Complexity is the enemy of execution
Tonny Robbins
Was catching up with a couple of Data Analytics professionals this week, it was a great conversation about how some projects can end up being over engineered for the business need. Some of those over engineered projects might never see the light and never be in production, others are even canned after being in production because the business is not able to maintain them.
Below are few of things we think about when setting a new Data Analytics project (there might be more depending on your project and the business need of course):
- Project Management: How the project will be managed especially if cross-functionally.
- Visualisation: What type of interactive visualisation is needed.
- Data: Data Engineering and Data Architecture set-up.
- Calculations: What type of calculations is needed and if there is a visualisation then where are the calculations made. Do we use calculations in the visualisation tool or calculate in the tables where the visualisation reads from (GCP, Azure, AWS or others)
- Modelling: If there is a model, how do we set it and what are the variables involved.
As much as the above are very important, what I would like to suggest is:
Don’t over engineer your Data Analytics Project that you end up with a complex situation where it is difficult to productionise/maintain.
Make sure that your Data Analytics project is simple, efficient, optimal and easy to maintain!
What has been your experience with Projects being handed over to you? What did you come across when doing a Data Analytics Projects cross-fuctionally?
Looking forward to your thoughts and experience!
#StrategicalAnalytics #DataAnalytics #AgileMethodology #DataEngineering #Modelling #DataScience #Visualisation #ProjectManagement
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