If you already have your data but you need it cross-referenced, queried, grouped, sorted, filtered, conditional rules applied, or just lots of math we can help.
A simpler example with timecard punches:
Our client was provided with timecard punch logs in several formats and needed to minimize the time it takes to read the entire log, convert the punches into a day’s activity and then confirm breaks, meals, and overtime rules were followed. The process needed to be repeatable and fast, here’s what we did:
- Using Python with Pandas, we loaded the data so that we manipulate it programmatically.
- Wrote a test to determine the log file format for each version we received.
- Developed and tested the rules necessary to present the data in a consistent format that met the client’s requirements
- Grouped punches by day
- Corrected end shift punches after midnight
- Filtered employee punches and administrative punches separately
- Created conditional rules to apply to anomalies in the punches
- Subtotaled minutes worked and break times
- Flagged entries where the rules were not followed
Our client now has a repeatable process that saves hours per punch log and takes minutes to run, while reducing the chance of error.
It gets deeper, migrating data from Ringtail to Relativity:
Our client had previously moved review platforms from Ringtail to Relativity. Active cases were exported and imported from the respective platforms using their provided tools. Time passed and so did Ringtail, When the client needed to migrate additional cases they no longer had access to the Ringtail platform.
Our goal was to use file and database archives for Ringtail, converting them to a format Relativity could use. To make this happen, we:
- Created a temporary SQL environment where the archives could be restored
- Analyzed the schema, of all the tables and fields to determine where the data we needed was.
- Wrote queries joining tables together to organize data into logical functions
- Retrieved each query programmatically to transform data into the desired format
- Wrote rules as needed to correct differences between the two platforms and in client practices over the years.
- Created Relativity DAT and OPT, along with extracted text files to prepare the data for loading into Relativity.
In the end we had a process that could now convert their archive data into a usable format in minutes with minimal labor. Besides saving time, our client now had one less thing to worry about in the future.
