Mobile Data Collection Output

Mobile Data Collection Output

Mobile Data Collection Output

Recently, we took a look at some best practices for mobile data collection at the input level. This week, we turn our attention to best practices at the output level. Following the critical input stage, data is often transferred to corporate departments or regional marketing teams which, in turn, proceeds with the output—that is, drawing insights and making merchandising decisions based on data analysis.

Of course, there are many ways to optimize this crucial decision-making stage through intelligent data publishing, exporting, aggregating, and filtering methods. The common practice of using spreadsheets is proving to be a more and more laborious and imprecise way to manage the output stage of data collection.  So what does a more sophisticated mobile data collection system look like at the output level?

For one thing, the right tool logically structures and aggregates disparate field data from different locations and input administrators so all the particulars needed for analysis are in one place. Graphical representation of reports, historical data extracts, and other data of interest is easy to access and does not require knowledge of complicated formulas or code. Elegant infographics, charts, and graphs should be at your fingertips, to make presenting data analysis to colleagues, executives or board members seamless. With the right mobile data collection tool, the time consuming practice of scrambling for and collating input is over; a robust system offers a smooth, productive and pain-free path to actionable insights.

When you are looking for a specific detail in data, a smart filtering system is a must. Improved mobile data collection software should provide a simple way to filter by location, date, survey response, photo, employee, and more. In this way, customization is of utmost importance across the tool’s functionality, both at the input and output level. Retail teams work differently, even within the same company, and the right system should embrace these differences, allowing managers in charge of output the opportunity to analyze data instinctually instead of being forced to adhere to a method of standardization.

One such software would be GoSpotCheck. It offers a system that enables managers to analyze data in this way. The system wholeheartedly embraces best practices like logical structuring, smart filtering and simply-presented data at the output level. That way, GoSpotCheck can offer the mobile data collection software best suited for your company, your unique working style and today’s advancements in retail operations.

GoSpotCheck and other similar apps take advantage of current technology to effectively gather data for larger purposes, such as big data analysis.

Matt Talbot

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