Data Engineering Best Practices to Optimize Business Transformation
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Source Data Analysis: The Key to Understanding Results
There are various methods and techniques used to analyze source data, such as statistical analysis, data visualization, and machine learning. Statistical analysis is used to quantify and summarize the data, while data visualization is used to present the data in a visual format, making it easier to understand and interpret. Machine learning is used to find patterns and relationships in large data sets that are not easily visible with traditional statistical methods.
- Identify key business questions:Determine what specific information is needed to support business decisions and goals. This will help to focus the data collection and analysis efforts on the most important areas.Identify key performance indicators (KPIs) that will be used to measure success and track progress towards business goals. These metrics should be specific, measurable, and aligned with business objectives.
- Understand data sources: Identify all potential sources of data, including internal and external data sources, and assess the quality and relevance of each source.
- Assess data infrastructure:Evaluate the current data infrastructure and identify any gaps or limitations that may impact the ability to collect, store, and analyze data effectively.Engage with key stakeholders across the organization to understand their data needs and ensure that data engineering efforts are aligned with overall business goals. Evaluate the quality of the data by looking at factors such as accuracy, completeness, and consistency. Data quality is essential for making accurate and reliable decisions.
- Understand data format and structure: Determine the format and structure of the data, such as whether it is structured or unstructured, and whether it is in a tabular or non-tabular format. Review the data for compliance with data privacy and legal regulations.