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How has data visualization evolved in the past decade?

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Data visualization is the art and science of presenting data in a visual form that is easy to understand, explore, and communicate. It is an essential skill for data scientists, who need to analyze, interpret, and share complex and large datasets. In the past decade, data visualization has evolved significantly, thanks to new technologies, tools, and trends. In this article, we will look at some of the major changes and innovations that have shaped the data visualization landscape in the 2010s.

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1 Interactive and dynamic charts

One of the most noticeable changes in data visualization is the shift from static and flat charts to interactive and dynamic ones. Interactive charts allow users to manipulate, filter, zoom, and drill down into the data, while dynamic charts update in real time as the data changes. These features enable more engaging and exploratory data analysis, as well as more responsive and adaptive data storytelling. Some examples of interactive and dynamic charts are dashboards, maps, timelines, and network graphs.

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Dr. Priyanka Singh Ph.D.

?? AI Author ?? Transforming Generative AI ?? Responsible AI - Lead MLOps @ Universal AI ?? Championing AI Ethics & Governance ?? Top Voice | Empowering Future AI Solutions | Packt Technical Reviewer
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In the last decade, data visualization has transformed tremendously, becoming more user-interactive and dynamic due to technologies like D3.js. The emergence of big data necessitated innovative tools like Tableau and Power BI, designed to interpret voluminous datasets. The rise of data storytelling amplified the communicative aspect of visualization, with tools like Flourish enhancing narrative delivery. Incorporating augmented and virtual reality brought immersive exploration. AI and machine learning interventions have automatized and improved precision in data visualization, making insights more lucid and accessible for informed decision-making processes.

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2 Web-based and cloud-based platforms

Another key change in data visualization is the emergence and popularity of web-based and cloud-based platforms. These platforms allow data scientists to create, publish, and share data visualizations online, without the need for installing software or hosting servers. They also enable collaboration, feedback, and access across different devices and locations. Some examples of web-based and cloud-based platforms are Tableau, Power BI, Google Data Studio, and Plotly.

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Sagar Khandelwal

Manager- Project, Sales, Business Development | Govt./Private Projects| Expert in Bid, Project Management, Presales, Post Sales | RFP Analysis | Solution Strategist
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In the past decade, data visualization has evolved significantly: Interactive Dashboards: Data visualization tools now offer dynamic, interactive dashboards for real-time insights. Big Data Handling: Visualizations can handle larger datasets, making it easier to extract meaningful information. Machine Learning Integration: AI and ML are integrated to create predictive and prescriptive visualizations. Storytelling Emphasis: Visualizations are used for storytelling, making data more accessible and actionable. Accessibility and Responsiveness: Visualizations are designed to be accessible across various devices and screen sizes, enhancing their usability.

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3 Artificial intelligence and machine learning

A third major change in data visualization is the integration of artificial intelligence and machine learning. These technologies help data scientists to automate, optimize, and enhance various aspects of data visualization, such as data cleaning, data transformation, data analysis, data modeling, and data presentation. They also enable new types of data visualization, such as generative art, natural language generation, and computer vision. Some examples of artificial intelligence and machine learning in data visualization are Datawrapper, Charticulator, and GANs.

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4 Augmented reality and virtual reality

A fourth significant change in data visualization is the adoption of augmented reality and virtual reality. These technologies allow data scientists to create immersive and interactive data visualizations that go beyond the screen and the 2D plane. They also enable new ways of experiencing and understanding data, such as spatial, temporal, and emotional dimensions. Some examples of augmented reality and virtual reality in data visualization are ARKit, A-Frame, and VRVis.

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5 Social and ethical issues

A fifth important change in data visualization is the awareness and discussion of social and ethical issues. These issues include data privacy, data security, data quality, data bias, data literacy, and data impact. They also involve the responsibilities and challenges of data scientists, data users, and data stakeholders. Some examples of social and ethical issues in data visualization are GDPR, Cambridge Analytica, and COVID-19.

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6 Future directions and opportunities

A sixth and final change in data visualization is the anticipation and exploration of future directions and opportunities. These directions and opportunities include new data sources, new data formats, new data methods, new data tools, and new data domains. They also involve the innovation and experimentation of data scientists, data designers, and data enthusiasts. Some examples of future directions and opportunities in data visualization are IoT, big data, blockchain, and quantum computing.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How has data visualization evolved in the past decade?

1

2

3

4

5

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1 Interactive and dynamic charts

2 Web-based and cloud-based platforms

3 Artificial intelligence and machine learning

4 Augmented reality and virtual reality

5 Social and ethical issues

6 Future directions and opportunities

7 Here’s what else to consider

Data Science

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How has data visualization evolved in the past decade?

1

2

3

4

5

6

7

1 Interactive and dynamic charts

2 Web-based and cloud-based platforms

3 Artificial intelligence and machine learning

4 Augmented reality and virtual reality

5 Social and ethical issues

6 Future directions and opportunities

7 Here’s what else to consider

Data Science

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