Visualize it...!

A Comprehensive Guide to Data Visualization

As we all know, a picture is worth a thousand words – especially when you are trying to understand and discover insights from data. Visuals are especially helpful when you’re trying to find relationships among hundreds or thousands of variables to determine their relative importance – or if they are important at all.

What’s inside...

1) Introduction

2) Data types, relationships, and visualization formats

3) Basic principles for data visualization

4) Storytelling for social and market communication

5) Trends in market research and data visualization dashboards

1)  Introduction

What is data visualization?

Data visualization is the process of acquiring, interpreting and comparing data in order to clearly communicate complex ideas, thereby facilitating the identification and analysis of meaningful patterns.

“Data visualization can be essential to strategic communication: it helps us interpret available data; detect patterns, trends, and anomalies; make decisions; and analyse inherent processes. All told, it can have a powerful impact on the business world.”

Data visualization chiefly helps in 3 key aspects of reports and statements:

a) Explaining

Visuals aim to lead the viewer down a path in order to describe situations, answer questions, support decisions, communicate information, or solve specific problems. When you attempt to explain something through data visualization, you start with a question, which interacts with the data set in such a way that enables viewers to make a decision and, subsequently, answer the question.

b) Exploring

Some visuals are designed to lend a data set spatial dimensions, or to offer numerous subsets of data in order to raise questions, find answers, and discover opportunities. When the goal of a visual is to explore, the viewers start by familiarizing themselves with the data set, then identifying an area of interest, asking questions, exploring, and finding several solutions or answers.

c) Analyzing

Other visuals prompt viewers to inspect and transform the most significant information in a data set so that they can discover something new or predict upcoming situations.

2)  Data types, relationships and visualization formats

Data Types:

There are 2 kinds of data before we talk about visuals themselves, we must first understand the different kinds of data that can be visualized and how they relate to one another.

1)   Quantitative (numeric) – Discrete and Continuous

Data that can be quantified and measured. This kind of data explains a trend or the results of research through numeric values

2) Qualitative - Ordinal and Categorical

This kind of data is divided into categories based on non-numeric characteristics. It may or may not have a logical order, and it measures qualities and generates categorical answers

Data relationship:

Data relationships can be simple, like the progress of a single metric over time or they can be complex, precisely comparing relationships, revealing structure, and extracting patterns from data.

a.    Ranking

b.    Nominal Comparisons

c.    Correlations

d.    Series overtime

e.    Deviation

f.     Distribution

g.    Partial or total relationship

Visualization Format:

There are two types of visualizations: static and interactive

Static visuals can only analyse data in one dimension, whereas interactive visuals can analyse it in several.

Selecting the right graphic to effectively communicate through our visualizations is no easy task. Stephen Few (2009), a specialist in data visualization, proposes taking a practical approach to selecting and using an appropriate graphic:

• Choose a graphic that will capture the viewer’s attention for sure.
• Represent the information in a simple, clear, and precise way (avoid unnecessary flourishes).
• Make it easy to compare data; highlight trends and differences.
• Establish an order for the elements based on the quantity that they represent; that is, detect maximums and minimums.
• Give the viewer a clear way to explore the graphic and understand its goal.

3)  Basic principles for data visualization

The first step in representing information is trying to understand that data visualization.

1)   Overview first

This ensures viewers have a general understanding of the data set, as their starting point for exploration. This means offering them a visual snapshot of the different kinds of data, explaining their relationship in a single glance. This strategy helps us visualize the data, at all its different levels, at one time.

·      System Content: The system plus users and system dependencies

·      Containers: The overall shape of the architecture and technology choices

2)   Zoom and filter

The second step involves supplementing the first so that viewers understand the data’s underlying structure. The zoom in/zoom out mechanism enables us to select interesting subsets of data that meet certain criteria while maintaining the sense of position and context.

Components: Logical components and their interactions within a container.

3)   Details on demand

This makes it possible to select a narrower subset of data, enabling the user to interact with the information and use filters by hovering or clicking on the data to pull up additional information

Classes: Component or pattern implementation details

4)  Storytelling for social and market communication

We cannot live without communicating, without expressing our personalities, emotions, and moods, our worries and fears.

We all love good stories, and data is one of the best tools for telling them. Millions of pieces of data are generated every day. They could be converted into great stories, but instead they are left unused. It’s time to change all that. It’s time to start telling stories that draw their power from data.

So-called “data storytelling” is nothing more than placing a structured focus on the way we use data to communicate insights. It relies on three key elements: narrative, visualization, and data.

A basic recipe for storytelling in your presentations and final reports –

• Find the story in your data
• Define the perspective
• Create a hierarchy
• Organize
• Plot
• Use data to anchor your narrative
• Design Principles
• Review, review, review…
• Be familiar with your content and respect your audience
• Keep it short and sweet

5)  Trends in market research and data visualization dashboards

Data visualization technologies and methods continue to evolve in important ways. This cutting-edge report reflects the most relevant alternatives available on the market that can be used to work in this field. In both the software industry and the academic sector, several paths for innovation and development are at the forefront, including: scrollytelling, social-first data visualization, and virtual reality visualizations.

Storytelling: “Storytelling” is a technique that we’ve all experienced first-hand when viewing certain info graphics or websites. As the name implies, it aims to tell a story as users scroll through a graphic.

Virtual reality visualizations

Virtual reality has the potential to revolutionize data visualization, especially when it comes to big data. Even in a two-dimensional image, there is already too much data for the human eye to capture. Now imagine a three-dimensional data visualization, which allows the user to fully interact with data in a 360-degree field of vision.

Virtual reality data visualizations are highly interactive, computer generated 3D projections. Although the concept of virtual reality is nothing new, the idea of immersive data exploration certainly is, and the exciting possibilities that it promises are endless

What does the future have in store?

Visual data representation techniques and methods progress every day, as technology evolves, and our body of theoretical knowledge grows. As this technology and this knowledge work in tandem, we will continue developing solutions for our problems and needs. From this report, we hope you have deduced that, in our current era, images are the most efficient language. I hope you now understand that tools and software can help us discover limitless graphic resources and develop new structures for communicating and conveying ideas. Consequently, I can confidently state that the applications of graphic representation are constantly expanding, and we must not forget that they are the objective of our communication strategies in market research.

Thank you for reading.

Source : Google Engine