/ˈdeɪtə əˈnæləsɪs/
noun — "turning mountains of numbers into something that actually makes sense."
Data Analysis is the process in information technology and data science of inspecting, cleaning, transforming, and modeling data to extract useful insights, support decision-making, and identify patterns or trends. It forms the backbone of business intelligence, predictive analytics, and system optimization.
Technically, Data Analysis involves:
- Data collection — gathering raw information from databases, logs, sensors, or APIs.
- Data cleaning — correcting or removing inaccurate, incomplete, or inconsistent data.
- Statistical analysis — applying methods to summarize, compare, and infer patterns from data.
- Visualization — creating charts, graphs, or dashboards to communicate findings clearly.
Examples of Data Analysis include:
- Identifying peak usage times in network traffic for capacity planning.
- Detecting anomalies in server logs using anomaly detection techniques.
- Analyzing user behavior on a website to improve user experience and conversions.
Conceptually, Data Analysis is like turning a messy spreadsheet into a story that informs smart decisions. It transforms raw data into actionable knowledge for IT systems, business operations, and research.
In practice, Data Analysis uses tools like Python, R, SQL, and specialized software to process, visualize, and interpret data effectively.
See Data, Anomaly Detection, Business Intelligence, Python, Statistics.