BUSINESS ANALYTICS: WHAT IT IS & WHY IT'S IMPORTANT

What are the three main reasons companies worldwide are using data, as mentioned in the text?

• Improve efficiency and productivity, achieve more effective decision-making, and drive better financial performance.

• Enhance customer satisfaction, reduce costs, and increase market share.

• Optimize operational efficiency, increase revenue, and gain a competitive advantage.

According to the text, what is the difference between business analytics and data science?

• Business analytics focuses on making sense of raw data using algorithms, while data science uses statistical models and computer programming.

• Business analytics is concerned with the decision-making process, while data science focuses on collecting and analyzing data.

• Business analytics extracts meaningful insights from data to facilitate decision-making, while data science focuses on making sense of raw data using algorithms and statistical models.

What are some benefits of business analytics mentioned in the text? Select two.

• Streamlining operational processes and reducing costs.

• Enhancing customer service and improving employee satisfaction.

• Increasing market share and expanding into new markets.

• Making informed decision-making and driving better financial performance.

Why is studying business analytics important in today's business environment?

• It helps professionals improve their critical thinking skills.

• It allows organizations to make data-driven decisions and gain a competitive advantage.

• It provides opportunities for career advancement in the field of data analysis.

• It enables companies to effectively communicate data insights to stakeholders.

What skills are necessary for success as a business analyst, according to the text? Select two.

• Strong communication skills to effectively relay insights to non-technical stakeholders.

• Advanced programming skills to handle complex data analysis.

• Critical thinking skills to interpret data results and draw meaningful conclusions.

• Knowledge of advanced statistical models and algorithms.