๐ง๐ต๐ฒ ๐ฐ ๐ง๐๐ฝ๐ฒ๐ ๐ผ๐ณ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐
Data creates value only when you understand it. Companies collect massive amounts of information every day. Collecting data is not enough. You must use it to make better decisions.
Data analytics moves through four stages. Each stage adds more value to your business.
- Descriptive Analytics: What happened? This stage summarizes past events. It uses historical data to show your current status.
- Examples: Monthly sales reports, website traffic, and revenue growth.
- Tools: Excel, Power BI, Tableau, and Google Data Studio.
- Diagnostic Analytics: Why did it happen? This stage looks for causes. It investigates trends and anomalies to find the root reason for a change.
- Examples: Finding out if a sales drop came from a technical bug or a marketing change.
- Methods: Drill-down analysis and correlation analysis.
- Predictive Analytics: What will happen? This stage forecasts the future. It uses patterns and statistical models to estimate what comes next.
- Examples: Predicting next month sales or identifying potential fraud.
- Tools: Machine learning, Python, and forecasting algorithms.
- Prescriptive Analytics: What should we do? This is the highest level. It does not just predict an outcome. It recommends a specific action to take.
- Examples: Suggesting new delivery routes or recommending a specific advertising budget.
- Methods: Optimization models and AI decision-making.
How they work together:
- Descriptive: You see revenue dropped by 15%.
- Diagnostic: You find website conversion rates fell.
- Predictive: You forecast a further 10% drop if you do nothing.
- Prescriptive: You decide to increase your ad budget and improve site speed.
AI is changing this field. AI now detects patterns automatically and suggests actions in real time. Modern professionals combine data skills with AI to drive strategy.
The companies that win will not just collect data. They will act on it.
Optional learning community: https://t.me/GyaanSetuAi