Data visualization plays a vital role in transforming complex data into easily understandable and actionable insights. We have done a few articles where I highlighted the importance of telling a story to management. With a plethora of data visualization tools available in the market, organizations have numerous options to choose from based on their requirements. Knowing how each tool compares with others would assist management with buying decisions. In this article, we will explore and compare twelve popular data visualization tools, namely Tableau, Power BI, Microsoft Excel, MicroStrategy, QlikQ, Looker, Sisense, Dunbas Bi, Adaptive Insights, Analytics, Plotly, and Python. We will explore their histories, advantages, disadvantages, and market shares.
- Microsoft Excel
Microsoft Excel has been a staple spreadsheet tool since its inception in 1987. It has evolved to include data visualization capabilities. This was with the integration of Power Pivot, Power View, and Power Query. Today, Copilot in Excel helps us analyse and explore our data so we can make the best decisions. Copilot helps us identify trends, propose what-if scenarios, suggest ideas for improving your business, and will even build everything into an easy-to-read dashboard.
The advantage of this tool is widely available and familiar to many users. It offers basic charting and visualization options, suitable for simple data analysis tasks. The disadvantages of Excel is its limitations in handling large datasets and its lack of advanced visualization functionalities compared to dedicated tools. Despite the above, Excel remains a popular choice for basic data visualization, especially among non-technical users.
- Tableau
Tableau was founded in 2003 and quickly gained popularity for its intuitive drag-and-drop interface and powerful analytics capabilities. Their visual analytics platform promises to transform the way people use data to solve problems. The latest tool tools bring analytics to life with artificial intelligence (AI) and personalized insights – Say hello to Tableau GPT and Tableau Pulse, the next generation of Tableau. Tableau has established itself as a leader in the data visualization market, with a substantial market share.
The advantages of Tableau are that it offers a wide range of visualizations, interactive dashboards, and seamless data integration. It provides robust features for data exploration and storytelling. However, the disadvantages include being expensive for small businesses, and some advanced functionalities require technical expertise.
- Power BI
Power BI is a business analytics tool developed by Microsoft and launched in 2013. It is known for its tight integration with other Microsoft products. The tool helps us do more with less using an end-to-end BI platform to create a single source of truth, uncover more powerful insights, and translate them into impact. Recently, Microsoft has incorporated their AI product, Copilot, into Power BI. This helps us uncover the full potential of our data using next-generation AI tools. We can describe the insights we need or ask a question about our data and Copilot analyses and pulls the right data into a report—easily turning data into actionable insights.
The advantages include excellent integration capabilities, a user-friendly interface, and a broad range of data connectors. It also provides strong collaboration features. The disadvantages are that advanced features may require a learning curve, and the free version has limited functionality. Besides this drawback, Power BI has gained significant market share, benefiting from its association with the Microsoft ecosystem. Ecosystem integration contributed to its popularity despite being launched a decade after Tableau.
- MicroStrategy:
MicroStrategy was founded in 1989 and has grown into a comprehensive business intelligence platform, including data visualization. MicroStrategy ONE is their latest and most complete and expansive analytics platform yet. Not only is it fully modern, open, and cloud-powered—it’s the single platform for all analytics use cases. The tool promises to unleash the power of data to create a competitive advantage and trust in insights at scale. They claim to be the only platform in the world that can do it all with maximum performance.
Their mantra is “Based On Fact, Not Instinct” thus they don’t believe in guessing games. They believe in (1) data that replaces guessing with knowing; (2) challenging the status quo with data to back it up; (3) organizations using data to fuel innovation and come up with the next big thing. No noise, no distractions. Focus and make every decision count. MicroStrategy has a notable presence in the enterprise market but faces stiff competition from other tools.
The advantage of using MicroStrategy is the ability to provide robust enterprise-grade analytics capabilities, scalability, and security features. However good it may seem; the tool can also be complex to set up and requires technical expertise. The learning curve may be steep for non-technical users.
- QlikQ
QlikQ, developed by Qlik, was introduced in 1993. It gained recognition for its associative data indexing technology. Qlik Cloud, their latest offering, promises organisations the ability to seize every business moment with our AI-driven data integration and analytics cloud platform. They have introduced a suite of new OpenAI connectors to expand organisational analytics possibilities, get new natural language insights, and add third-party data to the models in real-time.
The advantage is that QlikQ offers powerful data exploration capabilities, associative search, and data storytelling features. It provides a seamless user experience. On the contrary, some users find QlikQ’s interface less intuitive compared to other tools, and the pricing can be a deterrent for small businesses. Despite this, QlikQ still holds a significant market share, particularly in the business intelligence and data analytics space.
- Looker
Looker was founded in 2011 and acquired by Google in 2020. It is known for its cloud-based data platform and embedded analytics capabilities. Looker Studio is built on top of Google Drive, so you can share your reports and data sources the same way you share docs, spreadsheets, and slides. We can collaborate by sharing with edit permission or just share your insights in “view only” mode. Either way, other people never have direct access to your data. Because of this, Looker has been gaining traction, especially in the cloud-based analytics market.
The advantage of choosing Looker is its focus on collaboration, sharing, and embedding data visualizations. It offers a powerful API for customization and integration. The disadvantage is that the tool may have a steeper learning curve compared to other tools, and some users may find the interface less intuitive. I believe organisations in the Google ecosystem might prefer Looker the way Power Bi is being appreciated.
- Sisense
Sisense was established in 2004 and is known for its easy-to-use and scalable business intelligence platform. It was founded in Tel Aviv with the idea that data analytics can be made fluent, easy, and fast through technical innovation. The tool is scalable, secure, and seamless. Furthermore, it promised to (1) Infuse AI-driven analytics into your products and business applications; (2) Live/direct connection with Excel or Google Sheets directly to your data model and collaborate on Teams or Slack; (3) Leverage code-free to code-first tools for deeper analysis across all skill levels; (4) Seamlessly integrate into your existing tech stack and connect to all data; (4) Augment every analysis with AI and machine learning for forward-looking intelligence without the need for tech expertise; and (5) Build unique, customizable and actionable experiences that automate multiple steps in a workflow.
For this reason, Sisense has a growing presence in the business intelligence and data analytics market. Despite its growing market share, the advantage is that the tool provides a user-friendly interface, fast performance, and strong data preparation capabilities. The downside is that some users find the visualization customization options limited, and the cost can be a concern for smaller organizations.
- Dunbas Bi
Dunbas Bi is a business intelligence tool developed by Dunbas, a leading software company, with a focus on reporting and analytics. It is now part of Insightsoftware’s Logi Symphony, – a leading provider of reporting, analytics, and enterprise performance management software. The tool is designed for Product Managers who are struggling to bring their reporting and analytics vision to life. Dunbas Bi holds a notable market share, particularly in the reporting and analytics domain.
The advantage of choosing Dunbas Bi is that it offers a comprehensive suite of reporting and analytics features, including data visualization and self-service capabilities. It offers benefits for anyone who is: (1) stuck delivering poor quality data visualization content; (2) having inaccessible/inaccurate data with little insights; or (3) users demand consistent user experience. The disadvantages also include the learning curve that may be steep for non-technical users, and the interface can feel overwhelming for beginners.
- Adaptive Insights
Adaptive Insights, now a part of Workday, was founded in 2003 and offers a cloud-based corporate performance management platform. Adaptive Insights Business Planning Cloud has become the single planning solution moving forward for all Workday customers. This strategy ensures that Workday is offering customers the best solution to accelerate their finance, workforce, and business transformations.
The disadvantage is that you must be part of the Workday ecosystem to benefit from this tool. Another disadvantage is it is primarily focused on financial data, and advanced customization options may be limited. Despite these limitations, Adaptive Insights specializes in financial planning and forecasting, with integrated data visualization capabilities. It might be the reason why Adaptive Insights has gained a significant presence in the financial planning and analytics market.
- Analytics
Analytics is a cloud-based analytics platform that provides self-service data exploration and visualization. Analytics has been steadily growing in the analytics market, targeting both small and large enterprises. The advantage is that it offers a user-friendly interface, natural language querying, and AI-driven insights. It emphasizes collaboration and sharing. The disadvantage is that advanced analytics features may be limited, and some users may find the pricing higher compared to similar tools.
- Plotly
Plotly is an open-source data visualization library that originated in 2012 and has since grown in popularity. As an open-source library, Plotly has a strong community following and is widely used in the data science and programming community. Plotly introduced web-based data visualization to Python. Today, the company offers Dash Enterprise, which provides the best software tools and platform to enable every enterprise in the world to build and scale data applications quickly and easily.
The advantage of choosing Plotly is that it provides a versatile and interactive library for creating visualizations in various programming languages. It offers customization and embedding options. The disadvantage is that it requires programming skills to leverage its full potential, and it may not be as user-friendly as dedicated tools.
- Python
Python is a popular general-purpose programming language that gained traction in the data science community due to its rich ecosystem of data visualisation libraries. Python lets you work quickly and integrate systems more effectively. For this reason, Python has a significant market share in the data science and analytics community, owing to its versatility and extensive library support.
The advantage of using Python is that it provides a wide range of libraries such as Matplotlib, Seaborn, and Plotly, allowing for extensive customization and flexibility in data visualization. The disadvantage is that Python requires programming skills and may have a steeper learning curve for beginners compared to GUI-based tools.
Conclusion
In the ever-expanding landscape of data visualization tools, each option brings its unique advantages and disadvantages. Data visualization will continue to play a vital role in transforming complex data into easily understandable and actionable insights. Tableau and Power BI dominate the market share, catering to a wide range of users, from beginners to advanced analysts. Excel remains a go-to choice for basic visualizations, while Python and Plotly offer flexibility and customisation for data scientists. MicroStrategy, QlikQ, Looker, Sisense, Dunbas Bi, Adaptive Insights, and Analytics cater to specific business needs. Ultimately, the choice of a data visualization tool depends on factors such as budget, user expertise, scalability requirements, and the complexity of the data analysis tasks at hand.
