8 Key Data Visualization Tools in 2023

In this article, we’ll look at eight key data visualization tools in 2023, their benefits and suitability for different businesses, and the challenges associated with them.

What is a data visualization tool?

A data visualization tool is a software application that enables users to create visual representations of data, such as graphs, line charts, and dashboards. These tools help in transforming raw data into more easily understandable formats, assisting businesses and individuals in data-driven decision-making.

8 Key Data Visualization Tools for Business Professionals

Let's look at eight popular data visualization tools, their capabilities, benefits, and potential challenges that businesses may face while using them.

1. Looker

Looker is a business intelligence software and data visualization tool that helps organizations access, analyze, and visualize their data. It enables users to create customizable reports and dashboards, offering flexibility and scalability.

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Pros:

• Highly customizable and flexible
• Integration with a wide range of data sources
• User-friendly interface with drag-and-drop functionality
• Strong collaboration features for sharing and discussing insights

Challenges:

• Steep learning curve for non-technical users
• Pricing may be prohibitive for smaller businesses
• Limited pre-built visualizations compared to competitors
• Inability to easily drill down into any combination of dimensions
• Unable to see top drivers of change in key metrics

2. Tableau

Tableau is a widely recognized data analytics software that provides an array of tools to create interactive and shareable dashboards. Its user-friendly interface enables both technical and non-technical users to transform raw data into visually appealing charts and line graphs.

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Pros:

• Intuitive and user-friendly interface for creating visualizations
• Extensive library of visualization techniques and customizations
• Integration with numerous data sources and platforms
• Large and active community for support and resources

Challenges:

• Steep learning curve for non-technical users
• Tableau can be costly, particularly for smaller businesses
• Limited options for data preparation and transformation
• Dashboard management as volumes of dashboards swell
• Unable to see top drivers of change in key metrics

3. Microsoft Power BI

Microsoft Power BI allows users to collect, analyze, and visualize large data sets from a variety of sources. Unsurprisingly, it scores high in popularity among teams that already use Microsoft’s products.

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Pros:

• Seamless integration with the Microsoft ecosystem
• Wide range of data visualization options based on the use case
• Robust and versatile
• Easily scalable through flexible pricing options

Challenges:

• Access to advanced features costs more
• Basic user interface (UI)
• Limited customization options as compared to other business intelligence tools
• Steep learning curve for non-technical users

4. Qlik Sense

Qlik Sense is a self-service data visualization and analytics tool that enables users to create interactive dashboards and reports. It features an associative data model, allowing users to explore data connections and relationships more easily.

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Pros:

• Associative data model for faster insights
• Drag-and-drop interface for easy visualization creation
• Strong data security and governance features
• Scalable and suitable for large datasets

Challenges:

• Limited out-of-the-box integration with some data sources
• Advanced features may require scripting knowledge
• Pricing can be expensive for small businesses

5. Sisense

Sisense is a business intelligence and data visualization software that allows users to consolidate, analyze, and visualize data from various sources. It aims to simplify complex data analytics, making it more accessible to non-technical users.

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Pros:

• Intuitive drag-and-drop interface for easy dashboard creation
• Wide range of connectors for diverse data sources
• Robust data preparation and transformation tools
• Scalable and suitable for large enterprises

Challenges:

• Higher price point compared to some competitors
• Limited pre-built visualization options for pie charts and graphs
• Steeper learning curve for advanced features

6. Datawrapper

Datawrapper is a web-based data visualization tool that caters specifically to creating interactive charts and maps. It is designed for journalists, researchers, and businesses who need a simple, fast solution for visualizing data in real-time.

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Pros:

• User-friendly interface for quick visualization creation
• Focus on creating responsive, embeddable charts, histograms, infograms, and maps
• Customizable design options for branding purposes
• Free version available for basic use

Challenges:

• Limited functionality compared to full-featured BI tools
• Restricted data source integration and API options
• Advanced features require a subscription

7. Google Charts

Google Charts is an open-source data visualization solution that enables users to design interactive charts and graphs for web applications or websites. As part of Google's ecosystem, it is compatible with other Google services and offers a variety of types of charts (such as bar charts, area charts, scatter plots, etc.).

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Pros:

• No cost to use and open-source with a large user community
• Convenient integration with other Google services
• Diverse selection of chart types and customization features
• Adaptive design suitable for mobile devices

Challenges:

• Lacks the functionality of more comprehensive BI tools
• Absence of built-in data preparation or transformation options
• Necessitates a basic understanding of HTML, JavaScript, and CSS

8. Zoho Analytics

Zoho Analytics is a cloud-powered business intelligence and data visualization solution offering an array of features for big data processing and reporting. As part of Zoho's suite of applications, it can be combined with other products for a cohesive experience.

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Pros:

• User-friendly interface featuring drag-and-drop functionality
• Compatibility with Zoho suite and third-party applications
• Comprehensive range of visualization choices and customization options
• Flexible pricing structure accommodating businesses of all sizes

Challenges:

• Fewer data connectors in comparison to rivals
• More complex features might have a steeper learning curve
• The interface may be less intuitive for some users compared to other solutions

Challenges with Data Visualization Tools

Data visualization tools bring these three challenges that make the business intelligence workflow complex:

1. Time-intensive

Creating visually appealing and informative dashboards can be a time-consuming process. As a result, analysts might spend a significant portion of their time building and managing dashboards instead of focusing on higher-impact projects.

A survey conducted by Tableau in 2018 revealed that crafting a basic dashboard took around 3.3 hours on average, while constructing an advanced dashboard with intricate visualizations and multiple data sources required up to 29.3 hours.

For example, let’s say a data analyst at a marketing agency is tasked with creating a dashboard to display the effectiveness of various marketing campaigns.

Due to the complexity of the visualizations and the need to combine data from multiple sources, the analyst spends days building the dashboard, resulting in less time to analyze the data and generate actionable insights for the agency.

2. Dashboard Management

After setting up multiple dashboards, managing and maintaining them can become increasingly difficult, especially when the data changes frequently. Ensuring that hundreds of dashboards remain up-to-date and relevant can be a daunting task.

Consider a large retail company’s business team that has created hundreds of dashboards to track sales, inventory, and customer behavior across different regions and product categories for internal and external stakeholders.

Each time new data is added or changes occur in the business, they must update these dashboards, causing a considerable strain on resources and time.

3. Dashboard Paralysis

When businesses create an excessive number of dashboards, it can lead to dashboard paralysis. This occurs when users are overwhelmed by the sheer volume of available dashboards, causing confusion and making it difficult to find relevant information.

Additionally, redundancy may arise as analysts create new dashboards without realizing that similar ones have already been made in the past.

Imagine a Fintech company that has created multiple dashboards for financial data analytics in various departments and functions.

As the number of dashboards grows, employees struggle to find the right dashboard for their specific needs, leading to decreased efficiency and potential duplication of efforts. Some employees even resort to creating their own dashboards, further exacerbating the issue.

DataGPT Balances the Need for Simplicity and Speed for the Business User with Accuracy and Depth of Analysis

While data visualization tools offer valuable insights and visual representations of complex data, they also present challenges, such as time-intensive dashboard creation, dashboard management, and dashboard paralysis.

DataGPT addresses these issues with:

• A single, easy-to-use dashboard that eliminates dashboard paralysis and reduces the time and resources required for dashboard creation and management.
• Conversational AI Chatbot experience enables users to ask complex questions like “Why did conversions drop this week?” and get instant answers in plain English.
• The lightning cache database model enables instant insights across all company data, while the sophisticated algorithm surfaces key drivers of change in metrics and provides personalized insights for users.
• Exhaustive drill-down capabilities that allow users to quickly investigate the details of what changed and why, cutting the time and complexity of daily data analysis by 90%.
• Time series visualizations that help users identify trends in their data, while its trustability features ensure transparent insights and eliminates human bias.

As a result, businesses using DataGPT can improve their time to insights from days to seconds, reduce analyst backlog by 50%, and reinvent their data culture with an impressive 85% business user adoption rate, compared to the average of 18% among traditional BI tools.

Book a demo to learn how DataGPT's conversational analysis capabilities can transform your data analytics workflow for the modern era.