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Data Skills | 34 Questions To Ask Your Client Before Starting Any Data Analytics Project

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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Before starting any analytics project, it is important to establish a clear understanding of a your client’s requirements and the constraints you will face while building your solution.

These should be well documented, agreed on by all stakeholders, and used to guide your decisions as you create your deliverable. Doing this well from the outset is core to any project’s success – and will allow you to stay focussed and work efficiently.

The opposite is also true. The biggest cause of project failure stems from not clarifying requirements at the start of a project – something that may see you get bogged down in a rabbit hole of development issues, or waste time building features your client hasn’t requested.

So, how can you guarantee your project gets off to a good start? It’s all about the questions you ask.

Below are 34 questions crowdsourced from the DSAU9 team to help guide you when establishing project requirements at your first client meeting. These can be used in full, or taken individually depending on your timeframe and prior understanding of the business.

WHY – Establishing the purpose of a project

  1. What are the questions your client wants answered?
  2. What is the expected outcome of the engagement?
  3. How will this add value to your client’s business or team?
  4. What are their existing solutions?

WHAT – Establishing the deliverables

  1. What software will you have access to for data preparation, visualisation, and warehousing (including versions)?
  2. What functionality does your client want included in the deliverable?
  3. What are the brand guidelines you will be working with?
  4. What is the definition of “complete”?
  5. What are the KPIs and benchmarks of the project?
  6. How often will stand ups and check-ins take place?

WHERE – Establishing the data sources

  1. What data will you have access to?
  2. What data won’t you have access to?
  3. Where is the data warehoused?
  4. Who can provide you access to the data?
  5. Where are the metadata dictionaries and table schemas?
  6. What are the security protocols that govern data access?
  7. What additional data will be needed to complete the project?

WHO – Establishing the audience

  1. Who will be the users of the solution?
  2. What are their roles? (ie. CEO, manager, analyst)
  3. What are the availabilities of different stakeholders?
  4. What are their levels of data literacy, and how familiar are they with the tools being used?
  5. What security protocols need to be in place for access?
  6. Who is responsible for testing?

WHEN – Establishing the project timings

  1. What is the overall deadline for the deliverable?
  2. What are the dates of the individual milestones of the project?
  3. When will testing of the deliverable start?
  4. What date/time granularity will be used for analysis (day, month, quarter year)?
  5. What date/time standards does the client use (ie. start of financial year, start of week)?
  6. How often will the data source be refreshed?

HOW – Establishing implementation

  1. How will users access the deliverable?
  2. How will the deliverable be consumed (mobile, laptop, server)?
  3. How will access to deliverable and underlying data sources be governed?
  4. How will data be warehoused?
  5. What documentation will be required for an effective handover?

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