Data visualization has been a game-changer. The use of data visualization has changed the way businesses conduct mutual trade with each other and in the market. Data visualization has opened a new paradigm in the field of data management and trade while making the understanding of the prediction of trends in business a lot more stable and predictable. Data Visualization has carved out a new path of discovering new insights hidden in plain sight in massive surges of data that earlier was not possible. Data Visualization reduces the strain in understanding the accumulation of a large dataset into one visual while being able to provide insight into the hidden trend or new marketing strategy within the data.
One such visualization art is data visualization using Power BI, developed by Microsoft which was initially launched in 2013. It is solely responsible for having revolutionized the way we used to implicate visualizations in PowerPoint presentations or Tableau. Below mentioned are a few key features of how power bi visualization types implicate the employment of its data visualization. But before we get into that let’s first understand
Power BI is an extremely powerful business intelligence tool with pre-integrated features that are responsible for fabricating or creating visually appealing and interactive visualization and dashboards by utilizing business intelligence. To read more about – Future of Power BI
Power BI is loaded with several power bi custom visuals lists, so sometimes the user loses track of the kind of visualization they need to employ to get the desired results.
Zone graphs are founded on line charts, where the zone between the x-axis and the line is occupied by either a pattern, texture, or color. Zone graphs are primarily employed: –
These are horizontal charts that signify or compare unconditional data. This styling of graphs may also be used to showcase negative data. Bar charts are employed: –
Although bar graphs are utilized to show the diversification between data, they may not help understand the link between the data.
Similar to bar graphs, clustered column graphs depict or group the data from a similar category into a cluster. The data depicted in a particular cluster may be compared with each other or with the data from the other clustered data. This style of a graph provides an extensive view of the multiple products grouped under the same category.
As the name implies is an amalgamation of line graphs and column graphs into one common graph. Combo graphs are employed to merge values that are usually difficult to combine as the values belong to distinct scales or units like pieces sold & revenue.
This style of combo graphs could be utilized to understand the gross profit achieved in comparison to the cost incurred in the production of the product in comparison to the sales made.
As the name suggests, these graphs depict data in the shape of a doughnut. They are similar to pie charts and can be employed to display the composition of the whole data in the designated proportions. This style of graphs comes in handy when showcasing the multiple magnitudes that make up a certain value.
Doughnut graphs may be used to represent the percentile of the revenue per financial quarter generated by different products.
Funnel graphs are employed to demonstrate the procedure that leads to an adaptation. These graphs are a good option when the data is chronological and when the first phase has a greater number of “items” than the final phase. It is analogous to a marketing funnel that demonstrates phases or stages that transmute a call into a lead.
Gauge graphs are utilized to express the development towards a precise goal i.e., it helps in showing how much of the envisioned goal has been accomplished. Gauge graphs are used to represent KPIs such as the yearly sales targets of a company. The minimum and maximum values are prearranged and the line in the center governs how far the user has proceeded towards the target. Gauge graphs are visually alluring but they can also take up a lot of storage space. If the user feels that less is more, then the user may consider a doughnut over gauge graph.
Line graphs are a graphic depiction of a sequence of data points that are all linked by a straight line and it is used to represent unceasing data sets. Line graphs can be employed to display the precise value of the planned data. For example, Line graphs can be used to exhibit scheduled trends of products traded. Influences arise concerning the measurement between time points. Some believe that data may be tracked frequently while others believe that measuring data at occasional recesses may prove to be adequate. Line graphs should only be used for time series or to map a trend over some time e.g., dates, months, years, etc.
Pie graphs are employed to demonstrate the arrangement of the complete data into distinct portions. Each module of a pie graph is signified in percentiles and the sum of all the parts should add up to 100%. Each share of a pie graph can be segregated into portions. Since the magnitudes in pie graphs are separated into parts, it may be difficult to classify the dimensions just by looking at them. Pie graphs are convenient when dealing with unconditional data. As an operator, one needs to make certain that the user should have the necessary data that imitate this type of graph. This graph however can be utilized with only one set of data. Examples of a pie graph can be employed to examine the percentile of pieces sold by geographical areas.
Scatter plots or Scatter Graphs can aid the user to display the association between two variables for a set of data. In scatter graphs, data points have conspired along their axis but unlike line graphs, they are not linked by an orthodox straight line. Scatter graphs can aid to regulate the link between positive, negative, or no correlation between the variables. For example, Scatter graphs can be used when measuring the relationship between employees’ income and their happiness.
Waterfall graphs can be used to illustrate how an initial value can be affected by adding or subtracting subsequent values from the original value to reach the final value. For example, the waterfall graph can be used to track the income for the rest of the year to determine the net profit at the end of the year. Let’s say the user has been taxed with the task to plot a company’s annual profit. The user may add the various sources of income and after deducting the cost the user will arrive at the profit or loss. In such a scenario, the waterfall graph can prove to be extremely beneficial.
The multiple options in power bi visualization types make it one of the best business intelligence tools in the market. Power BI visualization has ruled the Gartner quadrant for close to a decade as the world-leading business intelligence and the most popular data visualization tool. Power BI charts and visualization have created a deep impact in the world of analytics and become a sought-after topic in data visualization using power bi.
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