Forem: stephen Githinji

Power BI: Turning messy data into actionable insights

stephen Githinji — Tue, 10 Feb 2026 11:02:15 +0000

Introduction

Within organizations, data analysts have the crucial task of deriving meaning from various data sets. In its true form, data does not provide substantial insights that can be used to drive meaningful change within organizations. Here is where data analysts come in to clean and analyze the data and to visualize their findings. To carry out these tasks, analysts employ various analytics tools. One of the widely used tools in the industry is Power BI. This article aims to demonstrate how data analysts and data professionals at large use Power BI to translate messy data into action, while touching on concepts such as data transformation with Power Query, analysis with DAX, and creating visuals and dashboards.

Power BI Overview

Power BI is a business analytics and intelligence tool, which is part of Microsoft's Power Platform, used to analyze data and create interactive visual reports and dashboards. Power BI can be downloaded directly from the Microsoft Store.
Click the following link to download: Download Power BI.
The Power BI window contains the following components:

Menu Bar

Located at the top of the window and it is organized into tabs such as Home, Insert, Modeling, View, Optimize, and Help.

menu bar

View Selector / navigation pane

Found on the left hand side and it contains the reports view, table view, model view, and the Dax query and TMDL view.

view selector

Visualizations Pane

Lets you choose different visual types (bar chart, line chart, map, table, etc.) and includes options to format visuals and manage visual-level filters

Data Pane

Displays tables, columns, and measures from the loaded dataset

Filters Pane

Used to apply filters at different levels(Visual-level filters, Page-level filters, Report-level filters)

Visualization, Data, and Filter panes

In addition to its intuitive navigation, one of the beauties of Power BI is that it allows analysts to load and work with files in various formats, for example: CSV, text, Excel, Dataflow, Dataverse, etc. To see all these formats, go to the menu bar >Home>Data>Get data.

Data formats acceptable in Power BI

Transforming Data Using Power Query

Data obtained in an organization often contains inconsistencies, missing values, duplicates, or errors. Data transformation refers to the process of cleaning, reshaping, and preparing this data so that it is accurate, consistent, and suitable for analysis and reporting. Power BI provides access to another Power Platform tool referred to as Power Query, whose sole purpose is to prepare and transform data before analysis. To access Power Query within Power BI go to menu bar>Home>Queries>Transform Data. This will open up a separate window for the Power Query editor.

Accessing Power Query

Power Query window

By simply selecting the rows and columns you want to modify and navigating to the editor's menu bar to find the action(s) you want to perform(for example: remove, split, merge, change data type, etc.), you can make all the necessary and desired transformations for your data. Once done with the transformations, you can click the Close>click and apply changes button to ensure the changes in Power Query are updated in Power BI.

Applying changes to power BI

Data Analysis

After satisfactorily transforming or cleaning the dataset, the next step is analysis. It involves examining data to discover patterns, trends, and insights that will ultimately support decision-making. It involves the implementation of statistical techniques, logical reasoning, and analytical tools to convert raw data into meaningful information. It is through analysis that analysts are able to extract meaning from data. In Power BI, analysis is done mainly by using the Data Analysis Expressions (DAX) tool.

Data Analysis Expressions (DAX)

DAX is a formula language for Power BI to perform calculations on data. There are 5 main DAX function categories:
Aggregate: Summarize data, similar (Sum, Average, AverageX, SumX)
Logical: Test conditions and return TRUE/FALSE
Calculate and Filter: Calculate dynamically based on filters or context.
Text: Manipulate text columns.
Date and Time: Handles dates and periods
In Power BI, DAX expressions are written in the formula bar, which is just below the menu bar.

formular bar

Once these calculations are carried out, they can either be recorded as a new measure or a new column. Therefore, before carrying out any DAX function, select whether the new values should be recorded as a measure or column, depending on the function you want to use. This can be done by going to menu bar>calculations>new measure/new column.

DAX calculations

Aggregate functions

The most common include:
SUM: Adds all the numbers in a column.

sum of column = sum(table_name[column_name](unit_of_measurement))

AVERAGE: Returns the average of a column

average of column = average(table_name[column_name](unit_of_measurement))

SUMX: Adds up an expression for each row in a table.

sum of expression = sumx(table_name, expression)

AVERAGEX: Calculates an average based on an expression per row.

average of expression = averagex(table_name, expression)

MAX: Retrieves the largest value in a column

max of column = max(table_name[column_name](unit_of_measurement))

MIN: Retrieves the smallest value in a column

min of column = sum(table_name[column_name](unit_of_measurement))

Expressions are calculations performed between 2 or more columns of a table.

Logical functions

The most common include:
IF: Returns one value if a condition is true, and another if false.

Result = IF(condition, "value if true", "value if false")

AND: Returns TRUE if all conditions are true.

Result = IF(AND(comma separated conditions) "value if true", "value if false")

OR: Returns TRUE if at least one condition is true.

Result = IF(OR(comma separated conditions) "value if true", "value if false")

NOT: Reverses a logical value.

Result = IF(NOT(condition) "value if true", "value if false")

To use more than 2 conditions in AND and OR functions, use the IF function together with && (ampersand operator) and || (double pipe) respectively.

A nested if statement (multiple if statements meshed together) can be provides more than 2 possible results (multiple results). However, it can only be used to evaluate only one column.

Calculate and Filter functions

calculate : changes the filter context of a calculation and then evaluates an expression under those new filters. Only done for one column at a time.

result = Calculate(Expression, condition)

Filter : Returns a table that contains only rows that meet a condition.

new table = Filter(source_table, condition)

Text functions

The common function include:
CONCATENATE : Joins two text values together.
CONCATENATEX : Concatenates text from a column or table with a delimiter.
LEN : Counts the number of characters in text.
UPPER and LOWER : Converts text to uppercase and lowercase respectively
PROPER : Capitalizes the first letter of each word.
TRIM : Removes extra spaces from text (except single spaces between words).

Data and Time functions

The most common functions include:
TODAY : Returns current date
NOW : Returns current date and time
DATE : Creates a date
DAY : Extracts day
MONTH : Extracts month number
DATEDIFF : Difference between dates

DATEDIFF(lower_date, higher_date, DAY)

DATEADD : Shift date context by either a month, day, or year.

shifted_date = DATEADD(date_column, operation, DAY/MONTH/YEAR)

operation refers to the number of days/months/years you want to shift the date (e.g., +1, +3, -4, -5)

Data Visualization

Visualization involves the graphical representation of data using visual elements such as charts, graphs, tables, maps, and dashboards to communicate information clearly and effectively. After generating the measures using DAX, it is possible to visualize your calculations thanks to the reports view tool in the view selector.

report view

To visualize a measure, you first select the visual on the visualization pane you want to use, then locate the measure you want to represent on the data pane. You can apply some filters from the filters pane, depending on how you want your visual to appear and how to represent your data.

select the visual

locate and select your measure

visual of a card

Power BI supports various visuals to represent data and they can all be located on the visualization pane. Some of the most popular visuals include:

Card - Displays a single key value, such as total sales or profit.
Bar Chart - Uses horizontal bars to compare values across categories.
Column Chart - Uses vertical bars to show data changes across categories or time.
Line Chart-Shows trends over time using connected data points.
Pie Chart - Displays proportions of a whole.
Donut Chart - A variation of the pie chart with a hollow center.
Map - Visualizes data based on geographic location.
Treemap-Displays hierarchical data using nested rectangles.
Scatter Chart - Plots values using two numeric axes to show relationships.
Slicer-Acts as an interactive filter for reports.

Bars, lines, and scatter plots within the accompanying visuals can also serve as interactive slicers.

Examples of visuals in Power BI

The Dashboard

A dashboard is a single-page visual display that presents key information needed to monitor performance and support quick decision-making. It is formed by bringing together and arranging key visuals that are deemed crucial (depending on the goals of the analysis) on a single view.

A simple Dashboard in PowerBI

Conclusion

Regardless of the field and type of data, data analysts are capable of deriving meaning from the data presented by following several steps with the help of various tools. Power BI is one of the most sought-after tools by data analysts, given its intuitive interface, support for various data types, and its vast array of technical, analytical, and visualization capabilities. From carrying out transformations using Power Query to using DAX for calculations and analysis, and creating visualizations from the visualization pane, Power BI is definitely a powerful tool for any data analyst or professional seeking to derive meaning from their data.

Schemas and Data Modelling in Power BI

stephen Githinji — Mon, 02 Feb 2026 14:56:21 +0000

Introduction

In Power BI, Data modelling is the process of creating visual representations of the relationships between data structures, including information about the individual attributes within those structures. These visual representations are referred to as schemas.

An example of a Power BI schema

2 main schemas are employed in Power BI, and they are: Star and Snowflake.
In Power BI, schemas can be found under the fields/data pane, located on the right-hand side of the Power BI window, under model view.

The star schema

A star schema is one where a single fact table is surrounded by multiple-dimension tables. The fact table contains the numeric measures or facts of the data model, while the dimension tables contain descriptive attributes or dimensions of the data model. Just as the name suggests, the visual resembles a star⭐

The Snowflake schema

On the other hand, a snowflake schema is a type of schema that extends the star schema by normalizing dimension tables. In other words, one central fact table connects to multiple dimension tables, but one or more of those dimension tables are further divided into multiple related sub-dimension tables.
This ultimately results in a structure that resembles a snowflake❄️

A star vs a snowflake schema

Relationships in Star and Snowflake Schemas

How fact tables and dimension tables connect is what is defined as a relationship. They are basically defined by the use of Primary Keys(PK) and Foreign Keys(FK).
PK : A unique identifier for every row in a table
FK : A column in one table that references the Primary Key of another table, creating a link between them.

Importance of Good Data Modelling Practices for Performance and Accurate Reporting

Performance	Reporting
Optimized query speed	Single source of truth
Reduced complexity and resource usage	Data integrity
scalability	contextual understanding
Faster processing	Fewer errors

Introduction to Linux For Data Engineers, Including Practical use for Vi and Nano

stephen Githinji — Mon, 26 Jan 2026 02:15:11 +0000

Overview

Data Engineers (DEs) are involved in building and maintaining systems that collect, store, and prepare data for data scientists and analysts to use. To be able to achieve this, they employ various techniques and tools, and one of these tools is Linux.
Linux is an operating system (OS) which is open-sourced and is based on the Unix system. It contains several distributions such as Ubuntu, Fedora, Debian, and many others, all of which use the command line interface. Linux is especially known for its versatility and security, characteristics that make the OS more desirable to work with for technical professionals such as Data Engineers.

DEs prefer to use Linux due to the following:

Most servers and Virtual Machines (VMs) run Linux.
Easier automation and scripting
High compatibility with Containerization and orchestration tools.
Resource control
Troubleshooting and monitoring
High compatibility with big-data tooling

Basic Linux Commands for Data Engineers

For a DE to be able to use the command-line interface in the Linux OS seamlessly, it is essential to understand some of the basic commands in Linux.
Some of the commands that are deemed basic for a DE are:
ls : To list files and directories.
pwd: To print the current working directory.
cd : To change to a different directory.
cp : To copy a file to a different directory while still maintaining the original copy.
mv : To move a file to a different directory and delete the original copy.
rm : To delete a file.
ssh: To connect to a remote server.
tar, zip: For Creating archives and compressions
chmod: For changing access permissions of files and directories.

Practical Usage of Vi and Nano

Introduction

Vi and Nano are the most common text editors (tools that are used in creating, viewing, and manipulating texts in files) found in Linux.
DEs use text editors for writing and editing code, editing configuration files, creating scripts and automations, and writing documentation.

Vi

It is a modal, keyboard-driven editor found on almost all Linux systems. Modal implies that the editor operates in different modes, where the same key does different things depending on the current mode. One mode is used for typing (insert mode) while the other is used for commands to navigate, delete, copy, and search (Normal mode). To change what your keystrokes can do, one is obligated to switch modes.
Vi is found in many servers and it is adopted by DEs to edit files on remote servers over SSH.
To open a Vi text editor, use the Linux command:

$vi <file_name>

Some Key commands that are used in the Vi text editor:
:w - To write (save) the contents of the buffer to the file without exiting the editor.
:q - Quit Vi.
:wq - Save and quit.
:q! - Force quit without saving.
i- To write on the document
Esc- To transition between modes.

Example of a vi text editor

Nano

Nano, on the other hand, is modeless, as it uses one mode for commands and typing. Keys are used to insert texts, while commands employ shortcuts like Ctrl and Alt. DEs prefer nano when carrying out minute text edits on documents.
To open the nano text editor, use the command:

$nano <file_name>

Once within the text editor, the following commands can be used to manipulate the file:
Ctr+O - save the file
Ctr+X - exit
Ctr+K - Cut the current line
Ctr+U - Paste
Ctr+W - search
Ctr+\ - replace
Ctr+G - help

Example of a nano text editor

Pushing and Pulling Code From GitHub

stephen Githinji — Sun, 18 Jan 2026 10:12:44 +0000

Overview on GitHub

GitHub is among the most popular websites that provides teams the ability to carry out version control (VC). It is built on git, a popular decentralized version control system.

In software development, Git tracks changes made to the source code, hence enabling developers to collaborate and manage different versions of their product. GitHub provides an interface for hosting git repositories.

GitHUb Operations

Various operations in GitHub allow developers to host their git repositories and track their changes. The most common operations are commit, push, and pull.

Commit

Committing your code entails recording a snapshot of the changes to one or more files in your project.
First, one needs to add the file(s) they need to make changes to by invoking the command:

git add <file_name>

This places them to the staging area.
To commit your changes use the command:

git commit -m "Message describing your changes."

Push

The other operation is push. It used to upload your local repository content to a remote repository (such as GitHub).
To do so, use the command:

git push

In case you have a branch, use the command:

git push <remote-name> <branch-name>

prerequisites before pushing

You must have a local Git repository with committed changes on your computer.
A remote repository (like GitHub) linked to your local one. You can check your configured remote repos using the command:

 git remote -v

The necessary permissions to write to the remote repository.

Pull

Pulling is aimed at updating your current local working branch with the latest changes from a remote repository.
Use the command:

git pull

In case your are pulling from a specific remote branch, use:

git pull <remote_name> <branch_name>

Hint: Always ensure that you are always on the correct branch. You can use the command:

git checkout <branch_name>