Data visualisation and inference with Power BI charts

Data visualization is the graphical representation of information and data using visual elements like Power BI charts, graphs, maps, and infographics. Visualization reduces the cognitive load required to interpret and analyze data. By presenting data visually, decision-makers can focus on interpreting the information rather than struggling to comprehend it.

There is a wide variety of Power BI charts to analyze and visualise data meaningfully. Each type of visualization serves a specific purpose and is suited for different types of data analysis.

Importance of data visualization in analytics

Data visualization is a powerful tool for qualitative exploration and quantitative analysis of data. It promotes statistical literacy and other quantitative literacies (Gal, 2002). Visualization also distils large and complex datasets into clear and concise visuals, making it easier to identify patterns, trends, and outliers. Furthermore, by presenting data visually, information becomes more accessible to a broader audience, including those who may not have deep expertise in data analysis or statistical methods (Reilly, 2017).

Tools like Power BI reports allow for real-time monitoring and scenario analysis, to test various outcomes and make decisions that are responsive to changing conditions. Visualization also helps viewers gain a quick overview of current problem situations and intuitively prioritize different problems. It plays a crucial role in extracting hidden insights and valuable knowledge from big data. Researchers have also demonstrated that integrating big data analytics into the decision-making process provides valuable insights to enhance decision quality (Elgendy & Elragal, 2016). Overall, data visualization plays a vital role in any business, enabling rapid analysis of data that would otherwise be tedious (Pandey, Sharma, Sachan, & Madhavan, 2022).

Enhancing decision-making with Power BI charts

Visualization tools such as line and bar graphs aid in identifying patterns, trends, and correlations that may not be immediately apparent in raw data.

Visualization serves as a common language that can be understood across different levels of an organization, facilitating better communication and collaboration.

Visualizations are used to tell compelling stories, making it easier to engage stakeholders and drive consensus on important decisions. This can also highlight potential risks and anomalies in data, allowing decision-makers to take pre-emptive actions. Early identification of risks through visual tools can prevent small issues from escalating into significant problems.

Understanding the use of Power BI Stacked charts

Stacked charts are a type of data visualization where multiple data series are stacked on top of one another, typically in a single column or bar. They are useful for comparing parts of a whole, across categories, showing how individual segments contribute to a total. Stacked charts allow comparison of both the overall totals and the individual segments that make up those totals across different categories. The colour palette also plays an important role here. Choose colours that contrast with each other so that differences between the bars are visible.

Stacked charts are valuable tools for visualizing data, but they come with limitations that can impact the clarity and effectiveness of the information presented. As more data series or categories are added, stacked charts become difficult to read and interpret. Stacked charts are not always ideal for trend analysis as they can sometimes lead to misleading interpretations, particularly when segment sizes are similar. The visual impression might exaggerate or downplay differences between data series, depending on how the segments are ordered.

Enhance viewer interactivity by adding a Slicer to the report

Adding a slicer visual to a chart in Power BI enhances data exploration, interaction, and overall report viewer experience. Slicers allow viewers to filter data directly within reports. With Slicers report viewers get greater control over the data charted, allowing them to focus on specific segments of interest without modifying the underlying data model.

Getting the most out of the Ribbon charts

Ribbon charts are used to show ranking changes of data categories across a series of periods or other sequential data points. They are particularly useful for visualizing how the rank or order of categories shifts over time. Ribbon charts use wide, flowing bands or ribbons to represent data categories. The flowing nature of ribbon charts draws attention to the shifts in data in a more dynamic way compared to static charts like bar or line charts.

From the chart, it can be derived that Electronics consistently ranks high in terms of average sale value and maximum discount given but the number of units sold is less than others.

While ribbon charts are excellent for showing relative rankings and trends, they may not be as effective for conveying exact quantitative values. In the above example, the chart indicates that the electronics category consistently ranks highest in terms of average sale value, but it doesn’t convey the difference in sales volume between the Electronics and the Office supplies category. This lack of precise quantitative information could be a drawback if the goal is to understand the magnitude of differences between the categories.

How are inferences derived from the Waterfall charts?

Waterfall charts help in understanding how an initial value is affected by a series of intermediate positive or negative values, leading to a result. They are used in financial analysis, sales performance tracking, and other scenarios where it’s important to see the cumulative effect of sequential values. Waterfall charts are effective for highlighting variances between expected and actual results. This helps in understanding the variance and what caused deviations from the estimate. They are especially valuable in financial analysis, sales performance tracking, budgeting, project management, and inventory control.

The above waterfall chart visualizes the flow of units sold during promotional campaigns across each quarter and the change in the total number of units sold across all categories in every quarter. For this visualization, it can be said that the campaign’s effect on sales showed a slow start but by the end of the year, the campaign had been fruitful. In quarters one and two, there have been lower sales from the campaign leading to lower quarterly sales as compared to quarters 3 & 4. Here it can also be seen that the sales from the campaign are positively impacting the total units sold over time with the electronics performing well in Q2, Office supplies in Q3 and Accessories in Q4.

Using the Funnel chart to show different stages of a process

A funnel chart is useful in presenting the progression of data through different stages of a linear process. Each stage is represented as a segment of the funnel, with the width of the segment representing the size of the data at that stage. It is frequently used in a business environment to highlight a problem in a sequential process such as sales pipelines, customer journey and online conversion rates.

By visualizing the data in a funnel chart, businesses can quickly identify bottlenecks or inefficiencies in a process. If a stage is significantly narrower than the previous one, it indicates a potential issue that needs attention.

Funnel charts assume a linear progression; thus, it is not suitable for business processes with loops, parallel stages, or complex decision paths. Although Funnel charts provide a subjective overview of the data, but they lack the granularity needed to understand the underlying reasons behind the changes between stages. The data presented in a Funnel chart needs to be proportional at all stages. The width of each stage indicates the relative size of the data. However, if the stages are not appropriately scaled or if there are significant outliers, the visual proportions can be misleading.

Power BI’s Key Influencers Chart uses AI to analyse data

The Key Influencers chart is a powerful visual tool designed to understand the factors that drive a specific metric or outcome in a dataset. The Key Influencers chart automatically analyzes the data to identify the factors that have the most significant impact on the chosen outcome or target metric. Power BI’s AI capabilities automatically surface insights without the need for manual data mining or complex statistical analysis. This Power BI chart can point out relationships and patterns that might not be immediately apparent.

The above Power BI chart shows that the first influencer “Initial inventory” is correlated to the “Units sold”. As the sum of “Initial inventory” increases by 56.63, “Units sold” also on average increases by 8.56-unit growth. This also highlights that there has been negligible inventory depletion over the period.

According to the above Power BI chart the second influencer “Average discount percent” is in correlation with “Units sold”. As “Average discount percent” decreases the “Units sold” increases. A fall in “Average discount percent” by 4.35 leads to a 2.31-unit growth in “Units sold”. This also shows that “Average discount percent” is inversely proportional to “Units sold”.

This insight challenges conventional belief and presents an opportunity to refine the discount strategy, potentially leading to higher sales without the need for deep discounting. One possible explanation for this inverse relationship could be that customers perceive products with lower discounts as having higher value or quality. A high discount might signal to some customers that the product is not selling well or is of lower quality, leading them to purchase fewer units. It will be worth exploring if other factors could be influencing this relationship.

While the chart is excellent for identifying simple relationships, it may not fully capture complex interactions between multiple variables. The accuracy of the insights depends heavily on the quality and completeness of the data. Poor or biased data can lead to misleading conclusions.

References

• Elgendy, N., & Elragal, A. (2016). Big data analytics in support of the decision making process. Procedia Computer Science, (pp. 1071-1084). doi:https://doi.org/10.1016/j.procs.2016.09.251
• Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International Statistical Review, 1-51. doi:https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
• Pandey, A., Sharma, I., Sachan, A., & Madhavan, P. (2022). Comparative Study of Data Visualization Tools in BigData Analysis for Business Intelligence. International Journal for Research in Applied Science & Engineering Technology, 1-12. doi:https://doi.org/10.22214/ijraset.2022.44400
• Reilly, S. (2017). The Need to Help Journalists with Data and Information Visualization. IEEE Computer Graphics and Applications, 8-10. doi:https://doi.org/10.1109/MCG.2017.32