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In this case study:
Background
Enverus is an Austin-based energy analytics company that aims to provide data-driven guidance for the oil and gas industry. Among their many different solutions is a portfolio of power demand and pricing forecasts for utility markets, which are organized into seven different offerings. The Power Analytics offering consists of three reports, each of which are designed to support trading activity for power traders and analysts.
Problem
The Power Analytics reports are produced through a series of interdependent processes that require significant manual intervention. The data is initially sourced through more than 130 comma-separated value files, which are dropped onto an FTP server at regular intervals throughout the day. An Enverus analyst must manually run a batch script within a given time period in order to transfer these files into a local directory, which is then linked to a macro-enabled Excel workbook. The workbook consists of more than 80 tabs and contains approximately 275,000 formulas, many of which contain hard-coded cell references. Macros are utilized to archive data in different tabs, which frequently overwrite historical data.
Additionally, visualizations are interspersed throughout the file among datasets and are subject to unintentional resizing based on data volume. These visualizations are then linked into PowerPoint files, which require an analyst to export into a PDF and manually e-mail to the customers.
Solution
Enverus came to Quisitive looking for a solution. After a working session to analyze their reporting process, we recommended the Power BI Quick Start in order to rapidly deliver a proof of concept. Quisitive focused on a small subset of source data with the intent of reproducing one report for one target market. Instead of consolidating the data into one self-referential Excel table, the Quisitive team sourced the data directly into Power BI and organized into a data model. This new structure allowed previously complex formulas to be rewritten with DAX in clear, semantic language. In addition to a tenfold increase in refresh time, the proof of concept revealed many logical discrepancies in the original system.
Results
The Power BI Quick Start engagement not only successfully demonstrated the capability of Power BI, but also provided a working prototype for enhanced reporting. The Power Analytics team was so impressed with the results, that they decided to move forward on implementing this prototype as the framework for their entire Power Analytics reporting portfolio.
