Research paper

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Eight Hats of Data Visualization

Submitted by

Team Name: Brief

Ahmed Farzan Anik

Suganya Madeswaran

Avijit Barua Chowdhury

Dhruvil Shukla

Submitted to Dr. Festus Elleh

Submission Date June 30, 2019

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Table of Contents:

Abstract………………………………………..………………………………….3

Introduction……………………………………… …………………….…….…..4

Hypothesis…………………..…………………………………………………….4

Methodology…………………………….…… …………………………., .……..5

Initiator………………………………..…………………………………..……….5

Data Scientist……………………………………… ……………………., …..…..6

Journalist…………………………………………………………………..……….7

Computer Scientist……………………….… …………………………….. .……..8

Designer…………………………………………………………………...……….9

Cognitive Scientist……………………………………… ………………………..10

Communicator…..……………………………………… …………….…………..11

Project Manager..……………………………………… ………….….…………..12

Data Analysis…….…..……………………………………… …………………...13

Limitation…….…..…………….……………………… …………….…………...13

Conclusion…….…..…………….……………………… ……………,,,,………...14

Reference…………………………………………………………………………..15

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Abstract

The purpose of this research paper is to assess the effectiveness of “Eight Data Visualization” hats.

Data visualization and data design are two integral part of how to effectively balance functionality

with presentation. How our data is designed tests us to make information more visual and

interesting to those who would not otherwise find it so. And that, in turn, increases understanding.

And effectively scheming data visualizations that delivers relevant, meaningful, and useful

understandings with proper information requires balancing our design by using the eight hats of

data visualization. We looked into the demand of a visualization designer, which is indeed mixture

of multidisciplinary subjects. The assembly of these assorted subjects presents an amazing richness

and assortment of issues to be worried about, however it can similarly introduce a significant test

for individuals hoping to ace data visualization. Depending on any project aspects, for any data

visualizer to acquiring these different arrays of capabilities might pose a barrier. To effectively

make a project successful data visualization may require one person with all the capabilities from

different areas of expertise or sperate people for each area.

Introduction

Data visualization combines techniques that effectively communicates data and information in a

meaningful pattern. It contains graphs and patterns that can be used by the people to understand

and interpret the data displayed. The goal of visualization is to communicate the data efficiently

to the users. Data visualization is an intriguing art and combines many sciences and patterns within

itself.

Visualization always require people to try and deliberate their mindsets to tackle the problems in

the right way and not from a constrained perspective. This makes the 8 hats of visualization very

useful in implementing successful visualizations. A number of reasons motivated Kirk, to create

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the 8 hats design. The most important among that would be the Edward de Bono’s 6 thinking hats

who detailed every aspects of thoughts in order to reason with tough and complex problems.

The 8 hats of data visualization include the following:

• Initiator

• Data Scientist

• Journalist

• Computer Scientist

• Designer

• Cognitive Scientist

• Communicator

• Project Manager

This paper explains what each role is about and explains how it improves the Data Visualization

patterns and the methods to get the desired solution for the main problem.

Keywords:

Data Visualization Design, Initiator, Data Scientist, Journalist, Computer Scientist, Designer,

Cognitive Scientist, Gestalt Law, Communicator, Project Manager, 8 Hats

Hypothesis

Null:

Eight Hats have significant effects to ensure any Data Visualization Design that is undertaken will

achieve its desired result.

Alternative:

Eight Hats don’t have significant effects to ensure any Data Visualization Design that is

undertaken will achieve its desired result.

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Methodology:

Market research generally involves two different types of research: primary and secondary.

Primary research is research we perform ourselves It involves going directly to a source —usually

samples or prospective samples in your target areas — to ask questions and gather information

like interviews, surveys, Questionnaires (online or mail), focus groups etc. Secondary research is

a type of research that has already been compiled, gathered, organized and published by others. It

includes reports and studies by government agencies, university researches, scholarly articles or

other web-based data source in the same field. For us, because of the time constrain and

unavailability of primary data source our methodology of data collection is secondary data source.

This is also free of cost.

Literature Review

Initiator:

The initiator is the primary source in any problem-solving technique. The initiator is the leader

who is actively participating in finding an answer to the problem. The qualities of leaders are:

curious, problem solver, opportunity seeker and energetic. The initiator wears the hat of an

explorer. An explorer is always curious and seeks to find answers to problems. The initiator wears

that hat and seeks to find answers to the main problems and finds way of coordinating people in

the team and they feed their minds with the proper evidence and seeks to find answers to the given

problem.

Since the initiator has a little bit of thinker in him, he fulfills the mind of the thinker by asking

questions among the team and willing to find answers for the main topic. Establishment of

analytical direction of the project is done by initiator. He finds the purpose and motive of the

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project and the needs and wants of the people working under his guidance. The initiator decides

the tone and the style of the given problem and sets the team according to his specification. He

asks questions like,” Is it about explaining or exploring or expression? Is it something that is

motivated by a need to facilitate maximum interpretation accuracy and efficiency or more about

creating an emotional engagement with the subject matter?” (Kirk, 2012)

Coming to data visualization perspective and how initiator influences the design, the initiator is

the key person who defines the parameters of the project and he defines the clarity and the key

technological topics surrounding the issue or the problem statement. He also decides on the

platform that the project work should take place and the roles and responsibilities of each member

of the team. He also sets the target audience and the design related to the visualization design. A

good visualization always depends on the platform being chosen and how one easily understands

the point that we are trying to explain. This job falls in the hand of an initiator who decides on the

design and specifies the project requirements.

Data Scientist:

The data scientist wears the hat of the miner, who deals with data at a large scale. The data scientists

are responsible of sourcing, acquiring, dealing and finally preparing the data. This generally means

that they have the technical skills to handle data and make it effective. The characteristics best

suits the Data Scientist are: researchers mind, explorer and good analyzer.

The data scientist has the capability to work with either large or small amounts of data. After

acquiring the data, the data scientist is the one responsible for handling and preparing the data for

the project.

Any good visualization design has to have accurate data statistics. The responsibility falls in the

hand of the data scientist to carefully analyze and prepare the data for the design. The data scientist

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holds the key statistical and mathematical knowledge of the visualization design. The design

should have the concept of applying visual analysis to learn about the pattern being produced. The

data scientist is the key person who does this task of making the learned get to know about the

visualization pattern and the logic around it.

“An informative visualization serves the relationship between the reader and the data. It aims for

neutral presentation of the facts in such a way that it will educate the reader. Informative

visualizations are often associated with broader data sets, and seek to distill the content into a

manageably consumable form.” (Ilinsky, Steele, 2011). This explains the job of data scientist to

present the data into a consumable and efficient way.

There may also be a necessity where the data scientist has to consider and mashup additional data

to create useful and elegant data that can be used for visualization. The primary purpose of

visualization is to create meaningful presentations which lies in the hands of data scientist.

Let’s first explore what’s the job of a journalist. Then we will link how that concept links with

effective data visualization design. Primary job of journalist is to educate others regarding an event

or story. They need to create a hypothesis, then gather & present data around that hypothesis. For

this they need to dig into public information and other data sources to support their assumption.

So, journalist must be aware of all the facts regarding their assumption, hypothesis, assumption to

their supporting points and present those facts to their audience. Now, for data visualization design

someone has to perform same types of activities.

Journalist:

Journalist is the storyteller, the individual who builds up the descriptive way to deal with the story

they are trying to analyze. It is ridiculous to expect that information representation devices and

procedures will release a torrent of instant stories from datasets. There are no guidelines, no

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'convention' that will promise anyone a narrative story beforehand. Rather, I think it makes more

sense to search for 'experiences', which can be cunningly knit into stories in the hands of a decent

writer. Working with the Data Scientist and the Initiator, Journalist has the eye for the key stories

and edges with which to cut the examination and present the stories. They work on keeping the

data questions in line so that it doesn’t derail from its planned editorial path. Expanding on the

Initiator's underlying thought the Journalist will build up a more profound scientist outlook to truly

investigate the logical opportunities. As with any examination procedure, the methodology might

be inductive or deductive, yet in any case, the journalist is at the core of the activity, looking to

discover the responses to move the venture on from the preliminary/preparation action and on to

its planned arrangement.

All of the above points are indicating to one direction and that is journalist hat cannot be deserted

in any way. Chances are ignorance might derail the project from its planned course of action.

Computer Scientist:

Computer scientist is one of the important hats of data visualization. It plays vital role in providing

solutions of the given project. Computer scientist is known as executor in a team that bring the

project alive to work with data scientist. Computer scientist bring ability to analyze problems and

trace them to their core causes. It brings the computer system in a constant manner to coordinate

the work and solve the problem that comes in task completion.

Role of Computer Scientist in Data Visualization

Computer scientist is a critical thinker and has technical ability to acquire, analyze and reviewing

the data with their technical and programming skills. Computer scientist are responsible for doing

research, create, and analyze the data design and implement the software and hardware technical

innovation. Computer scientist may closely work with data scientist to solve complex data

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visualization problems. The computer scientist has more role towards software programming

language like Java, C++, Python, Power Shell and many more. Computer Scientist write the script

in programming language that is connected to certain dataset so when data scientist looking for a

particular data from the database they can refer the scripts for it to write the query. Computer

Scientist provide consultation to their managers, end users, data analyst and business analyst for

the database related activities. “It might seem like a relatively small thing but being able to analyze

and pick up what’s going on with existing code is an important part of programming. You’re not

likely to work on an app alone and employers would love to keep handoff-related downtime to

minimum.” (January, 2019). Computer Scientist also create and control robots and write

algorithms that are used to design, analyze and detect the pattern in large sets of data from the

database which is useful for the data visualization. So, this is the complete role of Computer

Scientist I have explain in this paper.

The Designer:

The most creative person among the 8 hat is the designer. Designer basically in harmony with the

computer scientist. The designer brings the solution through computer scientist. The other name

of designer is the innovator. Because designer have the eye of visual detail and essence of

innovation. Usually the designer is very stylish and always need to know the latest trend.

Designer need to follow some chain of command also. They need to follow the message established

by Initiator and taken by journalist. Designer have a top view about the solution. As a result, they

can tell which work or and which will not work.

Designer need to work out with the five layers also. Those five layers of visualization anatomy:

Data representation

Color and background

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Layout and arrangement

Animation and interaction

Annotation layer

From the article of ‘The 8 hat of data visualization design’, we learn that

“Designer key responsibility is also to be capable of ensuring the harmony of the solution between

its form and its function, ensuring it is aesthetically appealing to draw in the reader whilst

fundamentally delivering the intended, communicated message.” (Kirk, 2012)

Cognitive scientist:

The main thinker of the data visualization design is the cognitive scientist. Cognitive scientist

makes sure that the eye and the brain work together and efficiently. From the article of ‘The 8 hat

of data visualization design’, we know that, “Cognitive Scientist appreciating the science behind

the effectiveness of the technical and designed solutions. Moreover, they have the visual

perception understanding to inform how the eye and the brain work most effectively and

efficiently.” (Kirk, 2012)

Cognitive scientist also knows, how about the principles of theories, the gestalt laws are familiar

with color theories. Let’s explain the gestalt law;

“The Gestalt Principles are a set of laws arising from 1920s’ psychology, describing how humans

typically see objects by grouping similar elements, recognizing patterns and simplifying complex

images. Designers use these to engage users via powerful -yet natural- “tricks” of perspective and

best practice design standards.” (www.interaction-design.org, 2012)

Communicator:

Now we will delve into the communicator hat of data visualization. To be able to present data in

the most effective way communication must be the integral part of this process. It is essential that

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in data visualization someone must be a good communicator, regardless of the method of

communication. He/she must be able to make his/her point clearly and unambiguously. It is also

important that he/she know how to ask insightful questions to retrieve the information they need

from stakeholders. For example, if stakeholder isn’t a technically sound, then a good

communicator may need to explain outcomes, issues in plain words – avoiding complex technical

terms. Being able to communicate information at the appropriate level is vital – some stakeholders

will need more detailed information than others. So here the communicator needs to have detailed

knowledge of the context.

In our course of analysis, role of communicator is the same for data visualization design. The

communicator is normally, worried about the correspondence side of the task. With their hard cap

on, they act as arbitrator and moderator, working at the customer client fashioner door, educating

every one of the individuals who are included on advancement, prerequisites, issues, and

arrangements. The communicator should be near all phases of the procedure, getting prerequisites,

acknowledging confinements, perceiving conceivable outcomes, and after last propelling,

publicizing, and displaying the last work. A capacity to lucid and disclose matters to various kinds

of individuals, specialized and non-specialized, and be fit for overseeing desires and connections

is imperative. If we think logically, every data visualization challenge is different. There are

different types of data visualization technique that someone can follow to present the analysis,

outcomes and methodology to the audience. Most of the time audience will not be aware of the

detail of the context or the technical lingos. So as a communicator they need to present those in a

way, so audience receives the message they are trying to provide. Otherwise that project will not

have the perceive value. Same way communicator needs to get the message from the audience and

convey that to his/her technical team so that they can work according to the expectations of stake

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holders. As we can see “Communicator” hat is also a integral part of data visualization that can

not be ignored.

Project Manager

Project manager manages or track the progress of on-going project cohesively understand the brief,

capabilities, finishes checks and attention to detail the data visualization to identify the set of

parameters. Project manager is a team lead for all the departments included as a part to work in

the project. Project manager is responsible to complete the project before deadline if any

department is fail to complete the work on time than manager is also liable for that part. Project

manager plays vital role in data visualization because he knows about the project and have skills

of database management system.

Project Manager Role in Data Visualization

Project manager shows the process and projects in a visual format that pulls people to add context

for their task, indicates graph, presentation, and dashboard. All project are complex, doesn’t matter

whether it’s large or small one. Project manager has to control the budget, team and time to

constraint and manage. Project manager has to used data visualization tool to understand

conceptual and idea development process. Projects are data rich environments and data

visualization can help with the status of the project. Data visualization give idea to the manager by

showing to take or improved decision making. “Project status reports monitor progress throughout

the life of a project. A status report will lay out a summary of what is expected from a project and

show planned versus actual progress. It’s a place to keep track of anything that needs immediate

attention and to document risks and issues. A report can keep track of important milestones and

keep an eye on the project’s budget.” (July, 2018).

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Analysis:

In the above analysis, we have got responses from scholarly data sources from web. After

collecting those data, we have analyzed how the Eight hats are affecting the data visualization

design. We can easily argue about some of the labels and descriptions of how the mindsets and

duties have been carved up and allocated but hopefully it is proved that the many varied

responsibilities that anyone delivering a data visualization solution needs to demonstrate,

regardless of whether this is an individual or collaborative design process. Our research found all

hats are equally important for data visualization design. As a result, we accept our null hypothesis.

Also, after years of research it’s been identified that scholars have decided “Eight Hats of Data

Visualization Design” which was originally influenced by the concept of Edward de Bono's six

thinking hats. These hats will help someone to identify where he/she is lacking or where he/she

needs to improve. The same concept might be applied for a team and they will be able to address

their strength and weaknesses more effectively. These hats state that in data visualization what we

need to be occupied when tackling complex problems and organize the different attributes required

to accomplish success in visualization.

Limitation & Recommendation:

All studies have limitations so do we have some limitation for our research paper. They are:

1.Limited access of data: Lack of available data is another limitation for our project. We can only

use secondary data that is a biggest limitation for the project.

2. Time constraints: Time is another factor for this project. If we can get more time we can do

more broad research. As a result, we will be able to find out more details about 8 hats.

3.Propose a direction for future studies and present alternatives:

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i. Try to use the primary data also from real life experience and try to merge with the theory.

ii. Use enough time.

iii. We also propose to figure out if there are other hats that can make this process efficient or

maybe we can merge any of these hats together.

Conclusion:

According to the research and findings we can say that 8 hats are important for data visualization

design. Lastly, it will help us to identify where we fit it in to the spectrum of duties and

responsibilities, helping us identify our strengths and your weaknesses accordingly. We may then

choose to address these weaknesses personally or plug the gaps with support from others. As per

the study and findings we can say that eight hats are important for data visualization design.

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Reference:

1. Article: The 8 hats of data visualisation design. (2014, August 30). Retrieved June 29, 2019,

from http://www.visualisingdata.com/2012/06/article-the-8-hats-of-data-visualisation-design/

2. Using Data Visualization to Find Insights in Data. (2019, April 02). Retrieved June 29, 2019,

from https://datajournalism.com/read/handbook/one/understanding-data/using-data-visualization-

to-find-insights-in-data

3. Schwartz, M., & Knapton, D. (n.d.). A Better Data Visualization Design Process. Retrieved June

29, 2019, from https://constructive.co/insight/data-visualization-design-process-best-practices/

4. Kirk, A. (2012). Data Visualization: A Successful Design Process. Packt Publishing.

5. Iliinsky, N., Steele, J. (2011). Designing Data Visualizations: Representing Informational

Relationships. O’Reily Media Inc.

6. Steinmetz, M. (2018, July). 5 Time-Saving Ways to Use Project Management Data

Visualization. Target Process https://www.targetprocess.com/blog/project-management-data-

visualization/