The analysts asked questions to define both the issue to be solved and what would equal a successful result.

Next, they prepared by building a timeline and collecting data with employee surveys that were designed to be inclusive.

They processed the data by cleaning it to make sure it was complete, correct, relevant, and free of errors and outliers.

They analyzed the clean employee survey data. Then the analysts shared their findings and recommendations with team leaders. Afterward, leadership acted on the results and focused on improving key areas.

1. Ask questions and define the problem, This part of the asking phase helps you keep focused on the problem itself, not just its symptoms.

   • we make sure that we fully understand stakeholder expectations.
     • Communicating with your stakeholders is key in making sure you stay engaged and on track throughout the project.
   • defining a problem means you look at the current state and identify how it's different from the ideal state.

   Questions to ask yourself in this step:

   1. What are my stakeholders saying their problems are?
   2. Now that I’ve identified the issues, how can I help the stakeholders resolve their questions?

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2. Prepare data by collecting and storing the information.

   Questions to ask yourself in this step:

   1. What do I need to figure out how to solve this problem?
   2. What research do I need to do?

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3. Process data by cleaning and checking the information.

   • data analysts find and eliminate any errors and inaccuracies that can get in the way of results. This usually means cleaning data, transforming it into a more useful format, combining two or more datasets to make information more complete, and removing outliers.
   • you'll also fix typos, inconsistencies, or missing and inaccurate data.
   • Using spreadsheet functions to find incorrectly entered data
   • Using SQL functions to check for extra spaces
   • Removing repeated entries
   • Checking as much as possible for bias in the data

   Questions to ask yourself in this step:

   1. What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve?
   2. How can I clean my data so the information I have is more consistent?

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4. Analyze data to find patterns, relationships, and trends.

   Questions to ask yourself in this step:

   1. What story is my data telling me?
   2. How will my data help me solve this problem?
   3. Who needs my company’s product or service? What type of person is most likely to use it?

5. Share data with your audience.

   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?

6. Act on the data and use the analysis results.

   Questions to ask yourself in this step:

   1. How can I use the feedback I received during the sharing phase (step 5) to actually meet the stakeholder’s needs and expectations?

These six steps can help you to break the data analysis process into smaller, manageable parts, which is called structured thinking. This process involves four basic activities:

1. Recognizing the current problem or situation
2. Organizing available information
3. Revealing gaps and opportunities
4. Identifying your options

Gut instinct is an intuitive understanding of something with little or no explanation.

• The difference with Follow the data life cycle.