𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
Raw data has little value alone. You must refine and analyze it to create business intelligence. Following a structured process ensures you solve the right problems.
The Data Analytics Lifecycle consists of six phases:
• Business Understanding • Data Collection • Data Preparation • Data Analysis • Data Visualization • Deployment and Monitoring
Phase 1: Business Understanding Start with the problem. Do not touch data until you know what goal you want to reach. Ask: What problem are we solving? How do we measure success?
Phase 2: Data Collection Gather information from CRM systems, databases, and websites. For an e-commerce store, you need customer IDs, product categories, and session durations.
Phase 3: Data Preparation Data professionals spend most of their time here. You must clean, transform, and standardize data. Remove duplicates and fix errors to ensure your results stay reliable.
Phase 4: Data Analysis This stage finds patterns.
- Descriptive: What happened?
- Diagnostic: Why did it happen?
- Predictive: What will happen?
- Prescriptive: What should we do?
Example: If mobile users abandon carts more than desktop users, the analysis shows a slow mobile checkout.
Phase 5: Data Visualization Decision-makers need clear visuals. Use tools like Power BI, Tableau, or Python to turn complex numbers into stories. This helps stakeholders make fast decisions.
Phase 6: Deployment and Monitoring Put your insights into action. If you find a slow checkout page, fix it. Monitor the results to ensure the changes improve your revenue.
Modern teams now integrate AI into this lifecycle. AI helps find missing values, predicts customer churn, and automates report generation.
Successful projects require clean data and clear business goals. Do not skip stages or you will face incorrect conclusions.
Source: https://dev.to/raju_ashokit_8ce772fb366a/data-analytics-lifecycle-explained-with-real-examples-3gek
Optional learning community: https://t.me/GyaanSetuAi