Did you know that according to a McKinsey report, organizations that heavily leverage data analytics are 23 times more likely to acquire customers and 6 times more likely to retain them? To transform this raw data into actionable insights, businesses rely on business analytics tools. Choosing the right tool is crucial for staying competitive and making informed decisions. Here are 10 Business analytics tools that every working professional or student in business should know.
Business decisions today depend on data. Companies collect large volumes of information from various sources. But raw data alone cannot help. It needs to be processed, sorted, and analysed. That’s where business analytics tools play a key role. They help in turning data into useful insights.
1. Microsoft Excel
Excel is still the most used tool in data analysis. It is easy to learn and works well for basic tasks. It helps users organize, analyze, and visualize data. People commonly use it for tasks like data entry, calculations, creating dashboards, charts, pivot tables, and reports.. Excel features a grid of cells arranged in rows and columns. It offers various functions and formulas to perform mathematical, statistical, and logical operations. Businesses and individuals widely use Excel for budgeting, financial analysis, data management, and more.
2. Power BI
Power BI is a business analytics service from Microsoft. It provides interactive visualizations and business intelligence capabilities. Users can connect to various data sources, transform data, and create insightful reports and dashboards. These visuals and reports help in analyzing data, identifying trends, and gaining actionable insights. It offers features for data preparation, visualization, analysis, and collaboration. Also allows real-time sharing of reports across teams. It is suitable for users at all levels.
3. Tableau
Tableau is a powerful data visualization tool focused on helping people see and understand data with ease. It enables users to connect to various data sources, then create interactive and shareable dashboards and reports. You can drag and drop data to make charts, graphs, and dashboards. It works well for large datasets. Many firms prefer Tableau for its clear visuals and user-friendly features.
4. Google Data Studio
Google Data Studio, now known as Looker Studio, is a free web-based data visualization tool. It allows users to connect to various data sources within the Google ecosystem and beyond. You can create interactive reports and dashboards with customizable charts and tables to analyze and share insights. This is a free tool from Google. It helps you connect data from Google Sheets, Ads, Analytics, and more. You can build reports that update automatically. Looker Studio emphasizes collaboration and easy sharing, enabling teams to work together on data exploration and present findings in an accessible format. Its integration with Google’s services makes it a convenient option for many users. It is best for marketing and digital reporting tasks.
5. R
R is a free and open-source programming language and environment primarily used for statistical computing and graphics. It’s highly extensible, with a vast collection of packages available for various statistical techniques, data analysis, machine learning, and data visualization. Also, help in data cleaning, modelling, and testing. If you are from a research or analytics background, learning R can help. While its syntax can be challenging for beginners, its power and flexibility for in-depth statistical analysis are highly valued.
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6. Python
Python is a versatile and widely used high-level programming language known for its readability and clear syntax. It is popular in both business and tech fields. It is used for tasks like automation, data analysis, and machine learning. Libraries like Pandas, NumPy, and Matplotlib make Python useful for working with data.It supports multiple programming paradigms, including object-oriented, imperative, and functional styles. With its extensive standard library and a vast ecosystem of third-party packages, Python is employed in diverse fields such as web development, data science, machine learning, artificial intelligence, scientific computing, and automation. Its ease of learning and broad applicability make it a popular choice for both beginners and experienced developers.
7. SQL
SQL, or Structured Query Language, is the standard language for managing and manipulating data in relational database management systems (RDBMS). It allows users to define, query, update, and control data. With SQL, you can retrieve specific information, insert new records, modify existing data, and create or alter database structures. Its declarative nature focuses on what data to retrieve rather than how. Proficiency in SQL is crucial for anyone working with databases for data analysis, application development, and business intelligence.
8. SAS
SAS (Statistical Analysis System) is a comprehensive software suite widely used for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics. It offers a robust platform for handling large datasets, performing complex statistical procedures, and generating detailed reports and visualizations. Known for its reliability and extensive statistical capabilities, SAS is a popular choice in industries like finance, healthcare, and government for critical data-driven decision-making. While proprietary, its powerful analytics tools and structured environment are highly valued for business.
9. Alteryx
Alteryx is a self-service data analytics platform that empowers users to prepare, blend, and analyze data from various sources without 1 extensive coding. Its intuitive drag-and-drop interface allows for the creation of automated workflows for data transformation and analysis. Alteryx excels at combining data from disparate systems, performing complex calculations, and generating insightful reports and visualizations. It aims to democratize data analytics, enabling business users to derive actionable insights efficiently and drive data-driven decisions across organizations.
10. Qlik Sense
Qlik Sense is a modern, self-service data analytics platform that allows users to explore data and create visualizations independently. Its key feature is its associative engine, which enables dynamic data exploration by understanding relationships between data points. With an intuitive drag-and-drop interface, users can easily build interactive dashboards and reports from various data sources. Qlik Sense emphasizes user empowerment, facilitating data-driven insights and collaboration across teams. It offers smart visualizations and supports embedded analytics for integration into other applications.
Also Read: Top Skills for Business Analytics Professionals
Business analytics is no longer limited to IT teams. Every department now uses data to make better decisions. Learning these tools will help you stay relevant in today’s job market. You don’t need to learn all at once. Start with one or two, practise regularly, and keep improving with time.
