Summary
Highlights
Data analytics is the process of analyzing raw data to extract useful insights for businesses. These insights are crucial for making informed decisions, solving problems, and identifying patterns related to customer and employee engagement, ultimately helping companies understand their audience and industry better.
Data analytics is widely used across all businesses to make faster, better decisions, reduce costs, and develop innovative products and services. Specific applications include predicting sales, protecting against fraud, analyzing marketing campaign effectiveness, boosting customer acquisition, and improving supply chain efficiency.
A data analyst's daily responsibilities include managing user satisfaction surveys, reporting using data visualization tools, defining success metrics, managing analytical projects, identifying improvement opportunities, translating questions into analytical tasks, collecting and organizing data, designing back-end code, establishing data processes, defining data quality, and collaborating with teams to shape business strategies.
The data analysis process involves five main steps: defining the questions to answer, collecting relevant data from primary and secondary sources, cleaning and preparing the data by removing duplicates and anomalies, analyzing the data using techniques like regression and cluster analysis, and finally, interpreting and sharing the results through charts and graphs with stakeholders for future collaboration.
Key skills for data analysts include mathematical and statistical ability, working knowledge of programming languages like Python, Oracle, and SQL, an analytical mindset for deeper understanding, strong problem-solving skills to choose the right tools, and excellent communication skills to effectively share insights with stakeholders and the entire company.