Importing Data into Power BI

Now that we understand the importance of cleaning data and even how to tackle some of the most common data-cleaning operations, it’s time to start bringing some data into Power BI.

When it comes to importing data into Power BI, there are six main topics, as listed next, to consider. Most importantly, for the top three, there are metrics that help us identify how clean data is prior to us working on it. In this chapter, you will learn about these topics and how you can use the Power BI platform to assess these metrics within your own data.

Specifically, we will be going through the following topics:

• Understanding data completeness
• Understanding data accuracy
• Understanding data consistency
• Assessing data relevance
• Assessing data formatting
• Assessing data normalization, denormalization, and star schemas

By the end of this chapter, you will be able to confidently assess how clean the data you’ve imported...

Technical requirements

You will find the uncleaned dataset to be used during this chapter at the following link: https://github.com/PacktPublishing/Data-Cleaning-with-Power-BI.

Understanding data completeness

When importing data into Power BI, one of the primary concerns is ensuring data completeness.

Before diving into the technical aspects of importing data, it is essential to understand the context in which data completeness becomes a critical factor. Incomplete data can lead to skewed analyses, erroneous visualizations, and misleading business insights. Therefore, in this section, we will explore the significance of data completeness and how it forms the foundation for accurate and reliable reporting in Power BI.

To assess data completeness in Power BI, we can employ various techniques. For instance, we can use data profiling to identify the percentage of missing values for each column. Power BI’s built-in data profiling capabilities help us visualize the completeness of data across different fields in our dataset.

By analyzing these visualizations, we can pinpoint columns with high percentages of missing values, indicating areas that...

Understanding data accuracy

Understanding the context in which data accuracy matters is crucial for data analysts and business professionals alike.

In this section, we will delve into two methods to validate data accuracy and detect errors or inconsistencies. By addressing data accuracy during the data import process, you will gain confidence in the integrity of your datasets, enabling you to make well-informed decisions based on trustworthy insights from Power BI.

Power BI provides several tools to assess data accuracy. One such tool is conditional formatting, where we can define rules to highlight data points that fall outside predefined accuracy ranges or thresholds. For example, we can set rules to flag unusually high or low values in our dataset.

Additionally, Power BI offers data profiling functionalities to examine data distributions and identify potential outliers. By identifying and addressing inaccurate data points, users can ensure that their visualizations and...

Understanding data consistency

Data consistency is the key to unlocking the full potential of Power BI as a powerful data analysis and visualization tool. However, achieving data consistency can be challenging, especially when dealing with multiple data sources and diverse data formats.

In this section, we will focus on understanding data consistency and its impact on Power BI reports and dashboards. By addressing data consistency proactively, you will be better equipped to harmonize and integrate disparate data, enabling you to create coherent and impactful visualizations in Power BI.

Power BI’s data modeling capabilities play a crucial role in assessing and ensuring data consistency. By creating relationships between tables and using data modeling best practices, such as defining proper data types and data categories, users can ensure that the data aligns seamlessly, leading to accurate and consistent analyses. You will learn more about this later in the book in Chapter...

Assessing data relevance

Data is only valuable when it is relevant to the questions we seek to answer and the goals we want to achieve. Irrelevant or extraneous data can clutter reports and hinder the decision-making process.

Therefore, it is crucial to consider data relevance during the data import stage. In this section, we will emphasize the importance of evaluating data relevance and ensuring that only pertinent data is imported into Power BI. By filtering and transforming data thoughtfully, you will gain a deeper understanding of your business objectives and derive meaningful insights from their Power BI visualizations.

Power BI’s data transformation capabilities allow users to filter out irrelevant data during the import process. For example, when importing sales data, users can filter the data to include only the relevant product categories, time periods, or regions that align with the analysis objectives. Moreover, Power BI’s query editor provides a wide...

Assessing data formatting

Effective data visualization heavily relies on appropriate data formatting. Raw data may not be suitable for direct visualization, and thus, appropriate data formatting becomes essential for clarity. Mastering data formatting enhances the visual appeal, improving the user experience and their understanding of complex datasets.

Power BI’s formatting options allow you to control how data is displayed in visuals such as charts, tables, and maps. Users can customize color schemes, font styles, and axis labels to create visually engaging and informative visuals. Conditional formatting features enable dynamic changes based on data values, highlighting important data points. Leveraging these capabilities transforms raw data into compelling visualizations, effectively communicating key insights to stakeholders.

This can most commonly happen when connecting to and working with revenue data. Particularly if this data needs to be formatted with a particular...

Assessing data normalization, denormalization, and star schemas

Data normalization is a fundamental concept in database design, and it holds equal importance in the realm of data preparation for Power BI. In this section, you will explore the context in which data normalization becomes necessary, especially when dealing with denormalized or redundant datasets. By understanding the principles of data normalization and applying them judiciously, you will be able to structure your data efficiently, leading to improved data organization and optimal performance of Power BI reports and dashboards.

Power BI’s data modeling capabilities support data normalization by enabling users to establish relationships between tables based on common keys. By doing so, you can reduce data redundancy, which not only saves storage space but also ensures that data updates are consistent across related tables. This results in more streamlined and efficient data analyses within Power BI. Additionally...

Summary

In this chapter, we explored aspects of data quality assessment and preparation during the process of importing data into Power BI. The chapter began with an understanding of the significance of clean data and the need to address data quality concerns. It emphasized that the top three factors to consider are data completeness, accuracy, and consistency, as they lay the foundation for reliable analyses and visualizations.

You learned about using Power BI’s data profiling capabilities to assess data completeness. By visualizing missing values and identifying columns that demand attention, you gained the skills to ensure a thorough and complete dataset.

Moving forward, your exploration of data accuracy unfolded with Power BI’s versatile tools such as conditional formatting and data profiling. These tools empower you to validate accuracy, detect errors, and pinpoint outliers.

Delving into data consistency, Power BI’s strong data modeling capabilities...

Questions

1. What is one of the concerns (described in this chapter) when importing data into Power BI?
   1. Location
   2. Size of data
   3. Data completeness
   4. Formatting
2. Which Power BI tool can be used to define rules and highlight data points that fall outside of the predefined accuracy ranges?
   1. Power Query
   2. Power Pivot
   3. Conditional formatting
   4. Data profiling
3. What does Power BI’s DAX language allow users to create?
   1. Visualizations
   2. Semantic models
   3. Relationships between tables
   4. Calculated columns and measures
4. How can users filter out irrelevant data during the data import process in Power BI?
   1. Using DAX expressions
   2. Through conditional formatting
   3. By creating relationships
   4. Utilizing data transformation capabilities
5. What is the fundamental concept introduced in the last section?
   1. Data profiling
   2. Data transformation
   3. Data normalization
   4. Data completeness