TELLING STORIES USING DATA COLLECTED: (Data Visualization)

By: Atino Elizabeth

This writing was inspired by me partaking Virtual Intern Program on Data Analysts Consulting @KPMG.insidershapers which gave me a deep insight on;
Data Quality Assessment
Data insights
Data insights and presentations
Being able to use Dashboards to visually represent data trends was an amazing experience I got from learning the course and I decided to write something about Visualization of Data. It’s just amazing, enjoy the Read. Let me know what you think or can add.
 
One of the best practices of data interpretation is the visualization of the dataset. Data interpretation and Analysis are fast becoming more valuable with the prominence of digital communication, which is responsible for a large amount of data being charred out daily.
 
BRIEFLY on Data Interpretation:
 
Data Interpretation is the “process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion.” Therefore, before we can ask about interpreting data, they need to be analyzed first. Data analysis is simply the process of ordering, categorizing, manipulating and summarizing data to obtain answers to research questions. Its noted as the first step taken towards data interpretation.
 
In business terms @Minerra defines it as “the implementation of different processes in which data is analyzed and revised with the purpose of gaining insights and recognizing emerging patterns and behaviors.” There are two main classification of Data analysis and these are; the quantitative and the qualitative analysis.
 
Qualitative Data Interpretation;

This method uses texts rather than numbers or patterns to describe data. It’s mostly used for observations, interviews, documents, surveys and many others. The best feature about a qualitative data interpretation is the findings are grouped into topics and categories which makes it easier to notice trends and collection of data. This analysis is more readable for a lay man.

With Quantitative Data Interpretation, you analyze the numbers in the data to gain insights and that is achieved with statistical modeling. The most common technique of quantitative analysis is running texts on two or more significant variables, which are later processed together or separately and in the end are compared to one another to sum up a report.
However, we saying that VISUALISATION makes it easy for any layman to understand the data and also encourages people to view the data as it provides a visually appealing summary of data.
Now let’s dive into data visualization!
 
What is Data Visualization(Data Viz)?

To me Data visualization is more of a graphical representation of information and data. By using visual elements like charts, graphs and maps.

Notably other definitions of Data Visualization herein include;
Klipfolio’s defines Data viz as a communication of data in a visual manner or turning raw data into insights that can be easily interpreted by your readers.
Wikipedia’s definition of Data Viz refers to a technique used to communicate data or information by encoding it as visual objects (point, lines, bars) contained in graphics.
Techopedia’s defines Data Viz as the process of displaying data or information in graphical charts, figures and bars.
In the world of Big Data, data visualization tools and techniques are essential to analyze massive amounts of information and makes data driven decisions. As the age of Big Data 'kicks" into high-gear, visualization is an increasingly key tool to make sense of the trillions of rows of data generated every day.
 
What are the advantages and benefits of good Data visualization?
• It’s very common and usual that our eyes are drawn to colours and patterns. We can quickly identify red from blue, square from circle. Our culture is visual including everything from art and advertisement to TV and Movies. And if data is easily visible, any layman can understand and appreciate the data.
• Data Visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. Thus if we can see something we can internalize it quickly. Its story telling with a purpose.
• Data Visualization helps to tell stories by curating data into a form easier to understand.
• A good visualization tells a story, removing the noise from data and highlighting the useful information.
• Effective data visualization is a delicate balancing act between forms and function. The data and the visuals need to work together and there’s an art to combating great analysis with great story telling.
 
Why Data visualization is important for any career?
• Most professional industry benefits from making data more understandable. Every important field benefits from understanding data and visualization is one of the most useful professional skills to develop.

If you can convey your points visually, whether in a dashboard or slide deck, the better you can leverage that information. This gives high possibility of engagement from the audience and can be impactful.
• It is increasingly valuable for professors to be able to use data to make decisions and use visuals to tell stories of when data informs the who, what, when, where and how.
 
To start thinking visually; Consider the nature and purpose of your visualization.
1. Is the information conceptual or data driven?
2. Am I declaring something or exploring something?
CONCEPTUAL, FOCUS: ideas, tools: simplify and teach… Show how your organization is structured and what trends about it.
 
If you know the answers to these questions, you can plan what resources and tools you will need and begin to discern what type of visualization will help you achieve your goals most effectively. (Here you are DATA-DRIVEN, you focus more on STATISTICS and the GOAL is to inform, enlighten. For example, "here are our resources for the past two years). There’s a whole selection of visualization methods to present data in effective and interesting ways and the common general types of data visualization are herein listed below;
• Charts; for example, using a pie chart to represent the percentage of occurrence of a variable using sectors. The size of each sector is dependent on the frequency or percentage of the corresponding variables. There are many pie charts to use for example, simple pie chart which is the most basic type of chart used, the Doughnut Pie chart and the 3D Pie chart.

And the advantage with using a pie chart is that, its visually appealing and best for comparing small data samples. The disadvantage with it is that it can only compare small sample sizes and unhelpful with observing trends over time.
 
• Tables; These are used to present statistical data by replacing them in rows and columns. They are one of the most common statistical Visualization technique and has two main types i.e. Simple tables which summarizes information on a single characteristic. An example of a simple table showing number of employed people in a community concerning age group. This is hypothetical;

Age-Group /No Of Employed
20-25/15
25-35/30
36-50/35
51-64/25

There is also a complex Table; as its name suggests, it summarizes complex information and presents them in two or more interesting categories.
And the advantage of using a Table is that it contains large data and is helpful in comparing 2 or more similar things. The only disadvantage is that they don’t give detailed information and they maybe time consuming.
 
Other common data visualization techniques are;
• Graphs
• Infrographs
• Dashboards
• Maps and many others.
 
It’s worthy to note that the right visualization must be paired with the right information. Simple graphs are only the tip of the iceberg.

Thank you for being here with me!