How Analysts Translate Messy Data, DAX, and Dashboards into Action Using Power BI
As a data analyst, whether in the corporate world or working on a personal project, you will find that data analysis is often driven by the need to solve a problem or wanting an accurate, insightful view of data to make a better decision.
1. Define the Objective
First, we need to come up with the objective of our analysis.
- Ask Questions: Write down the specific questions you want answers to from the analysis.
- Theme Your Dashboard: If you need a dashboard, design one that maintains a clear theme. Don’t just show every kind of data to the user—focus on what matters.
2. Gathering the Data
The data we need is often scattered. You might need to:
- Scrape it online.
- Use data from survey results.
- Combine several Excel sheets.
- Fetch data from a database.
So, how do analysts translate this Messy Data into action?
Power BI allows you to access data from various sources, load it, transform it, and analyze it—all in the same workspace.
In Power BI, we follow the ELT approach (Extract, Load, Transform).
The ELT Process
Extract and Load
First, we start by Extracting the required data.
When you launch Power BI, you get a prompt to select a data source. Simply select where your data lies and import it into Power BI by Loading it there.
Transform
Once all your data is loaded, the next step is Cleaning. You want to remove all inconsistencies in your data by ensuring:
- Correct Data Types: All fields (Dates, Decimals, Text) must be set correctly.
- Handle Missing Values: Deal with nulls or blanks by either removing rows or filling them where appropriate.
- Splitting/Merging: Split or merge columns where necessary (e.g., splitting an "Address" column to get separate "Location" and "Street" fields).
- Standardization: Ensure consistency (e.g., making sure all currency data is in either Dollars or Shillings across the entire dataset).
Data Modelling: The Integration Step
After the data is clean and transformed, the best way to integrate it is through Data Modelling.
As discussed in my previous article Link , data modelling helps you create a clear structure and establish necessary relationships before you begin any plotting or calculations. This involves creating a proper Star Schema, consisting of a Fact table and Dimension tables.
Note: Always make sure to click "Close & Apply" to save all changes made in the Power Query Editor before you start building your reports.
DAX
Once your data model is set up, you might find that the raw data doesn't provide all the answers immediately. This is where DAX (Data Analysis Expressions) comes in.
DAX is the formula language of Power BI, allowing us to calculate and generate new information in two primary ways:
Measures: Use DAX to create summary aggregations. These calculate values on the fly, such as Total Sales, Year-over-Year Growth, Profit Margin, or Average Selling Price.
Calculated Columns: Generate new columns within your tables to provide more granular information. For example, you could create a "Profit Status" column that labels each row as "Profitable" or "Loss" based on a calculation.
DAX respects your model’s relationships. When you create a measure and plot it in a visual, it automatically reacts to the "Filter Context." This means the numbers will automatically filter and update based on the Dimensions (such as Date, Category, or Location) you use in your report.
Visualizing for Answers
A well-modeled dataset allows us to answer any question we might have. The best way to do this is to plot Visuals. Why?
- Spot Patterns Fast: Visuals help you and your audience see trends or patterns immediately.
- Tell a Story: After answering your initial questions, creating a dashboard tells a story about the problem you want to solve.
A great dashboard should:
- Describe the problem clearly.
- Illustrate insights found in the data.
- Suggest a potential solution to the problem.
By following this flow, your analysis remains relevant and provides genuine value, rather than just showing the dashboard user what they already know.
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