Workflow of Data Analytics

Workflow of Data Analytics

“Numbers have a compelling tale to share. They trust our ability to provide them with an authoritative and clear voice.” – Stephen Few

Raw data in its aggregated form lacks orientation. It requires a structured understanding and the right set of questions to derive meaningful insights. Many analytics efforts fail due to incomplete data interpretation, making it difficult for stakeholders to comprehend the findings. Therefore, data analysts must define, understand, and analyze data through a standardized workflow tailored to the type of analysis required.

Jeff Leek, in his book The Elements of Data Analytic Style, classifies different phases of analysis based on the questions posed and the expected outcomes. These phases help analysts streamline their approach to deriving insights from raw data.

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1. Descriptive Data Analysis

This type of analysis focuses on summarizing the raw dataset without making interpretations or predictions. Descriptive analytics can be both visual and quantitative, using statistics and simple graphical representations to provide a summary.

Example: Analyzing the student population enrolled in a course based on various categories such as total numbers, gender, age, and residency. This summary provides structured information but does not infer any relationships or predict future trends.

2. Exploratory Data Analysis (EDA)

EDA builds upon descriptive analytics by identifying patterns, trends, and correlations in the data. It helps generate hypotheses for further analysis and serves as a playground for discovering unexpected insights.

Example: Studying traffic patterns in different cities can reveal surprising insights, such as accident frequency at specific intersections, pollution levels caused by vehicular emissions, and weekly congestion trends. These findings may not directly answer a problem statement but offer valuable context for deeper analysis.

3. Inferential Data Analysis

Inferential analysis extends beyond exploratory data by determining whether findings from a sample dataset can be generalized to a larger population. It relies on statistical techniques to infer relationships between variables.

Example: Analyzing the correlation between students’ exam scores and the difficulty index of the questions for a sample of 100 students can provide insights into their performance trends. While causation isn’t established, inferential statistics help in drawing reasonable conclusions.

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4. Predictive Data Analysis

Predictive analytics forecasts potential outcomes using historical data. It applies statistical models and machine learning techniques to predict future trends or events.

Example: Predicting the success rate of political candidates based on factors like their campaign topics, ideological stance, and popularity metrics. While this analysis estimates probable outcomes, it cannot guarantee precise results.

5. Causal Data Analysis

Causal analysis determines how one variable affects another by modifying one factor and observing the impact. It differs from predictive and inferential analytics as it aims to establish cause-and-effect relationships.

Example: A clinical trial testing the effectiveness of a new drug by randomly assigning patients to a treatment group and a control group. If the results show a significant improvement in the treatment group, it indicates a causal relationship between the drug and patient recovery.

6. Mechanistic Data Analysis

Mechanistic analysis delves deeper into understanding how a particular effect is produced by analyzing the underlying mechanisms. It is often applied in fields like engineering and biological sciences.

Example: Studying how different wing designs influence airflow to minimize drag. This level of analysis requires domain expertise and advanced scientific methods.

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Conclusion

Big data analytics provides organizations with the ability to transform complex datasets into actionable insights. By following a structured analytical workflow, businesses can derive meaningful intelligence that enhances decision-making. Understanding different types of data analysis ensures that analysts can extract valuable insights efficiently, making their contributions ten times more impactful for stakeholders.

Harnessing data analytics is not just about finding answers; it’s about uncovering the right questions that drive informed decisions and business growth.

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