The Data Analytics Process

Introduction

This post is a high-level overview of the 4 step Data Analytics process that is taught in the WYWM Data Analytics course. In this post I detail what is required at each step of the process, the key points or skills needed, common mistakes to avoid, and which lessons in the Data Analytics Course you should refer back to.

Step 1: Define

What questions do we want to ask our data?

In this stage you need to: 

1. Take the given problem and phrase it in data terms
2. Understand your dataset
3. Identify questions for targeted analysis, taking notes of how you might display this data

Note: you will come back to (and sometimes refine) this at all stages of your process. 

• During the Transform stage you refer back to the questions you’re trying to answer in order to better prep your data
• During the Analyse stage you’re looking to generate the insights from the questions of targeted analysis you identified during the Define stage

Key points and skills 

• Basic quantitative and qualitative analysis
• Maintaining an open dialogue with the key stakeholders

Common mistakes to avoid:

• Not understanding the required outcome – you should always clarify any points of uncertainty with your key stakeholders
• Not spending enough time on this stage – this is arguably the most important step in the process as it is the foundation from which your analysis is built.

Key Lessons: 

• The data analytics process
• Interpreting basic descriptive statistics

Step 2: Transform

Preparing our data for analysis

In this stage, you need to: 

1. Gather your data (from different sources where required)
2. Merge your data together (where required)
3. Create and maintain a data dictionary
4. Record your cleaning steps

Key points and skills:

• Merging tables of data together
  • Power Query – Append
  • INDEX / MATCH
  • VLOOKUP
• Correcting formatting and normalising data
• Dealing with missing or incomplete data
• Maintaining a Data Dictionary
  • updating new columns or fields created (such as calculated fields or using Power Query)
• Recording cleaning steps

Common mistakes to avoid: 

• Segmenting data too early
• Incorrectly dealing with missing data – can you use an average of similar values? 
• Not updating the data dictionary – if you include new fields, you should include them.

Key Lessons:

• Cleaning data
• Dealing with missing and incorrect values
• Text manipulation

Step 3: Analyse

Generating insights or findings

In this stage, you need to: 

1. Generate insights using the questions from your Define stage
   1. Remember that an insight needs to be actionable
2. Use of exploratory analysis to reveal trends
3. Spotting patterns

Key points and skills:

• Pivot tables and charts
• Histograms
• Quartiles, percentiles, ranking, classes
• Boxplots

Common mistakes to avoid:

• Overanalysis or incorrectly targeted analysis – refer to your define stage to keep you on track

Key Lessons:

• Exploratory Analysis
• Plotting frequency distributions
• Calculating and interpreting box-plots and IQR

Step 4: Communicate

Presenting our key findings

In this stage, you need to:

1. Present your findings in a relevant format, whether that is written, video or audio. Examples include a Powerpoint presentation, a formal meeting, a word document (a template can be found below), an Excel Workbook or Dashboard, or a Power BI report or dashboard.
2. Ensure your report is tailored for the intended audience. To be valuable, the insights must be understood by the decision maker, who generally isn’t a data analyst. 
3. Keep your presentation and visualisations clean and free of clutter

Key points and skills:

• Knowledge of which chart to use to optimally present the insight
• Communication

Key Lessons:

• Visualisation
• Avoiding misleading statistics
• Storytelling with Data

Templates

• Define, Data Dictionary and Cleaning Steps: Template Link
• Communicate: Template Link
• This post as a google doc: Link

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