Why it's essential: To get useful information from data, you need to know the basics of statistics.
Practising the skill: Learn about basic statistical concepts such as mean, median, standard deviation, and correlation. Over time, go deeper into statistical hypothesis testing and regression analysis.
Why it's essential: A picture can express a thousand words. Data visualisation helps 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 data visualisation to ensure that your graphs and charts are clear, simple, and actionable.
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.
Why it's essential: The majority of organisational data is stored in databases. Knowing how to query databases with SQL 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.
Why it's essential: While programming knowledge isn't required for every BA role, it can help you complete advanced data manipulations and even machine learning.
Practising the skill: Python and R are great places to start because of their data analysis libraries. For machine learning, look into Python's Scikit-learn or R's ggplot2 for advanced visualisation.
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.
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.
Why it's essential: The data landscape is ever-changing. New tools, platforms, and approaches are constantly being developed. I personally check websites like Producthunt to see new emerging technologies.
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.
In the digital age, it's become harder to distinguish between a business analyst and a data analyst. By building a solid basis in data analysis, encouraging data-driven decision-making, and supporting innovation, business analysts can add significant value to their businesses.
