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What business questions?
The first thing that you have to figure out is not whether you want to do data analysis, a dashboard, machine learning or whatever new fancy technique you want to introduce. The first thing you have to come up with is what business question you want to answer. List a few of your business questions and mark down a ranking of which are most important. After this you should estimate what type of analysis is needed in order to answer the questions.
What type of analysis?
There are multiple types of analysis possible. These vary in the complexity and time that is needed in order to develop them. But with that also the value that can be gained from them can be increasing, as you can answer more complex questions.
When you are looking for that easy win you will want to look at what you can answer with the very simple analytics that you can do. Most of the time when you just visualize what is going on, you already can gain very important insights that you otherwise wouldn’t have had.
For more information on what type of analytics are possible please read our article on Different types of Data Analytics.
How much time should I spend?
Data science projects are easily run for a very extended amount of time and an ever growing scope. The reason for this is very often that it’s hard to say what exactly will be the outcome of the analysis. You don’t know exactly where it will start and once you find something interesting your business counterpart will ask you the very same second: “So does that mean you can also do…”. This causes very often that the scope keeps growing and the project never gets done. Even though the initial question is already long answered.
For more information on how to run your data science project, read our article on Pitfalls of analytics projects.
Do you want help finding these easy wins and give your analytics project the highest chance of paying off. We are happy to find that early insight or help you identify where you should look in order to bring the value that is right there at the surface.
