Data Visualization: turning Big Data into actionable insights

Standard techniques of data visualization

Depending on the kind of data you’re working with and the picture you want to depict, you will need to use different data visualization techniques. The standard methods include charts (line, bar, or pie), plots (bubble or scatter), diagrams, maps (heat maps, geographic maps, etc.), and matrices. Each offers specific variations to help you – the storyteller – convince the insightful messages.

Learn more about data story telling at: Practical lessons for data storytelling

Charts

The chart appropriately demonstrates the change in one or several data sets. The relationship between elements is more clearly expressed in some data visualizations, while others could confuse the viewers. When it comes to data, the most appropriate chart type depends on the purposes you’re trying to convey. There are four fundamental purposes:

• A relationship tries to demonstrate a connection or correlation between two or more variables using the data provided, like the development of semiconductor export over time versus the overall market trend.
  Some of the charts for relationship purposes: scatter graphs and bubble charts.

• A comparison tries to place one set of variables away from another, then show the interaction between the two sets of variables, like the number of visitors to three flagship stores in a single month.
  Some of the charts for comparison purposes: bar charts, line charts, column charts, etc.

• A composition tries to gather different data types that make up a whole and showcase them collectively, like the engagement or impression from a website over a month.
  Some of the charts for composition: pie charts, waterfall charts, etc.

• A distribution tries to lay out a collection of related or unrelated information simply to see how it correlates, if at all, and to understand if there’s any interaction between the variables, like the number of bugs reported during each month of a beta.
  Some of the charts for distribution purposes: column histograms, line histograms, etc.

Plots

Plotting and data visualization can tell different stories between features and target variables with actions like comparing quantities, studying trends, quantifying relationships, or displaying proportions.

Imperative components for designing an actionable plot:

• Data Component: what type of data it is, e.g., categorical data, discrete data, continuous data, time-series data, etc.

• Geometric Component: what kind of visualization is suitable for your data, e.g., scatter plots, line graphs, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, heatmaps, pie charts, etc.?

• Mapping Component: what variable to use as your independent variable (x-variable) and what to use as your dependent variable (y-variable)? This is important, especially when your dataset is multidimensional with several features.

• Scale Component: what kind of scales to use in your plot, e.g., linear scale, log scale, etc?

• Labels Component: other things like axes labels, titles, legends, font size, etc.

Maps

Maps depict the physical characteristics of the land, such as its regions, landscapes, cities, roads, and waterways. They allow locating elements on relevant objects and areas — geographical maps, building plans, website layouts, etc.

Diagrams and matrices

Diagrams are frequently used to show intricate data relationships and links and combine different forms of data into a single visual representation. It is suitable for processing maps, decision support, root cause analysis, idea fusion, and project planning.

Some of the most common types of diagrams are:

• Flowcharts
• Mind maps
• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Histograms
• Wireframes
• Site maps
• Use case diagrams

Matrix is one of the advanced data visualization techniques that help determine the correlation between multiple constantly updating (steaming) data sets.

Ultimate tools for Data Visualization

A data visualization tool is a form of software developed to visualize data. Although the features of each tool differ, at their most fundamental level, they all let you extract a dataset and visually alter it. Most have pre-built templates that you may use to produce basic visualizations.

1. Tableau

Tableau is one of the market’s most popular data visualization tools for two main reasons: it is relatively easy to use and compelling. The software can integrate with hundreds of sources to import data and output dozens of visualization types—from charts to maps.

Tableau boasts millions of users and community members owned by Salesforce, and it’s widely used at the enterprise level.

Tableau offers several products, including desktop, server, and web-hosted analytics platform versions and customer relationship management (CRM) software.
Pros

• Hundreds of data import options
• Mapping capability
• Free public version is available
• Lots of video tutorials to walk you through how to use Tableau

Cons

• Non-free versions are expensive ($70/month/user for the Tableau Creator software)
• The public version doesn’t allow you to keep data analyses private

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