The need for Advanced Data Visualization in Business Intelligence
DashboardWorx
Data analytics consulting. Building an advanced data analytics toolkit for Tableau and PowerBI users.
Data visualization is an efficient way of conveying the business’s message to many people concisely and beautifully. In today’s world, behemoths and small businesses rely on mass disseminating data to increase their visibility in such a competitive market. However, traditional data visualization techniques have lost their charm in an environment that demands even better methods to aesthetically visualize data quickly, user-friendly, and informative. Traditional data visualization methods such as bar graphs, pie charts, etc., failed to highlight hidden insights for a company’s growth since they cannot indicate key assumptions, patterns, impacts, and other relevant metrics. This is where advanced data visualization takes a massive lead. This article will explain the need for advanced data visualization in business intelligence.
DEFINING ADVANCED DATA VISUALISATION
With the advent of large-scale creation of data, expedited by the Internet and social media, the dire need for a mechanism to circulate it efficiently and speedily was very prominent. Data visualization emerged as an essential step in the business intelligence process which compiles large amounts of data and models it in a way that is easy to comprehend. Advanced-Data Visualization (henceforth ADV) is the refined and sophisticated version of visualisation techniques that uses machine learning and automated technologies to make more analytical and comprehensive reports for all relevant stakeholders, make predictions, derive hidden insights, and generate recommendations.
HOW IS ADV DIFFERENT FROM TRADITIONAL DATA VISUALIZATION TECHNIQUES?
Traditional BI systems are primarily used in situations when a professional (data scientists or data analysts) with expertise in the field is involved in the process of data visualization. Traditional techniques rely on ‘hypothesis-based reporting,’ and its primary data is past results. A few of the most well-known traditional data visualization methods include bar graphs, pie charts, line plots, maps, etc. They are effective in several scenarios, but in certain instances can be ineffective in depicting insights to base decisions on; they provide incomplete analysis and require a lot of additional content to explain the hidden insights. In such a scenario, the difference between the benefits extracted from traditional and advanced data visualization techniques is stark and eye-opening.
Six core capabilities make up what makes advanced data visualization different from traditional BI graphs and charts. These are charts that have
Traditional Data Visualization: Pie Chart
Advanced Data Visualization
ADVANCED DATA VISUALIZATION AND BI
Business Intelligence (BI) is a composite package of processes and structures a business firm uses to make more data-driven and analyzed decisions. The idea is to collect information from as many sources as possible, internal and external, organize it for analysis, and produce an impactful report to facilitate more significant ROIs, optimal functioning, higher customer satisfaction, and technologically-backed decisions. Today, ADV plays a highly significant role in contributing to the cause of BI.
However, applications like Tableau are not naturally equipped with the technology needed to create advanced data visuals. While it may be the champion of traditional data visualization, it fails to cater to the dynamic needs of the market today. It may serve as the go-to software for someone's content using basic bar graphs, pie charts, and maps for visualization, but it does not provide what big data calls for. So, the visualizations may not be glove-in-hand for your business. What can be seen as a general trend, data analysts have had to shift their operations from Tableau to other software to produce meaningful advanced data visualizations such as Python and R libraries.
Some examples of advanced data visualisations
Cluster Map
When you use a dendrogram to display the result of your cluster analysis, it's always good practice to add an interactive heatmap that allows users can see how entities are structurally organized.
Violin Plot
A violin plot is a hybrid of the box plot and kernel density plots, which can be used to visualize data distribution. It's helpful for showing both summary statistics as well as how variables are distributed in your sample set.?
Joint Histogram?
It is used to analyze the relationship among two data variables which has a wide range of values. A Joint histogram is very similar to 1D histogram. It is drawn by including the total number of combinations of the values which occur in intervals of x and y, and marking the densities. It is useful when there is a large amount of data in a discrete distribution, and simplifies it by visualizing the points where the frequencies of variables are dense.
Bivariate density plot
In a one-dimensional density plot, the height of the curve was related to the relative density of points in the surrounding region. In a bivariate density plot, nested contours (or contours plus colours) indicate regions of higher local density
Scatter plot matrix
A scatter plot matrix is?a grid (or matrix) of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships to be explored in one chart.
?