Excel in Bi

In today’s data-driven world, mastering the core data analytics techniques is crucial for effective decision-making. These methods, while simple, are the backbone of successful data analytics strategies.

1. Descriptive Analytics: Understanding Past Data

Purpose: Summarizes and interprets historical data to identify patterns and trends.

Techniques: Mean, median, mode, standard deviation, frequency distributions, charts, and graphs.

Examples: Sales reports, average customer ratings, and website traffic summaries.

2. Diagnostic Analytics: Understanding Why Something Happened

Purpose: Delves into data to uncover the reasons behind past performance.

Techniques: Drill-down analysis, data discovery, data mining, correlations.

Examples: Investigating sales decline, and identifying root causes of customer turnover.

3. Predictive Analytics: Forecasting Future Trends

Purpose: Uses historical data and models to forecast future events.

Techniques: Machine learning algorithms, statistical models, time series analysis, regression analysis.

Examples: Sales forecasting, predicting customer behaviour, risk assessment in finance.

4. Predictive Analytics: Recommending Actions Based on Data

Purpose: Suggest actionable strategies to achieve desired outcomes.

Techniques: Optimization, simulation, decision analysis, machine learning.

Examples: Supply chain optimisation, personalised marketing strategies, resource allocation.

Mastering these four key techniques transforms raw data into valuable insights and actionable strategies, driving success in data-driven endeavours. Whether understanding past performance, diagnosing issues, predicting trends, or prescribing actions, these techniques are essential tools for unlocking data’s full potential.

Learn more of this and more Data topics in our monthly Masterclasses

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