began life as Data Explorer. Power Query enables you to discover, import, and merge data from any number of sources, while Power Pivot enables you to model, manipulate, and operate on the data you have imported.
Power Query can be embedded in Excel or the Power BI Desktop, and the tool can load a data set into an Excel spreadsheet or into Power Pivot for data modeling.
Power BI Ecosystem
The story gets even better. The tight integration between Power BI and Excel, which extends to Azure, Office 365, SQL Server, and other Microsoft technologies, creates an ecosystem that makes Power BI a flexible and formidable analytical tool.
Power BI and Excel also share the Data Analysis Expressions (DAX) language, which can be used to create formulas to operate on your data. DAX runs on top of the VertiPaq engine, whose official name is the “xVelocity in-memory Analytical Engine.”
You can apply DAX formulas to columns of data to perform calculations. For example, you can use DAX to calculate anticipated sales based on a percentage increase over last year’s sales or to calculate sales rank or customer rank by sales. You also can perform time analysis functions such as year-to-date sales, actual sales vs. targeted sales, and current sales.
Automating Analyses
Power BI provides a host of features to simplify and speed up analytical operations. You can use Calculated Columns, which are formulas that are applied to joined columns of data. For example, you can calculate profits by joining sales and costs columns.
You also can use Measures, which are formulas that are applied dynamically to perform calculations as you interact with your reports. Measures are typically used to calculate aggregations such as average sales amount or average sales by region.
You can use Power BI to track your business’s sales and marketing performance by creating key performance indicators (KPIs) that enable you to evaluate the current value and status of a metric against a defined target. The KPIs can be updated automatically and the data can be visualized for easy understanding.
You also can use Power BI’s Quick Insights feature to evoke a machine learning engine that will instantly apply algorithms to your data sets to discover hidden insights such as correlations, outliers, and trends.
Data Visualization
Another core strength of Power BI is the tool’s data visualization capability. As Gil Raviv notes, Microsoft built an HTML 5 compliant visualization engine for Power BI that can slice and dice data in any direction and provide visualizations of the results.
Besides enabling a myriad of visualizations out of the box, Power BI has a library of custom visualizations users can draw upon. This highlights another strength of Power BI: the large and growing user community that contributes to the ongoing innovation centered on Power BI as it evolves.
Analytical Advantage
Power BI accommodates a wide spectrum of users, enabling novice users to work at a basic level while enabling more sophisticated users to work within the Power BI Desktop to employ formulas and perform complex calculations and analyses.
Power BI enables small and mid-size organizations to gain the advantages of advanced analytics that were beyond their scope in the past. This includes visualizing predictive analytics that can be employed using Power BI linked to Microsoft Azure Machine Learning.
Predictive analytics can be used to optimize marketing and sales initiatives, such as predicting how customers will respond to new products, as well as helping to identify the best channels and messaging for campaigns. Machine learning can help identify potential customers, personalize marketing approaches, predict customer churn, and much more.
Real-Time Analytics
As Microsoft explains, Power BI also lets you access real-time information, which can be used to identify trends, optimize pricing, and take advantage of market opportunities that arise. Power BI and Azure Stream Analytics can be combined to create real-time IoT dashboards for monitoring sensors. Uses include tracking and monitoring instruments, devices, packages, greenhouse environments, market fluctuations, and real-time monitoring of machinery data streams that can be analyzed to predict failure.
Microsoft has done an excellent job of making it easy and affordable for non-technical users and power users to gain the benefits of enterprise-grade analytics. And with the continual contributions of the Power BI community, the story grows better day by day.
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