What are the best data analysis tools for creating impactful narratives with your data?

Tableau and Microsoft Power BI are among the best data analysis tools for creating impactful narratives with your data. They offer intuitive interfaces and powerful visualization capabilities, allowing users to tell compelling stories through interactive dashboards and reports.

While Excel remains a valuable tool for quick, ad hoc analyses, Tableau and PowerBI are generally preferred for more complex, scalable analytics projects since they offer several advantages: 1) Handling Large Datasets: Tableau and PowerBI are designed to perform better with extensive data, making them more suitable for complex analyses. 2) Advanced Features: These platforms provide a vast array of tools for data sourcing, processing, and in-depth analysis, surpassing what Excel can offer. 3) Sharing and Security: They facilitate easy dashboard sharing and robust security measures, supporting effective data governance.

Excel remains the most user-friendly tool for data analysis, capable of handling basic analytics and visualizations. However, it can struggle with large datasets and lacks the advanced analytics capabilities of specialized software.

I agree excel remains the go to tool for initial data analysis. But, now industry promotes more robust tools and technologies to perform data analysis tasks on big data (more than 10L rows) like PySpark in Azure Synapse and SSMS (SQL). Using these two technologies one can perform data analysis on millions of records with low response time. Then surely comes tools like Power BI and Tableau for visualisation and reporting perspective.

1/ ?? Wide Availability: Excel is accessible to most users, making it a common starting point. 2/ ?? Data Sorting & Filtering: Easily organize and refine data to focus on key insights. 3/ ?? Pivot Tables: Summarize large datasets and uncover patterns crucial for narratives. 4/ ?? Conditional Formatting: Highlight important values to emphasize critical points. 5/ ?? Data Visualization: Create charts and graphs to visually represent data. 6/ ?? Built-in Functions: Perform complex calculations to support your data story. 7/ ?? Versatility: Has so many features like power query, power pivot, array functions, python!

1/ ?? Tableau: Offers powerful drag-and-drop functionality for creating detailed visualizations. 2/ ?? Power BI: Integrates well with other Microsoft products, providing robust visualization tools. 3/ ?? Google Data Studio: Free tool with customizable templates for diverse visualizations. 4/ ?? QlikView: Enables interactive data exploration and storytelling. 5/ ??? Plotly: Provides advanced graphing libraries for creating sophisticated charts and maps. 6/ ?? D3.js: Highly customizable JavaScript library for complex data visualizations. 7/ ?? Infogram: Simplifies the creation of infographics and data visuals with templates. 8/ ?? Looker: Offers real-time data exploration and visualization capabilities for impactful narratives.

1/ ?? R: Offers extensive libraries for statistical analysis and data visualization. 2/ ?? SAS: Provides comprehensive tools for advanced analytics and predictive modeling. 3/ ?? SPSS: User-friendly interface for performing complex statistical tests and analyses. 4/ ?? Stata: Known for its powerful data management and statistical capabilities. 5/ ?? MATLAB: Excellent for numerical computing and algorithm development. 6/ ?? Minitab: Simplifies statistical analysis with a focus on quality improvement. 7/ ?? JMP: Interactive and visual approach to statistical discovery. 8/ ?? Python (with libraries like Pandas, NumPy, SciPy): Flexible and powerful for statistical analysis and data manipulation.

I agree the tools like Power BI and Tableau do provide the functionality to create dashboards using drag and drop. But it doesn’t end here, the visualisation to use entirely depends on the data points and what story needs to be narrated. For example if one wants to show - the performance of two Categories over a period of months in a year. Which visualisation would you with? Many will choose the clustered bar chart but the correct visual to go with will be a Line Chart. The clarity of visuals to be used should be clear with all the transformational steps.

In my experience, businesses have voracious appetites for dashboarding. Keeping track of where you’ve been, where you stand right now, and making intelligent projections about where you could go is a skill transferable to any business. Dashboards can be created in a variety of ways or software packages, from MS Excel to Power BI and Tableau. In my opinion, MS Excel works great for simple dash boarding. You may need a variety of tabs in the background, but functions like COUNTIF, SUMIF, MAX/MINIF within a table with conditional formatting is fantastic for this purpose. Tableau can do these things as well, but the Tableau Server also lets you insert hyperlinks so users can see the data or individual record within the source context.

Scripting language, gives more choices to create a end to end pipeline for robust Solution. Scripting language like python and R helps us to store the code in GitHub. Maintaining code in github will enable us to retrace the changes in business logic. Libraries like plotly and dash in Python helps us to create a complete dashboard. From scratch.

I go with the scripting languages because of the extensive analytical libraries, advanced data processing capabilities, superior visualisation tools,flexibility and strong community support.

In my opinion once you learn how to visualise your data using Language like Python especially their libraries like Matplotlib,Seaborn. You end up saving a lot of time and most of the parts can be automated which makes it very time efficient.

1/ ?? Python: Widely used with powerful libraries like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and SciPy for advanced analysis. 2/ ?? R: Specialized for statistical computing with packages like ggplot2 for elegant data visualization, dplyr for data manipulation, and Shiny for interactive web apps. 3/ ?? Python + Plotly: Allows for the creation of interactive graphs and dashboards. 4/ ?? R + Tidyr: Simplifies data tidying and preparation for analysis.

My personal favorite scripting language is SQL. Writing queries against an SQL server provides a quick way to gather information and standardize reporting. T-SQL provides the same levels of speed and uniformity when updating a database, but therein beggars another question: that assumes a data pipeline automated along those lines. Gathering data, especially open source data, can be just as challenging as organizing it. For that, I really enjoy writing scripts for web scraping using the Selenium library or Python. If I had to manually look up a data point across 6,000 web pages, then the information is stale as soon as I finish. A bot (or a series of bots) can compile all that in a matter of hours.

1/ ??? Google Sheets: Allows real-time collaboration on data analysis and sharing insights. 2/ ?? Microsoft Teams: Integrates with Excel and Power BI for seamless data collaboration and sharing. 3/ ?? Notion: Combines notes, databases, and collaborative features to share data narratives. 4/ ?? Slack: Integrates with various data tools for sharing insights and fostering discussions. 5/ ?? Airtable: Combines spreadsheet functionality with database capabilities for collaborative data projects. 6/ ?? JupyterHub: Allows multiple users to work on Jupyter Notebooks collaboratively. 7/ ?? Microsoft Power BI Service: Facilitates sharing and collaboration on data reports and dashboards in the cloud.