Overview
This lecture introduces business analytics, covering its core concepts, types, life cycle, key tools, and related career paths.
What is Business Analytics?
- Business analytics transforms large volumes of data into meaningful insights to improve business decisions.
- Raw data often requires cleaning and scrubbing before analysis is useful.
- Analytics is more than visualizations; it includes extracting actionable information from data.
Examples and Related Terms
- Examples: predicting credit card subscribers, analyzing employee turnover, forecasting loan defaults.
- Common terms: business intelligence, decision science, data science, data mining—all aim to turn data into useful insights.
Types of Analytics
- Descriptive analytics: examines past data (e.g., sales, market share).
- Predictive analytics: forecasts future outcomes (e.g., expected sales).
- Prescriptive analytics: recommends actions based on predictions.
Analytics Life Cycle (CRISP-DM)
- Business understanding: define the business problem to solve.
- Data understanding: assess available and needed data, data quality, and frequency.
- Data preparation: clean and organize data for modeling; often the most time-consuming phase.
- Modeling: build models to mimic real-world processes and make predictions, selecting variables and tools.
- Evaluation and deployment: assess model accuracy and implement it in practice.
Popular Analytics Tools
- Microsoft Excel: data exploration and analysis.
- Tableau Desktop and Microsoft Power BI: data visualization and dashboards.
- Python and R: building predictive models.
- SQL: database interaction.
Careers in Analytics
- Business analytics sits between business, technology, and math skills.
- Some roles combine business functions with analytics, while others are analytics-focused.
- Common job titles: business analyst, business intelligence analyst, analytics manager, data analyst, and sometimes data scientist.
- Job postings typically require familiarity with key analytics tools.
Key Terms & Definitions
- Business Analytics — Using data to generate actionable business insights.
- Descriptive Analytics — Analyzing historical data to understand past performance.
- Predictive Analytics — Using data to forecast future outcomes.
- Prescriptive Analytics — Providing recommendations for actions based on predictions.
- Model — A simplified representation of a process to assist calculations and predictions.
- CRISP-DM — Cross-Industry Standard Process for Data Mining, the standard analytics life cycle.
Action Items / Next Steps
- Download the cheat sheet summarizing key points from codybaldwin.com.
- Choose a key analytics tool, download a free version or trial, and practice to build familiarity.