The Data Analysis Workflow The Data Analysis Workflow

The Data Analysis Workflow

The Data Analysis Workflow cont. § § § Data Collection: Data is typically collected from various sources and stored in preparation for cleansing. Data Cleansing: During the data cleansing phase, outliers and anomalies are identified, missing values are removed, and data is prepared for detailed analysis. Data Analysis: Once data has been cleaned and prepared, the next step is to analyze the data. Data models are built and ran repeatedly for improvement. “Data Mining” and “Data Profiling” are two types of analysis performed by Data Analyst.

Creating your Data Model A model developed for your dataset should have predictable performance. A successful model is required to predict future outcomes. Data Models: • • • Easily adapt to changes according to business requirements. Scale according to the data after modifications. Easily consumed by the clients for actionable and profitable results.

Data Mining vs Data Profiling Data Mining: Data Mining refers to the analysis of data with respect to finding relations that have not been discovered earlier. It mainly focuses on the detection of unusual records, dependencies and cluster analysis. Data Profiling: Data Profiling refers to the process of analyzing individual attributes of data. It mainly focuses on providing valuable information on data attributes such as data type, frequency etc.

Data Workflow Replication • • Being able to replicate a process is an essential component of good analysis. An effective workflow is essential for an analyst to replicate their findings.

Data Workflow Finding the right answers • Without the use of the workflow an analyst is prone to errors and often need to retract findings. Saves Time • Workflows help you create a system of efficiency.