As a newcomer data analyst working on a project, we greatly need the knowledge and skills of project management. After weeks of intensive work, I have identified some basic phases for project management in data analysis projects. Following these phases should lead to a framework that makes decision-making and problem-solving a little easier.

Step 1: Understand the questions and requirements

The first day of project week starts with a client meeting. This meeting focuses on understanding the client’s requirements and the problems they want to solve through the project. During this phase, it is essential to fully comprehend and clarify the client’s expectations. After the client meeting, it is beneficial to conduct a brainstorming session to generate as many ideas as possible and gain different perspectives on the problem.

It is always crucial to understand even the seemingly simple problems or questions. Making assumptions or lacking a complete understanding of the problem can lead to incorrect conclusions and misguided actions. Identifying the problem itself is often one of the most challenging tasks.

  • Questions to ask during this phase:
  1. What problems are the stakeholders stating?
  2. Is the stated problem truly the root cause?
  3. What are the core underneath requirements for stakeholders?

Step 2. Think what to do about the questions

Once there is an understanding of the problem, It is time to decide what data needs to be collected in order to answer the questions and how to organize it so that it is useful. One should think about the following aspects:

  • Questions to ask yourself in this step:
  1. What needs to be figured out how to solve this problem?
  2. Where is the data located (files, database, external system, internal system)?
  3. What research is needed?
  4. Where is the information held?

Step 3. Prepare data for further analysis

When we start using the data, it might be a combination from different sources or it might not be of the highest quality. A process known as data cleaning is the fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. What we aim to achieve is clean data.

  • Questions to ask yourself in this step:
  1. Is the data source trustable and data quality high?
  2. What data errors or inaccuracies could occur within given dataset.
  3. What is the best possible answer to the problem being solved?
  4. How to clean the data so the information is more consistent?

Step 4: Explore the data

Once we get all the data we want, we should play with our data freely, make as many charts as possible, and put them into one dashboard using filters to make the charts related to each other. Then explore between charts to find interesting insights. Think analytically about your data, be critical and be creative.

  • Questions to ask yourself in this step:
  1. What story is my data telling me?
  2. Will X (e.g. time, money, manpower or expertise) allow us to solve the issue?
  3. How will my data help me solve this problem?
  4. Who needs my company’s product or service?
  5. What type of person is most likely to use it?

Step 5: Find insights and decide the story

In this step we have already got a lot of charming charts, we need to make some conclusions based on the trustable charts and decide what story we will talk in the final step. As a team member working for one project, it is always necessary to get additional opinions about the findings. This will significantly help to improve the results and ensure that main aspects were taken into account. The feedback will help to answer the questions that initially were not thought of.

  • Questions to ask yourself in this step:
  1. How can I make what I present to the stakeholders engaging and easy to understand?
  2. What would help me understand this if I were the listener?
  3. What makes a data visualisation good?

Step 6: Present in an appropriate way

The final step of the project is to present the data analysis results in an appropriate way, which is vital for effective communication, decision-making, and collaboration. It enhances understanding, credibility, and engagement while mitigating misinterpretation. By investing time and effort into presenting the data analysis results in a clear, concise, and visually appealing manner, you can maximize the impact and value of your analysis.

  • Questions to ask yourself in this step:
  1. Who is the audience, and what are their expectations?
  2. Is my story organized in a logical way?
  3. Is the storyline clear enough for the audience to understand?
  4. How can I present my story to the audience as clearly as possible?


These are the six steps do help to structure the thinking when conducting Data Analysis and then break the process into smaller, manageable and logical parts.

The Data School
Author: The Data School