Crunching the Numbers: Key Data Analysis Skills for Business Analysts

Kenneth Gray

1. Statistical Knowledge

Why it's essential: A fundamental understanding of statistics is required to extract useful insights from data.

Practising the skill: Learn about basic statistical concepts such as mean, median, standard deviation, and correlation. Over time, delve deeper into statistical hypothesis testing and regression analysis.

2. Data Visualisation

Why it's essential: A picture can express a thousand words. Data visualisation aids in the translation of complex data sets into simple, visual tales.

Practising the skill: Learn tools like Tableau, Microsoft Power BI, or QlikView. Understand the fundamentals of excellent data visualisation to ensure that your graphs and charts are clear, simple, and actionable.

3. Data Cleaning

Why it's essential: Raw data might be disorganised. BAs must be competent at finding and correcting data errors in order to obtain true insights.

Practising the skill: Learn about technologies like OpenRefine and Python's Pandas library. Understand common data quality challenges and how to solve them, such as dealing with missing numbers or outliers.

4. Database and SQL Knowledge

Why it's essential: The majority of organisational data is stored in databases. Knowing how to query databases with SQL(Structured Query Language) can be extremely useful.

Practising the skill: Begin with studying the fundamentals of SQL, such as how to write SELECT statements, JOIN tables, and use WHERE clauses. As you advance, investigate more complicated operations such as subqueries and stored procedures.

5. Programming & Advanced Analytics

Why it's essential: While programming knowledge isn't required for every BA role, it can help you execute advanced data manipulations and even machine learning.

Practising the skill: Python and R are great places to start because of their rich data analysis libraries. For machine learning, look into Python's Scikit-learn or R's ggplot2 for advanced visualisation.

6. Problem Solving & Critical Thinking

Why it's essential: Data analysis is both an art and a science. The ability to approach problems critically and spot patterns in data is priceless.

Practising the skill: Constantly challenge yourself with real-world data puzzles. Participate in online community platforms such as Kaggle to test and improve your analytical skills.

7. Knowledge of ETL Processes

Why it's essential: ETL (Extract, Transform, Load) operations are critical to preparing data for analysis, particularly in large organisations.

Practising the skill: Understand the fundamentals of data extraction, transformation strategies, and the principles behind data warehouse loading. Tools such as Microsoft SSIS and Talend can be valuable.

8. Stay Updated with Emerging Technologies

Why it's essential: The data landscape is ever-changing. New tools, platforms, and approaches are constantly being developed.

Practising the skill: Participate in the BA and data communities. Take advantage of workshops, webinars, and conferences. Subscriptions to sites such as Towards Data Science or Data Science Central can also be beneficial.


The line between a Business Analyst and a Data Analyst is becoming increasingly blurred in the digital era. Business Analysts may contribute enormous value to their organisations by creating a strong foundation in data analysis, enabling data-driven decision-making and fostering innovation. It's time to take on the data revolution one skill at a time!