Power BI has over 200 formulas that can be used for data analysis and visualization. Today, I'll be talking about the two different ways you can analyze and translate data in Power BI.
Before loading data into Power BI, please ensure your data is cleaned.
How to clean data
Before loading your data into Power BI, always ensure that it's cleaned for easier calculations and visualization.
We use Power Query to clean data in Power BI.
Open Power BI>open blank page> get data>click transform data to open Power Query.
Steps to follow
- Remove duplicates
- Remove errors
- Apply text, number, and date filters like unknown for specific conditions.
- Rename columns clearly.
- important Change the data types. Format the different types of data into the correct type of data according to Power BI.
Data types- Whole Number–Integers (e.g., 10, 25)
- Decimal Numbers.
- Fixed Decimal Number(Precise financial values).
- Text(Words or strings).
- Date/Time(Dates and timestamps).
- True/False.
- Replace/remove blanks to prevent more errors. Ensure you check the resulting table in the data view to spot issues you might have missed and save your work
- Click close & apply to load your data into Power BI.
Power BI visuals
- A calculated column is a new column that calculates a value for each row and stores it in Power BI, just like a formula column in Excel. These columns can be used to filter and slice data with precision. They are used directly in slicers, filters, and visuals where you need fixed, stored values.
- A measure is a formula that Power BI calculates on the fly based on the current filters or visuals, without storing data.
DAX- Data Analysis Expressions. These are formulas inside Power BI.
Essential DAX formulas
- Aggregate functions SUM - adds all numeric values in a column.
SUMX - adds up values row by row after applying a calculation to each row.
AVERAGE - finds the average of a column.
COUNT
Counts numeric values in a column
COUNTROWS -counts the number of rows.
COUNTX - checks each row, then counts the non-blank results.
- Filter functions
CALCULATE - changes filter context for a calculation.
Note - This measure calculates total revenue for Maize by filtering the Crop Type to Maize.
FILTER - returns a table that will keep conditions if true, and omit if false.
Note - This measure calculates total profit for crops with a yield above 3000 kg.
ALL - removes filters from a column or the whole table.
Note - this measure ignores all the slicers and filters.
IF - do this or that
You can check my detailed article on the if functions and their uses. uses of if function in data analysis
SWITCH - lets you check several conditions in one expression. If the first condition is true, do one thing; if the second is true, do another; and if none are true, do something else.
RANKX - gives each row a rank based on a measure, starting at 1 for the largest value.
MIN/MAX - return the lowest/highest value.
DASHBOARDS
Analysts also translate data into meaningful insights using dashboards in Power BI. Dashboards are mainly used for data visualization.
Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results.
Shown below is an example of an interactive dashboard that uses different visualizations to showcase trends and data types across a company.
Types of charts
Pie charts - used to show the relationship between parts and the whole.
Line chart - this is used for showing trends, relationships between data, and changes over time.
Bar/Column chart - they are used to show trends, differences in trends, and relative sizes.
Matrix - This is used to compare data findings across multiple rows and columns.
Map chart - used to showcase data findings by location.
Nairobi county map
Table chart - This type of chart displays detailed data.
Q&A chart - A Q&A chart lets you ask simple questions like (total profit), and Power BI gives you a visual with the answer automatically.
Power BI charts are the vibrant and informative windows through which data comes to life.
These charts are not just data visualisations, they're vital tools for gaining insights, making data-driven decisions, and telling compelling stories.
As a data analyst, this statement is what makes you, right?
Stay tuned for my next piece.😊
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