The transformation of raw data into meaningful information is based on in-depth analysis. Power BI offers a robust suite of tools designed for efficient data evaluation. As the availability and complexity of data increases, so does the demand for effective analytical processes to extract value from it. This value is discovered through a systematic analysis of data in Power BI, which generally develops through multiple iterative phases. This article will guide you through the various steps of the data analysis process in Power BI and introduce you to different techniques for using Power BI. Our goal is to clarify what happens at each stage of the data analysis workflow. At the end of this article, you'll have an in-depth understanding of the analysis process, which you can begin to integrate into your practices.
Owned by Microsoft, Power BI is a first-level business intelligence and business analysis tool, widely used by companies to stimulate growth.
The main purpose of Power BI is to offer sophisticated analysis, comprehensive reports, and dynamic visualizations; it allows app developers to embed fully interactive reports into their applications without having to create custom visual elements and data controls.
Its versatility and platform independence allow seamless integration into cloud services, mobile devices, and web applications.
Power BI is a software application packed with all the features necessary for data analysis. You can download it directly from the official Microsoft website or from the store.
Before going into the ways to analyze data in Power BI, it is necessary to know what components of Power BI are available, and to become familiar with the workspace that you are going to use.
As shown in the image, use the 'Get data' option to import data from a variety of sources, including Excel, Azure SQL Server, the web, and many others.
To the left of the diagram, we find the three buttons of Report, Data and models.
The 'Reports' tab shows all the views and reports created. The 'Data' tab allows you to view and edit imported data. The 'Templates' tab is used to create and manage relationships between multiple tables. At the bottom left, as in Excel, are the number of pages or reports used in the project. Click on the '+' icon to add a new page.
In the diagrams area, you can insert visualizations, graphs, and diagrams.
The 'Filters' panel includes all the filters that can be applied to your visualizations and charts.
The 'Visualizations' panel lists all the charts available for use in a report. In this window we find some interesting sections such as Field, Format and Analysis.
The 'Fields' section is used to provide data to the display chart.
The 'Format' section improves the appearance of the chart.
The 'Analysis' section allows you to make adjustments to the display.
The 'Fields' panel contains all the imported data and tables.
To start importing data, go to the 'Home' tab and select the 'Get Data' option from the menu.
Choose your data source and start the import process. Beginners may find it useful to start with simple data sets that contain fewer entries to better understand basic concepts.
Data analysis involves examining, converting, and supervising raw data to extract meaningful information, which helps make decisions essential to business growth. Adopting a strategy based on data requires an in-depth analysis of the data. Learning the various data analysis techniques in Power BI will be both interesting and beneficial. In fact, Power BI provides a series of tools and features to carry out advanced data analysis.
In today's business environment, ensuring a competitive advantage is crucial because of numerous challenges such as rapidly changing markets, economic uncertainties, unstable political climates, unpredictable consumer behavior, and global health crises.
These factors have significantly reduced the margin of error in business operations. For companies that aim not only to survive, but also to excel in this demanding environment, the adoption of data analysis is critical. By systematically collecting and using valuable and actionable information, companies can improve decision-making processes and gain a competitive advantage.
That's why it's crucial to use Power BI for data analysis.
Dev4Side Software specializes in creating customized reports through Microsoft Power BI, transforming complex data into intuitive dashboards and reports to support informed business decisions.
Operating vertically across the entire Microsoft Power Platform ecosystem, we have developed excellent expertise in Power BI, allowing us to offer business intelligence solutions that highlight critical insights and promote operational efficiency.
Find out how we can help you make the most of your data. Contact us to transform business information into concrete actions.
Data analysis in Power BI includes the phases of collecting, cleaning, modeling, scrutinizing, interpreting, and visualizing data to obtain information that facilitates decision-making. This process employs a series of business intelligence techniques and tools.
Here are the five phases of data analysis in Power BI that we will review in detail:
Ask questions -> Data collection -> Data cleaning -> Data analysis -> Communication of results
Questions are critical to identifying the data you need to collect or use, focusing on relevant aspects of your data, and guiding you to enlightening conclusions.
For example: what goals are you trying to achieve? What specific challenges are you trying to address? In the context of subscription-based businesses, a relevant question might be: what factors contribute to customer churn and how can it be effectively mitigated? In a cancer center, it is crucial to determine the size range of each tumor characteristic that indicates whether a tumor is cancerous.
For a sales company, understanding whether increased revenue translates to higher profits is essential. In addition, companies often try to predict future consumer behavior.
Once you've identified the questions that guide your analysis, the next step is to specify the type of data you need and collect it accordingly. This could include quantitative data, such as sales numbers, or qualitative data, such as customer feedback and reviews.
Data collection can take place internally or be obtained externally as needed. First-party data is information collected directly by the organization that performs the analysis. On the contrary, external data includes information from other companies (second-party data) or from various open data repositories (third-party data).
Next, you will need to import this data into Power BI Desktop. Start Power BI and access the 'Get Data' dialog box by clicking on the Get Data icon. Here, you'll find several options for importing data from different sources.
This step is significant because the depth of the analysis depends on the nature and quality of the data sources collected. It is crucial to ensure that you have enough data to effectively address the questions being asked.
After collecting your data, the next critical task is to prepare it for analysis, a process known as data cleansing. This phase is crucial to ensure the accuracy and reliability of the analysis results, directly affected by the quality of the data.
Data cleansing includes identifying and correcting incompleteness or discrepancies, as well as eliminating erroneous entries. Ideally, clean data should show the following characteristics:
In Power BI, data cleaning is carried out using Power Query, a complete data manipulation framework integrated into the platform. Power Query allows users to connect to different data sources and reshape their data to meet specific needs through a series of transformation operations.
Once the data has been cleaned, the next step is to start analyzing the data. The specific type of analysis conducted will depend mainly on the objectives that are intended to be achieved.
The first step involves performing an exploratory analysis. This phase aims to evaluate the suitability of your data for subsequent phases, deepening their structure. Exploratory analysis helps identify and address data quality issues, such as identifying anomalies in the dataset, and at the same time revealing patterns and correlations between variables. During this phase, it is possible to generate new variables and metrics that will support subsequent processes.
These new elements are created using DAX (Data Analysis Expressions), a programming language integrated into Microsoft Power BI. DAX makes it easy to create calculated columns, measures, and custom tables across the platform. DAX includes a series of functions, operators, and constants used in formulas or expressions to calculate and produce multiple values.
Here's what a DAX function looks like:
Churned = IF ('Database - Data' [Churn Label] ="Yes”, 1, 0)
Subsequently, we continue with the descriptive analysis that aims to discover what happened. This overview serves as a basis for determining next actions.
Following the descriptive analysis, the next step involves diagnostic analysis, which focuses on understanding the reasons behind the results of the descriptive analysis.
Another fundamental step is predictive analysis, which makes it possible to identify future trends based on the historical data present in the dataset. This form of analysis is typically used to predict future activity.
The final phase involves prescriptive analysis that makes it possible to make recommendations for future actions based on the insights obtained from all previous analyses.
The final step in the entire analysis process is to effectively communicate your insights. This involves presenting the information in a clear and understandable way for all intended recipients.
In Power BI, this is achieved through data storytelling, where data is transformed into an engaging narrative using visualizations, dashboards, and reports.
Here's an example of a customer trend analysis dashboard:
The steps provided above constitute the essential framework for conducting data analysis. They can be adapted and rearranged as needed to meet your specific needs.
As you proceed, prioritize knowledge and mastery of Power BI's DAX functions, as well as familiarity with diversified concepts in the field of data analysis. These skills enhance your ability to effectively extract meaningful insights from data.
Remember that data analysis is not universally applicable and the results depend on numerous factors. Integrating data analysis in Power BI into business strategy is crucial to maximizing its value.
Power BI offers a wide range of tools and charts that facilitate efficient data analysis. Let's analyze what Power BI can do to effectively use data, what fundamental techniques to use within Power BI to extract valuable insights from data.
Go to the 'Reports' tab to start creating visualizations and charts.
In the 'Views' tab, choose or drag a view onto the screen. For example, you can use an overlapping column chart on the left and an area chart on the right.
Still in the same tab, access the 'Fields' section to manage your graphs or visualizations.
Each type of chart has its own set of properties that can be adjusted, and these properties vary depending on the type of chart chosen. You can explore and experiment with these properties to familiarize yourself with them.
Let's explore some commonly used fields in a chart:
Power BI will update the chart based on changes made in the Fields section. The shape of the graph will adapt according to the selected operator and the resulting values on the Y axis.
Experiment with different field views and settings to deepen your understanding of Power BI's capabilities.
In Power BI, grouping allows you to consolidate different categories into unified sets, while binning refines the representation of data by adjusting dimensions and numerical ranges. These techniques are critical for conducting in-depth data analysis within Power BI.
Analyzing individual data fields can be challenging. For example, if we have data from different countries and we want to group them by continents, creating separate groups for Asian and non-Asian countries, we can use a direct method to achieve this goal.
After creating a bar chart:
This clustering technique allows the comparison of multiple groups based on different categories.
For refining data digits, binning is a useful technique. For example, if we have sales data that includes customer IDs and quantity of products purchased, we can use a bar chart to compare customers who made initial purchases versus those who made recent purchases based on the quantity of product.
Using a bar chart becomes impractical when managing a large number of entries. In such scenarios, binning becomes essential for dividing entries into manageable blocks. It is important to note that binning can only be applied when both the X axis and the Y axis of the graph consist of numeric or integer fields.
If you want to make a graph clearer that is too congested also due to the different entries in our data, then it is possible to use binning.
Bin size = (Maximum value - Minimum value)/Bin count.
Now you will notice that all the bars are grouped into equal-sized bins. Each entry is evenly distributed among these bins based on the specified criteria.
Clustering is similar to clustering and binning. In a scatter chart, with a large number of records, representing each as an individual point can become complicated. Therefore, clustering is used to consolidate these points into groups based on their values and properties, calculated automatically by Power BI.
Once you have your scatter chart, follow these simple steps:
Your scatter chart will now display the specified number of clusters, grouping the data points, accordingly, based on the criteria you've set.
Time Series Analysis is instrumental in representing time trends. Previously, we discussed the benefits of using a scatter chart. Now we'll explore how to take advantage of a scatter chart to analyze temporal data. Make sure that you have correctly configured the necessary fields in your scatter chart before proceeding with the following steps.
Now you can observe how the values fluctuate over time. Such visualizations are valuable for comparing product sales and analyzing general business growth trends.
Based on the detailed exploration of data analysis in Power BI presented in this article, we have seen what Power BI can do to effectively use data and what are the main components of Power BI for data analysis. It's clear that taking advantage of the Power BI suite of tools enhances the ability to transform raw data into useful information. The process is developed in different phases, each of them using Power BI features such as Power Query for data manipulation and DAX for creating calculated measures and tables.
By integrating these techniques, companies can not only understand historical trends, but also predict future results and prescribe optimal strategies. Effective data analysis in Power BI culminates in a compelling data narrative through visualizations and reports, ensuring that information is communicated clearly and decisively.
Ultimately, these are the reasons why you should use Power BI for data analysis. Mastering Power BI allows users to confidently navigate complex data sets, extracting valuable information that guides informed decisions and promotes business growth in today's dynamic landscape.
Here are the answers to some of the most frequently asked questions about how to use Power BI for data analysis, based on the content of our article.
Power BI, owned by Microsoft, is a business intelligence tool that provides sophisticated analysis, comprehensive reports, and dynamic visualizations. It allows seamless integration into cloud services, mobile devices, and web applications.
The main components include the 'Get Data' option for data import, 'Reports' for created views, 'Data' for viewing/editing data, 'Templates' for managing table relationships, and panels for filters, visualizations, and fields.
Power BI offers a series of tools and features to perform advanced data analysis, providing valuable insights that help businesses make informed decisions and maintain a competitive edge.
The five phases are:
Best practices include exploring statistics, grouping data, binning, clustering, and performing time series analysis to derive meaningful insights.
The Modern Work team effectively and swiftly addresses IT needs, primarily focusing on software development. The technical staff is well-trained in implementing software projects using Microsoft technology stacks and is skilled in managing both agile and long-term projects.
