Recently, while at work, I was given the opportunity to run some data analytics on some data in order to create some business insights and recommendations for a colleague. While I have learned about Business Analytics while in school, implementing it in real life as a one-man analyst is not just a walk in the park.
After practicing in real life and coming out with some insights so far. Here are some possibilities you could explore if you are keen on introducing data analytics into your day-to-day management work.
1. Establish the Goal of your Project
When given a project, there is surely an end goal which is required by whomever has assigned you the project. One way to establish the end Goal of the project is to ask the project leader who has provided the project. If he or she does not have a goal in mind, you could look into the data to propose the possibilities. With a goal, it would be easier to scope your project.
2. Determine the Nature of the Project
Once you have established your goals, you will have to figure out the nature of your project. Is it more descriptive in nature? Or more predictive? Do you want to see what your data says, or try to use the data to predict something else? With the nature of the project in place, it would help you to know whether you should be focusing on descriptive or predictive methods, especially since there is so many analytics tools out there and there is no way you can try everything on the same project in a limited period of time.
3. Try your Visualisations and Models
With so many models out there, which to use? I am also in the process of figuring this out and you could stay tuned to future blog posts if I come across the chance to do more projects.
For now, a good example of visualisations can be through Tableau, Google Analytics and Excel Charts.
A good example of models can be Machine Learning Models through R, Python and Microsoft Excel.
4. Prepare Insights, Recommendations. Rinse and Repeat
Once you are done with your models, you have to summarise your insights which are paired with specific recommendations. You can then engage your project leader, check if everything is going along the right direction. Over time, if you can continue to work on the project, find ways to consistently relook at the data, the insights and think of ways to improve the model and the connect to the business.
At the end of the day, when you are doing analytics, always focus on the needs and requirements of the business to propose strong insights and recommendations, and not the models which you are using.
If you’re interested in how to formulate insights, do take a look at my analysis of 500 users on Google Analytics.
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