What are the best data visualization tools for decision making?

Data Visualisation is an art which helps to understand the data in a graphical manner. One should always focus on various visualisation techniques or methodologies so that you will get a better understanding of any data. You can use a power BI and tableau for data visualisation and any kind of graphs such as histogram, barplot, pie-diagram etc.

The choice of data visualization tools depends on multiple factors such as the specific needs of the user, the complexity of the data, and the ultimate goals of the visualization. There are several popular and effective tools for supporting decision-making, including Excel, Tableau, Power BI, R Studio, and Python Libraries (Matplotlib and Seaborn). Furthermore, when deciding on a data visualization tool, it's crucial to consider aspects such as user-friendliness, compatibility with existing systems, collaboration capabilities, and the unique demands of your data analysis tasks. Additionally, one should take into account the users' skill levels and the desired degree of interactivity and customization that can be leveraged on.

When selecting a data visualization tool such as Tableau, Power B, QlikView and Qlik Sense, Looker, or Plotly for decision-making, consider factors such as ease of use, compatibility with your data sources, interactivity, scalability, and cost. The best tool for your specific needs will depend on the complexity of your data, your technical skills, and the requirements of your decision-making process. It's often beneficial to experiment with a few options to find the one that suits you best.

Data visualization tools can be categorized into three main types: 1. Spreadsheet Tools (e.g., Excel, Google Sheets, Numbers): - Accessibility: Easily accessible to users with basic computer skills. - Simplicity: User-friendly with simple charting options. 2. Online Tools (e.g., Tableau, Power BI, Datawrapper): - Features - Ease of Use - Subscription Costs 3. Programming Tools (e.g., Python, R, JavaScript): - Customization: Provide the highest level of customization and control over data visualizations. - Versatility: Suitable for creating highly specialized and complex visualizations. - Technical Skills: Require programming skills and a steeper learning curve

Effective decision-making in analytics involves a structured approach. First, clearly define the problem or decision you're facing. Then, gather relevant data, ensuring its quality and completeness. Utilize statistical and analytical tools, such as Python for data analysis, to extract insights. Visualize your findings to communicate complex patterns effectively. Consider the implications of different decisions, weighing the potential outcomes. Leverage business intelligence tools to create dashboards for real-time monitoring. Lastly, involve stakeholders in the decision-making process, ensuring alignment with organizational goals. Regularly assess and adapt your decision-making processes based on feedback and outcomes.

Choosing the best data visualization tool involves a number of factors to consider, thus, data complexity (velocity, variety, veracity and volume), the specific needs of the users, objectives to be met with the visualization, apart from the data ethics and integrity considerations. By carefully considering these factors, you can choose a data visualization tool that best fits your overall objective and empowers you to derive meaningful insights from your data. Some of the best data visualization tools for decision-making encompasses; Tableau, Excel (with Power Query & Power Pivot), Microsoft Power BI, Matplotlib and Seaborn (Python Libraries). One can leverage these data visualization tools based on the earlier highlighted factors.

Choosing the best data visualization tool ???? can be a critical decision. Here are some factors to consider: 1. Data Type & Size 2. Data Complexity 3. Real-Time Updates 4. Budget & Cost 5. Deadlines 6. Interactivity & Analysis 7. Presentation & Storytelling 8. Audience 9. Skills & Learning Curve 10. Community & Support Ultimately, the best data visualization tool for you depends on the specific needs of your project, the resources available, and your familiarity with the tool. Carefully evaluate these factors to make an informed decision that aligns with your goals ??.

Other considerations for choosing tools: - The tool(s) you know the best - The tool(s) you can get help with when you need to troubleshoot issues - The tool(s) that can deliver results in the necessary time you have before presenting it to stakeholders

Effective use of data visualization tools hinges on thoughtful adherence to key principles: 1. Comprehend Your Data ?? Grasp the source, quality, and structure of your data to produce meaningful visuals. 2. Clarify Objectives ?? Define your central questions, hypotheses, or insights intended for visualization. 3. Know Your Audience ?? Customize visuals to suit your audience's expertise, interests, and attention span. 4. Opt for Appropriate Visuals ?? Choose chart types that align with your message, like line charts for trends, pie charts for proportions, or scatter plots for correlations. 5. Streamline Complexity ?? Avert visual clutter and intricacy, preserving focus on essential data elements.

1. Exploration of Insights: Data visualization tools allow you to visually dissect complex datasets, making it easier to identify patterns, outliers, & correlations that might remain hidden in raw data. 2. Hypothesis Testing: Visualization enables you to test and validate hypotheses more effectively by providing a dynamic, visual representation of data, helping you draw meaningful conclusions. 3. Pattern Recognition: Visualizations facilitate the recognition of trends & anomalies, enabling you to make informed decisions and identify areas that require further investigation. 4. Data-Driven Decision-Making: Visualization tools empower you to base decisions on concrete data, reducing reliance on intuition and enhancing the accuracy of choices.

One time at work, I was eager to enhance my data visualization skills. Here's how I did it: I began by exploring online courses, such as "Data Visualization with Tableau," "Data Visualization with Python," and "Data Visualization with R." These courses offered hands-on experience and insights into various tools and techniques. Additionally, I delved into the world of data visualization through blogs and podcasts. Resources like FlowingData, Data Stories, and Storytelling with Data provided valuable updates on trends and best practices. Regularly tuning into these platforms kept me informed and inspired.

In-person or virtual meet ups, conferences, and social media can also expose you to new tools and features of your already known data visualization tools. Meets ups can vary in quality, but they can connect you with other data visualization practitioners. This will be similar for conferences. For social media, you can also listen for and find people who regularly post their own or others' data visualizations for inspiration or general learning.

Each DI tool right now in the analytical space has its pros and cons. However, the fundamental remains the same. My suggestion to anyone who intends to learn - any tool is fine as long as you learn A) when to use which visualization? B) how to interpret that visual? C) what kind of data preparation would be required to generate that visual?