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Despite its release eight years ago, however, most financial analysts still do not know how to use Power Pivot, and many do not know it even exists. Power Pivot offered next-generation business intelligence and business analytics functionality to Excel in its ability to extract, combine, and analyze almost limitless datasets without processing speed impairment. In 2010 however, Microsoft added a new dimension to Excel, called Power Pivot.

More specifically, older-generation Excel’s infrastructure and processing limitations such as its row-limit of 1,048,576 rows, or inevitable processing slow-down where large data sets, data tables and interconnected spreadsheets are concerned, reduced its usability as a effective big data tool. With the arrival and exponential growth of big data, however, driven by decades of data aggregation and accumulation, the advent of cheap cloud storage and the rise of the internet of things-i.e., eCommerce, social media, and the interconnectedness of devices-Excel’s legacy functionality and capabilities have been pushed to their limits. The Emperor’s New Clothes: Power Pivot TutorialĪcross the various fields and sub-sub-fields that span finance, financial analysis, financial markets, and financial investing, Microsoft Excel is king.
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Power Pivot was created in direct response to the big data demands of contemporary business intelligence needs, which prior generations of Excel-given their 1,048,576 row limit or processing speed shortcomings-struggled to cope with.According to Microsoft, " Power Pivot enables you to import millions of rows of data from multiple data sources into a single Excel workbook, create relationships between heterogeneous data, create calculated columns and measures using formulas, build PivotTables and PivotCharts, and further analyze the data so that you can make timely business decisions without requiring IT assistance.".Introduced to Excel 20 as an add-on, but now native to the application, Power Pivot is part of Microsoft's business intelligence stack capable of (but not limited to) big data analytics work without specialty infrastructure or software.
