The key stages of the data analytics lifecycle include:

1. Data Collection: This is the first step, where relevant data is gathered from various sources, including databases, sensors, or user inputs. The accuracy and quality of the collected data are crucial for the analysis.

2. Data Cleaning: Raw data is often messy, with missing values, duplicates, or inconsistencies. In this stage, the data is cleaned and pre-processed to ensure it's ready for analysis. This step improves the quality and reliability of the data.

3. Data Exploration: In this phase, analysts perform Exploratory Data Analysis (EDA) to understand patterns, relationships, and insights within the data. It helps identify trends and anomalies and sets the foundation for the next stage.

4. Data Modeling: Here, statistical models or machine learning algorithms are applied to the data to derive insights and make predictions. The choice of model depends on the problem being addressed.

5. Interpretation and Reporting: The results are analyzed, interpreted, and presented in a way that decision-makers can understand. Visualization tools like graphs and charts are commonly used.

To develop expertise in this process, exploring data analytics certification courses can help professionals build the necessary skills.