So you can get to work and do it fast (you can be sure that the more time you spend on this stage, the more the wish list will grow with nice-to-haves) Data that they can trust.Īt this point of the process, after a couple of conversations, you should have a clearer scope of the work that is needed, the purpose of the dashboard and what they plan to do with it. Ultimately, this is what every customer wants. If your customers know that you understand their needs and take the time to clean and optimize their data, they will trust that it reflects their reality. ( instead of focusing on the origin of the numbers or what they mean for each participant? ) How can I make sure these discussions revolve around solving problems?.Will this reporting facilitate a recurring discussion?.Ask yourself: What are they trying to accomplish? When you know their priorities, you can answer important questions before starting to analyze their data, such as: Instead, it is best to find out why your Product Owners want to see that metric. Therefore, when they say “I need A and it should show B”, your next question shouldn’t be: What you keep in mind as the person behind the reporting, is that the purpose of reporting is to: They will be focused on the final look and formatting of what they want to see. Customers and end-users (or Product Owners) will come to you with wish lists and must-haves. Understand the Ultimate Dashboarding Requirements Nobody should have to dissect a file that became a black box each time they want to make a small change!
You can always adjust your approach to decouple your process and enable your future self (or your backup/replacement) to repeat it/update it/change. Note that you should be able to follow these steps no matter how technical your background is (from basic excel files to complex Python flows). I’ve laid down the steps that are required to separate the stages of your data journey and make each of them simple to automate afterwards. Decoupling implies that each step is done in a tool that is most appropriate for it, then automated and then the pieces of the puzzle are simple and independent from each other. By simplifying the process as inputs and outputs, you will invest in your own future mental health and your customers’ satisfaction. Most of the time, after an ad-hoc analysis is built, Data Analysts are required to turn it into stable reporting, facing challenges to automate the work done in a smart way.ĭecouple (separate) your data preparations from your analytics and stick to using BI tools for visualizations and formatting.
These functionalities can be helpful to build a one-time quick analysis, but fall short when trying to build effective, scalable and stable visualizations with a wide, demanding audience. Now, both PowerBI and Tableau offer their own features for more complex use cases like the above.
These tools can help you analyze business data and visualize information to gain valuable insights.Ĭreating a dashboard that provides some insights can be a quick process (especially once you gain some expertise with the BI tool of your choice). Business Intelligence (BI) technologies like Power BI and Tableau collect, integrate, analyze, and present business information.
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