There are literally hundreds, if not thousands, of new software tools out there for business intelligence and data analytics. But the most popular reporting application remains good old-fashioned Microsoft Excel.

First debuting in 1987, Excel has been used by billions of users worldwide, and while it's sometimes dismissed or bemoaned by developers and IT personnel, the truth is, it's still an essential product in any data engineer or analyst's toolbox. Even in a company that specializes in data software, we use spreadsheets nearly every day (primarily via Google Sheets) and we reject the mindset that all Excel files must be replaced operationally by web dashboards or BI tools (though, under appropriate circumstances, we can help with that too).

Especially in the early stages of developing a report or working with small data sets, spreadsheets can be an ideal medium, and they're familiar to most non-engineer end-users, which is a huge advantage when it comes to effective adoption of reporting and analytics initiatives. Specialized tools almost always require dedicated training and support, but most professional workers in 2020 have at least a baseline level of Microsoft Office proficiency.

Like any technology-driven business solution, spreadsheet-based reporting has its drawbacks, principally that it doesn't scale particularly well, which is to say that it can become arduous and time-consuming to produce Excel-based reports as they grow in size, or need to be updated more frequently. There can also be issues with managing multiple versions or vintages of a spreadsheet report, and if they're being prepared manually, there's the constant risk of human error (not to mention a fair amount of likely frustration).

If you have an Excel or Google Sheets-based report that has become challenging to produce, or is routinely needed on-demand by users in your organization, don't worry: you may not have to transition that report to a more expensive or complicated tool just yet. Your team can still make the preparation process more efficient by programmatically scripting the generation of these files. Anecdotally, we've found a lot of executives are satisfied and most comfortable with the spreadsheet reports they receive currently, but their teams are burdened with the repetitive tasks of data sourcing, collection, aggregation, and formatting.

So here's another option: using a simple, custom-configured Python application, and an open-source statistical library like pandas, the underlying data for a given report can be pulled or "extracted" from a wide variety of source systems, including enterprise apps and the web, and then manipulated or "transformed" before being loaded into a relational database, file bucket, or other common cloud storage destinations. This extract-transform-load or "ETL" process is part of an automated "data pipeline" that can be scheduled to run on a defined interval, at a pre-determined time, or upon the occurrence of certain trigger events.

From there, the Python application can be instructed to process, compile, and/or distribute spreadsheet reports as needed, saving your team countless hours and ensuring the accuracy and consistency of the resulting spreadsheet files. For some clients, we've launched dedicated web portals where executive users can go to securely download the latest reports. And recently, Earlybird was able to save one auto retail client nearly a dozen hours each week for their in-house financial controller, time savings that individual was able to dedicate to other work (tasks that actually require the critical thinking and analysis of a human worker).

Curious about how we might be able to streamline your operations through automated reporting? Let's talk. You can get started with automated reporting solutions from Earlybird for as little as $5,000, with initial implementation (for most use cases) in under 100 days.