Asking the right questions in the discovery phase is super important for your data analytics projects to succeed. This first step is all about learning the details. Ask questions to understand the business problems that need solving. You want to know how the analysis will help the company and who will use it. Good questions help everyone agree on the goals. You also ask about what data you'll need, where it comes from, and how to get it ready. Questions help you plan the data work. Ask about project timelines, skills needed, and risks too. This helps you scope the work.

Lots of smart questions early on makes sure your analytics focuses on the right business needs. It helps your project go smoothly. Asking questions shows stakeholders you care about getting them the best data insights. It starts everyone working together. In the discovery phase, your good questions set up the analytics project for success! They build the foundation to get real business value from the data work.

Business Questions

​

• What are the main business problems we're trying to solve or key decisions we need to inform with this project?

• How will this analysis create business value if successful? What metrics are we looking to optimize?

• Who are the main stakeholders who will utilize the analysis? What insights do they need?

• How will the analysis output be used for decision making and strategy?

 

Data Questions

​

• What are our most important data sources and systems related to this problem?

• Do we need to access new data sources beyond what we currently capture?

• What data gaps or quality issues do we anticipate?

• What historical time frame of data do we need?

• Are there legal, compliance or privacy restrictions on any datasets?

• How complex will data wrangling and pre-processing be prior to analysis?

 

Scope Questions

​

• What is the minimum viable product (MVP) we need the analysis to provide? What are the next phases?

• What time and resource constraints do we have for the analysis based on business needs?

• What specialized data science skills (e.g. machine learning, NLP) does this require?

• How will we validate the analysis methodology and results along the way?

 

Process Questions

​

• What risks, blockers or dependencies could impact delivery?

• Who will oversee and approve the project at key milestones?

• How frequently will we give status updates and gather feedback from stakeholders?

• Who will eventually own and monitor the analysis output if operationalized?