Guides → Visualizations

Data visualization gives a clear idea of what the information you have means; by giving it a visual context through maps, charts, or graphs. This makes the data easy to understand and comprehend; therefore, easier to identify trends, patterns, and outliers within large data sets that would facilitate the decision making process.

Incorta provides different visualization types that you can use to represent your data. But here comes up one of the most important questions: which type of visualization is suitable for my data and my business?

In this guide, we will tackle the following topics and points of interest:

• Importance of data visualization
• Visualization types
• Choose your visualization
• Configure your visualization
• Important pill properties and settings

Importance of data visualization

One of the essential steps in the business intelligence process is data visualization. It takes your data, models it, and plots it in graphs and charts so that you can understand and interpret your data. Such a process will help you reach conclusions and make decisions.

In Incorta, you can use Business Schemas, the Analyzer, and the formula builder to transform your raw data and better compile it into visualizations.

Data visualization uses visuals to communicate information in a manner that is universal, fast, and effective. This practice can help you identify which areas need to be improved, which factors affect customer satisfaction and dissatisfaction, and what to do with specific products, for example, where these products should go and who the target customers are. Visualized data gives stakeholders, business owners, and decision-makers a better prediction of sales volumes and future growth.

Visualization types

Incorta supports various visualization types including tables and charts with different dimensions that you can use to plot your data. A visualization can represent data sets in one dimension, two dimensions, or three dimensions based on the aggregated value(s) and number of attributes. To learn more about the visualization dimensions, review Concepts → Visualization.

The following lists the most common types of visualizations:

• Tables
• Numerical charts
• Bar charts
• Line charts
• Pie charts
• Map charts
• Scatter plots
• Organizational charts
• Hierarchy charts

Choose your visualization

To decide which visualization you need to use to better show your data and analytics findings, you need to ask yourself few questions:

• What type of data are you trying to visualize? Is it integer, real, categorical, time series, geospatial, ... and so on?
• What are you trying to communicate? Is it data comparison, composition, distribution, trends, relationships, .. and so on?
• Who are the end-users consuming the information? Are they fellow analysts, board members, a CEO, … and so on?

Knowing the scale of measurement is also an important aspect in choosing the right visualization. Visualizations can display different data scales: nominal, ordinal, interval, or ratio, where nominal and ordinal are qualitative data, while interval and ratio are quantitative data.

• Nominal data are categorical and have no order in place, such as gender, eye color, or blood type.
• Ordinal data are ranked and ordered categories, such as customer satisfaction ratings, economic level.
• Interval data are ordered values with an equal range between two values, such as temperature scales, time in years, or credit scores.
• Ratio data have the interval data properties as well as a clear definition of zero, such as weight, length, or pulse.

Based on your answers to the above questions and your scale of measurement, the type of visualization you will decide to use will differ. A visualization is not always specific to a single purpose, a visualization can serve multiple purposes between data comparison, data distribution, and so on.

The following table illustrates all the visualizations in Incorta and when to use each accompanied by examples:

Visualization	Utilization
●   KPI (numerical value)   ●   Gauge (scale range)   ●   Solid Gauge (half ring scale range)	Highlight the performance and progression towards a goal Use the Key Performance Indicator (KPI) visualization to evaluate performance and measure success, and use the Gauge and Solid Gauge visualizations to track progress towards a goal. Examples: Revenue, Gross Profit Margin, Cost, and Total Profit for the fiscal year. To learn more, review the KPI, Gauge, and Solid Gauge documents.
●   Listing Table   ●   Aggregated Table   ●   Pivot Table	Data comparison in a tabular view (rows and columns) Use Tables to compare or look up individual and precise values that can involve multiple units of measure. Listing Tables are ideal for viewing your data at a granular level, while Aggregated Tables and Pivot Tables are for utilizing aggregations and changing the granularity of your data. Examples: Product revenue in high value European markets and Pricing of products in 2021. To learn more, review the Listing Table, Aggregated Table, and Pivot Table documents.
●   Bar (horizontally oriented)   ●   Column (vertically oriented)	Data comparison at the same scale and data distribution among various categories Use Bar charts and Column charts to compare measures across multiple categories at the same scale and to display data sets with positive and negative values. Bar charts and Column charts are ideal for displaying nominal, ordinal, or interval (in categories) data along one axis, and ratio data along the other axis. Examples: Quarter sales by product categories, Most common items sold by a store location, and Sales performance by sales representative. To learn more, review the Bar and Column documents.
●   Line (data points connected with straight line segments)   ●   Line Time Series (data points connected with sharp lines)   ●   Time Series (data points connected with lines in the form of waves)	Data comparison to show trends and changes over time Use Line charts, Line Time Series charts, and Time Series charts to compare data that change over time, to highlight patterns and trends, to show relationships within a continuous data set, and make predictions. Line charts are ideal for displaying ordinal, interval, and ratio data. Examples: Sales volume growth over a three-year period, Currency exchange rates, and Product cost over years. To learn more, review the Line, Line Time Series, and Time Series documents.
●   Area (color filled area between the axis and line)   ●   Area Range (color filled area between high and low values)	Data comparison to show a time-series relationship with a volume indication Use Area charts to compare data that change over time, identify the magnitude or volume of the change, and show the overall trend. Use Area Range charts to compare changes in data between high and low values over a timespan. Examples: Quarterly sales per product categories, Coffee range prices in the USA over a five-year period, and Source of job applicants (Social Media, Referrals, and Organic Search) over years. To learn more, review the Area and Area Range documents.
●   Stacked Area (vertically stacked)   ●   Stacked Bar (horizontally stacked)   ●   Stacked Column (vertically stacked)   ●   Stacked Line (data points connected with stacked lines)   ●   Stacked Column and Line (combined charts with dual Y-Axis)	Data comparison among categories and data composition with part-to-whole relationships Use Stacked Area charts, Stacked Bar charts, and Stacked Column charts to compare between categories of data with a part-to-whole relationship. Use Stacked Line charts to compare trends and patterns of data over time. Use Stacked Column and Line charts to visualize and compare two data sets with different scales (y-axes) and related dependencies (x-axis). Examples: Profit and revenue over a 5-year period, Cost per product category over years, and Gross Margin percent per product category. To learn more, review the Stacked Area, Stacked Bar, Stacked Column, Stacked Line, and Stacked Column and Line documents.
●   Combo (data points plotted as line segments)	Data comparison at the same scale Use Combo charts to compare multiple measures that have the same scale against a data category. Examples: Revenue and cost per product subcategory. To learn more, review the Combo document.
●   Combo Dual Axis   ●   Dual X-axis	Data comparison at different scales Use Dual Axis charts to compare and correlate multiple measures at different scales. If you have two categories of data sets that require two grouping dimensions, use Combo Dual Axis, otherwise, use Dual X-Axis. Example: Revenue, cost, and units sold per region and country. To learn more, review the Combo Dual Axis and Dual X-Axis documents.
●   Spider (also known as Radar or Star)	Data comparison between multiple measures Use Spider charts to compare multiple measures against a single dimension and visualize the differences between measures. Examples: Cost and Revenue per product category, Revenue and Profit by region. To learn more, review the Spider document.
●   Radial Bar (data points plotted on a polar coordinate system)	Data comparison among categories and composition among sub-categories Use Radial Bar charts to compare between categories using circular bars in which longer bars represent larger values. Each bar can be divided into segments to represent subcategories using the Coloring Dimension. Examples: Position count per department, Top 5 products by Gross Margin percent. To learn more, review the Radial Bar document.
●   Pie circular graph divided into slices)   ●   Donut (variant of a Pie without a center area)   ●   Pie Donut (circular graph divided into slices and segments)	Data composition with part-to-whole relationships Use Pie charts, Donut charts, and Pie donut charts to display data categories as proportions or percentages of a whole. Pie charts and Donut charts are ideal for displaying nominal and ordinal data. If you have more than six categories to visualize, consider using a Donut chart instead of a Pie chart. Examples: Budget allocations per department, Profit per product category and subcategory, and Revenue per region. To learn more, review Pie, Donut , and Pie Donut documents.
●   Combintation (Column and Pie chart)	Data comparison and composition Use a Combination chart to compare data sets across multiple categories at different locations or time periods (Column chart) and display the categories as proportions of a whole (Pie chart). Combination charts are ideal for displaying nominal and ordinal data. Examples: Revenue per region and category, Average transaction size by payment type per Country, and Number of purchases made on a site by different types of users. To learn more, review the Combination document.
●   Percent Area   ●   Percent Bar   ●   Percent Column   ●   Percent Line	Data composition in terms of value-based contribution percentage Use Percent charts is to compare the value-based contribution of each individual item to a 100% total (sum) across categories. Examples: Revenue percentage by product category and Product Gross Margin percent. To learn more, review the Percent Area, Percent Bar, Percent Column, and Percent Line documents.
●   Map (locations on a map)   ●   Advanced Map (geographical layers on a map)   ●   Bubble Map (bubbles of varying sizes plotted over geographic points on a map)	Geospatial analysis of data Use Map charts, Advanced Map charts, and Bubble Map charts to visualize and compare data sets on a map, show categories across geographical regions, and display variations or geographical trends within the data. Examples: Revenue per country, Average housing prices in different regions, Number of customers per country, and Population of prominent German cities. To learn more, review the Map, Advanced Map, and Bubble Map documents.
●   Funnel   ●   Pyramid	Data hierarchy and process stages Use Funnel charts and Pyramid charts to represent categories based on their hierarchy, importance, quantity, or size. Funnel charts are ideal for demonstrating the flow of customers through a business or sales process and Pyramid charts are ideal for representing hierarchies. Examples: Customer retention and Sales pipelines. To learn more, review the Funnel and Pyramid documents.
●   Bubble	Data relationship Use Bubble charts to identify relations between three data sets by using the positions and sizes of the bubbles. The first two measures determine the location of the bubbles on the x-axis and y-axis while the third measure determines the size of the bubbles. Example: Revenue, Cost, and Profit per product category. To learn more, review the Bubble document.
●   Packed Bubble	Data relationship without scale Use Packed Bubble charts to quickly show relational values and aggregated measures without using a scale. Dimensions define the individual bubbles, and measures define the size and color of the bubbles. Example: Revenue Per Product Subcategory with respect to Category. To learn more, review the Packed Bubble document.
●   Waterfall	Data relationship and comparison across time or categories Use Waterfall charts to show the running total of a series of positive and negative values that is time-based or category-based. Examples: Gross profit per region, Total sales variance per fiscal year, and Net income per quarter. To learn more, review the Waterfall document.
●   Scatter (data points plotted on an x-axis and a y-axis)	Data relationship and distribution without regard to time Use a Scatter chart to compare and show correlations between two measures and to identify outliers within your data. Scatter charts are ideal for displaying nominal, ordinal, or interval data. Examples: Product revenue versus cost, Correlation between advertising costs and units sold per product. To learn more, review the Scatter document.
●   Heatmap	Data relationship and variance across multiple variables Use Heatmap charts to analyze the relationship between the two variables placed in rows and columns and reveal patterns using the color of the cells. A Heatmap chart has a dark-to-light color scheme, where the dark colors represent high-value data points and the light colors represent low-value data points. Examples: Segmentation analysis of target market, Cost by categories in Europe, and Product adoption across regions. To learn more, review the Heatmap document.
●   Treemap	Comparative analysis of hierarchical data Use Treemap charts to view and complex large data sets and hierarchical data sets as a proportion of a whole. Examples: Cost and gross profit per product category, Number and priority of technical support tickets, and Comparing fiscal budgets between years. To learn more, review the Treemap document.
●   Organizational	Representation of hierarchical data Use Organizational charts to illustrate the structure of an organization as well as the relationships within it, such as the chain of command from any employee all the way to the top. Examples: Organization structure, Board of directors. To learn more, review the Organizational document.
●   Sunburst (a multi-level Pie chart)	Representation of hierarchical data and categorical data Use Sunburst charts to represent categorical or hierarchical data using concentric circles, where each ring represents a level and the center circle represents the top of the hierarchy. Rings can be divided into sections to represent multiple divisions within the same organizational level or to represent subcategories. Examples: Department stores, Revenue per category, subcategory, and product. To learn more, review the Sunburst document.
●   Tag Cloud (also known as Word Cloud)	Data frequency to show trends Use Tag Cloud charts to identify trends and show frequencies of categories in terms of text font sizes and weights. Example: Customer feedback (identify your customer’s satisfaction based on the size of words, such as “convenience”, “quality”) To learn more, review the Tag Cloud document.
●   Sankey (flow diagram)	Data Mapping between domains Use Sankey charts to map data between domains by depicting the flow rate between two or more nodes. A node can be a source node, a target node, or both. Incorta dynamically determines an intermediary node, a node which is a source and a target. For intermediary nodes, you can select a node in the visualization, and filter by source or target. Examples: Flow of resources between countries, Largest export partners, and Traffic flow between pages on a website. To learn more, review the Sankey document.
●   Rich Text	Descriptive text-based visual Use Rich Text to make dashboards more readable and understandable. You can insert text, images, and other elements using an embedded text editor. Incorta stores the Rich Text visualization as HTML. To learn more, review the Rich Text document.

Configure your visualization

After choosing the suitable visualization in the Analyzer, Incorta enables you to configure the pill properties, filter properties, as well as the settings to create an insight.

The distinction between a visualization and an insight is that an insight is an instance of the visualization. As an instance, an insight has unique configurations for displaying a chart on a dashboard tab. Insight configurations affect the available dashboard user interactions, such as the ability to drill down into a grouping dimension and/or coloring dimension.

The insight settings and pill properties determine how a visualization appears as an insight on a dashboard tab and how you can interact with the insight.

From the Data panel, add a column or a formula to a tray. A column or a formula in a tray is a Pill. Each pill has configurable properties and the parent tray determines the available properties of a pill.

Here are the common available trays for an insight:

• Grouping Dimension — for grouping categorical information within your data
• Coloring Dimension — for distinguishing dimension categories by color
• Measure — for calculating or aggregating numerical data or for counting non-numerical data
• Individual Filter — for filtering data sets to reduce the amount of data shown in an insight
• Aggregate Filter — for defining a filter expression that contains an aggregation or a calculation. Using the Filter panel, you can specify the filter operator, filter values, or the filter expression with the Formula Builder.

Important pill properties and settings

Here are some useful configurable properties to control the layout of your visualization(s):

Pill properties

Settings Panel