Building and Visualizing Reports in Mine-Weather

Now that the weather data is ready to be used by the Mine-Weather reporting tool, decision makers can focus on building reporting templates and presenting them via reporting dashboards to the end users. Mine-Weather system uses different types of reporting formats, filtering and data grouping approaches to accurately measure different forecasts under a range of weather conditions of interest. Decision makers use the Mine-Weather analytical framework to compare different weather forecast service providers based on the weather parameter score. These scores have been calculated by comparing forecast to the actual value of each weather parameter.

Mine-Weather uses highly customizable reporting frameworks that play an important role in predicting appropriate weather service forecast for a given weather parameter. Mine-Weather reporting framework constitutes two primary roles the Report Builder and the Report End User.

Report Builder

Report Builder is a subject matter expert who prepares the final reporting package on the visualization tool to generate reports. Data is readied using data filters, data connections, calculated metrics, hierarchies, then packaged as worksheets and combined with respective dashboards as per the analyst’s requirement.

Report End User

Report End User are the recipients of the reporting package who can view the reports online or using reporting tools.

Report Worksheet, Dashboard and Storyboard are the 3 main reporting entities available in the reporting framework.

Report Worksheet

Dashboard

Storyboard

Report Worksheet is a report building canvas that enables report builder’s setup interactive chart elements to be consumed by the end-user on the dashboards or storyboards. Dashboard is the presentation canvas where the report builder optimizes and positions various components in the report. Storyboard constitutes a set of dashboards laid out in a chronological order that can be navigated by the end users. End users can apply interactive filters to the dashboards to get a better insight of the weather parameters of interest.

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Interactive filters play a critical role in slicing and dicing the reports and further drill down into the data sets to reveal specific areas of the report. Report builder gets an insight into the multifaceted dimensions that are critical to the business, much earlier to the requirements analysis stage. This learning is incorporated into the reports design by way of interactive filters. Each of these filters perform their operations based on the relational hierarchy defined in the database. Download our WhitePaper to delve into the functional details of Mine-Weather analytical reporting framework.

A good report design identifies the most requested scenarios and presents complex data sets into easily identifiable charts and graphs. Mine-Weather uses Tableau reader a free desktop application that can be accessed to view and interact with data visualization built in Tableau Server. End users can access the story boards and dashboards published to Tableau Server safely and securely from any browser or mobile device or embedded to any existing web site. Also, powered by the Tableau Online a cloud-based data visualization solution for sharing and distributing storyboards and dashboards. Mine-Weather’s analytical reporting framework provides a host of data visualization solutions to work with rendering flexibility to its end users.

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About the Author

Dr. Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc.. Ron heads up all company strategic activities, and directs day-to-day work of the Leadership Team at Macrosoft. Ron is also Macrosoft’s Chief Scientist, defining and structuring Macrosoft’s path forward on new technologies and products, such as Cloud; Big Data; and AI. Ron has a Ph.D. in Theoretical Physics from New York University, and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, NJ, and at Argonne National Laboratory. Ron also worked at Bell Laboratories in Murray Hill, NJ., where he managed a group on Big Data, including very early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including: predictive analytics; data mining, machine learning and neural networks.

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