REDUCING MANUEL WORKTIME BY USING AUTOMATED DATA WORKFLOW
Working with company data is very important to make processes more effective but take often many ressources if it is done manually. Hereunder I describe a real company case and outcome by using automatisation.
I did some work on an automatization task for a client, which had some challenges working with a data set from an energy supplier, where the challenge was the amount of time spent on the manual filtering, classification and finally necessary graphical plots.
The task usually took about 2 working days for each month of data, sometimes resulting in a changing visual view of the graphical plots i.e., changing series colour, legend etc.
The first step is to get familiar with the data structure, understanding the origin and value in the dataset and most important, what are the expected answer(s) from restructuring the data into a suitable subset of the initial data.
The first approach using power query gave an automated work-flow, resulting in a small Power Pivot table with the selected parameters in a certain predefined range and finally in a graphical plot, all readily updated using a single key stroke. #dataprocessing, #powerquery, #pivot
The automated setup took about a single workday to make it perform and about 2-3 hours to familiarise with the data plus the 45 minutes to process 6 months of data, i.e., a total of 12 hours of work. This would have taken 16 hours for each month or 96 hours in total thus a reduction in 87.5% or breakeven well before processing the first month of data.
The reduction on an annual basis is 23 working days or 180 hours FTE #costreduction
The next logical step, I imagine, would be looking into the direct extraction from the supply company database and apply the power query workflow or a similar workflow or algorithm. #cloudsolution #dataautomation
