What is dataviz?

The practice of placing information in a visual context, such as a map or graph, to make it easier for the human brain to absorb and extract ideas from it.
The main goal of Dataviz is to make it easier to identify patterns, trends and outliers in huge datasets. The phrase is often used interchangeably with other terms such as information graphics, information visualization, and statistical graphics.

Dataviz, what exactly is it?

Dataviz is one of the processes of data science which states that data should be visualized once it has been collected, processed and modeled so that conclusions can be drawn. Data visualization is also a component of the broader discipline of data presentation architecture (DPA), which strives to discover, extract, modify, format and transmit data in the most appropriate way. as efficient as possible.

Data visualization is essential in virtually every profession. Teachers can use it to display student test scores, IT professionals can use it to improve artificial intelligence (AI), and executives can use it to share information with stakeholders. It is also very important in large data projects. Businesses needed a way to quickly and easily gain insight into their data when they amassed large amounts of it during the early years of the big data trend. The use of visualization tools came naturally.

For similar reasons, visualization is essential in advanced analytics. When a data scientist develops complex predictive analytics or machine learning algorithms, it is essential to display the results in order to monitor the results and confirm that the models are working as expected. This is because representations of complex algorithms are often easier to understand than numerical outputs.

Why is Dataviz important?

Data visualization is a quick and effective approach to presenting information universally through the use of visual data.
This method can also help businesses determine which aspects influence customer behavior, identify areas that need to be addressed or given more attention, make data more memorable for stakeholders, understand when and where to deploy specific products and forecast sales volumes.

Here are some additional benefits of Dataviz:

• the ability to quickly digest information, develop ideas and make decisions more quickly; better knowledge of the next steps to take to improve the business; and a better ability to keep the audience interested with information they can understand.
• simple distribution of information that increases the potential of all participants to contribute ideas;
• data scientists are no longer necessary since the data is more accessible and intelligible; and there is a greater ability to act quickly on results and, therefore, achieve success with greater speed and fewer errors.

Dataviz and big data

The growing popularity of big data and data analytics initiatives has made the need for visualization stronger than ever. Machine learning is rapidly being used by businesses to collect vast volumes of data that can be difficult and time-consuming to sort, interpret and explain. Visualization provides a mechanism to accelerate this process and communicate information to business owners and stakeholders in a way they can grasp.

Big data visualization often goes beyond traditional visualization approaches such as pie charts, histograms, and business graphs. It uses more complex visual tools, such as heat maps and fever charts. Big data visualization requires the use of powerful computing systems to collect raw data, process it, and convert it into graphical representations that humans can use to quickly draw conclusions.

While big data visualization can be advantageous, it can also present a number of disadvantages for businesses. As an example, here are a few:

• You need to hire a visualization specialist to get the most out of big data visualization technologies.
  This professional should be able to determine the optimal data sets and visualization methods to ensure businesses are maximizing the use of their data.
• Because big data visualization requires powerful computing hardware, efficient storage systems, and possibly a move to the cloud, IT and management are often involved in big data visualization initiatives.
• Big data visualization cannot provide information as accurate as the data presented.
  Therefore, it is essential to have people and systems in place to oversee and control the quality of the company’s data, metadata and data sources.

Some examples of Dataviz

Initially, the most common visualization strategy was to use a Microsoft Excel spreadsheet to translate the information into a table, bar graph, or pie chart. While basic visualization approaches are still widely used, more complex techniques, such as the following, are also available:

• infographics
• bubble clouds
• bullet graphs
• Heat maps
• fever charts
• time series charts

Other popular techniques include the following.

Line charts
This is one of the most basic and often used procedures.
Line charts show how variables change over time.

Area graphs.
This is a variation of a line chart that displays many values ​​in a time series – or a succession of data collected in consecutive periods and equally spaced in time.

Graphs with dispersion.
This method makes it possible to demonstrate the link between two variables.
A scatter plot has an x-axis and a y-axis with points representing data points.

Tree maps.
This approach allows hierarchical data to be displayed in a nested manner.
The size of each category’s rectangle is proportional to its percentage of the total.
Tree maps work well when there are multiple categories and the goal is to compare different aspects of a whole.

Population pyramids
This technique uses a stacked bar chart to show the complex social narrative of a population.
It is particularly useful when describing the distribution of a population.

Common Data Visualization Use Cases

Examples of common use cases for data visualization include:

Marketing and sales.
According to Magna research, 50% of all global advertising dollars will be spent online by 2020.
Therefore, marketing teams need to pay close attention to their web traffic sources and the money generated by their web domains.
Data visualization makes it easy to see traffic trends resulting from marketing efforts over time.

Policy.
A geographic map showing which party each state or district voted for is a typical use of data visualization in politics.

Health.
Choropleth maps are commonly used by healthcare professionals to illustrate vital health data.
A choropleth map depicts geographic areas or regions that have been color-coded based on a numerical value.
Professionals can use choropleth maps to see how a variable, such as heart disease mortality rate, changes in different territories.

The scientists.
Scientific visualization, abbreviated as SciVis, allows scientists and researchers to understand their experimental data better than ever before.

Finance.
When determining whether to buy or sell an asset, financial professionals must monitor the performance of their investment decisions.
Candlestick charts are used as trading tools, helping financial professionals analyze price movements over time and displaying essential information such as securities, derivatives, currencies, stocks, bonds and raw materials.
Data analysts and financial experts can spot trends by evaluating how prices have changed over time.

Logistic.
Visualization tools can be used by shipping companies to discover optimal shipping routes around the world.

Researchers and data scientists
Data scientists often create visualizations for their own use or to disseminate information to a small group of people.
Visual representations are created using visualization packages from programming languages ​​and tools of their choice.
Data scientists and researchers typically use open source programming languages, such as Python, or proprietary data analysis tools.
These scientists and researchers use data visualization to better analyze data sets and find patterns and trends that would otherwise go unreported.

The science of Dataviz

Understanding how humans collect and interpret information is the foundation of data visualization science.
Amos Tversky and Daniel Kahn worked on research that highlighted two distinct approaches to acquiring and analyzing information.

System 1 concerns rapid, automatic, unconscious mental processing.
This strategy is commonly used in daily life and achieves the following goals:

• Read the language of a sign
• solve elementary math problems, like 1+1,
• identify the source of a sound
• ride a bike
• determine the difference between colors.

System 2 focuses on cognitive processing that is slow, rational, calculating, and sparse.
This approach is used in any of the following circumstances:

• remember a phone number
• perform complicated math problems, like 132 x 154
• distinguish the meaning of several signs placed side by side
• understand complex social cues.

https://www.youtube.com/watch?v=DlG7ZzRaJLc

Dataviz tools and providers

Data visualization tools can be used in a variety of ways.
Today, the most widespread application is as a business intelligence (BI) reporting tool.
Users can configure visualization tools to create automatic dashboards that graphically track and evaluate business performance based on key performance indicators (KPIs). The images created can also have interactive capabilities, allowing users to modify them or dig deeper into the data for research and analysis. Indicators that alert users when data is updated or when predetermined conditions are met can also be included.

To monitor their own initiatives, several departments within the company use data visualization software. A marketing team, for example, can use the program to track the performance of an email campaign, tracking data such as open rate, click-through rate, and conversion rate.

As data visualization companies expand the functionality of their products, they are increasingly used as interfaces for more sophisticated big data environments. In this context, data visualization software helps engineers and scientists track data sources and perform basic exploratory research on datasets before or after further research.

Microsoft, IBM, SAP and SAS are some of the best-known names in the big data tools market.
Other manufacturers offer specialized big data visualization tools, with the best-known names in this sector being Tableau, Qlik and Tibco. While Microsoft Excel remains a popular application for data visualization, others have emerged that offer more sophisticated capabilities:

• IBM Cognos Analytics
• Qlik Sense and QlikView
• Microsoft Power BI
• Oracle Visual Analyzer
• SAP Lumira
• SAS Visual Analytics
• Tibco Spotfire
• Zoho Analytics
• D3.js
• Jupyter
• MicroStrategy
• Google Charts