The first week at The Data School for our cohort, DS17, ended with the first Friday challenge. We had 3 hours to re-design a viz from one of our old Data School applications, supplement it with any additional data we wished to add, and reviz it. As our first week was focused on Alteryx, we needed to use this tool to prepare our data before bringing it to Tableau.
Sometimes 3 hours may not seem enough to complete the task. So how can you make it work? My approach is to treat each challenge as a project and think about how you can decompose it to deliver the outcome on time. This approach becomes even more important when you are working on your own and can get carried away tackling one issue rather than focusing on the final outcome of the project.
Understand the scope of the project
Read the assignment or your meeting notes carefully to understand what outcome is actually expected and what resources you can use. If you have a question about the ask, it’s better to clarify it at this stage rather than spending all your time on delivering something completely different from what your clients expect.
Know your data
Once you know what you need to deliver, take a closer look at the dataset in question to understand if it poses any limitations to your analysis (in its scope, structure, completeness, reliability, etc.). Such constraints might require additional research or extensive data cleansing and reshaping. This could take more time than planned and make your approach challenging to complete within the deadline.
Divide to conquer
Once you know what resources are available to you and what outcome is expected, plan your work. Write down the logical steps you would need to take to achieve the desired outcome and understand if you have everything you need to succeed. By doing this you might discover any previously unforeseen constraints or missing information. Don’t be afraid to ask questions to make sure you are on the right track.
Time is one of the most critical factors in project management. Timebox yourself: understand how much time each of the previously outlined steps would take and stick to this schedule. Be honest with yourself and about what you can realistically achieve within the set timeframe. Also remember to set aside some additional time for lunch, practicing the presentation of your work, and any unforeseen challenges along the way.
Know when to stop
By now you have a clear idea of how much time each step of your project should take, but it’s equally important to understand what you should not spend your time on. Don’t over-research the topic or overthink all the incredible data vizes you would build with the data set. Build a ‘minimum viable viz’ first and perfect it later.
There are no right or wrong questions, most likely other people have faced a similar issue before. Whether it’s a technical question or one related to the project’s scope, turn to online communities or people in your network for help. The Data School thrives thanks in part to a culture in which questions are actively encouraged and we all learn from our collective challenges and mistakes.
Learn from experience
Once the project is finished, take a step back and look at what worked and what didn’t work in your workflow. Think about how you can structure your work in a more efficient way next time and ask for feedback to get the full picture.