My typical data analysis process

• Asking smart questions: helping stakeholders figure out both their expectations, and data requirements for business decisions. 

• Data Collection: After getting a solid grasp of the business idea, I collaborate with primary stakeholders to  define what data type and volume will be sufficient for analysis, Then figure out if the data is readily available in the organization, or if this analysis will require additional data sources. This ensures  the focus of the analysis is at the problem to be solved.

• Data Cleaning and Normalizing: After combining and collecting data from multiple sources, the data needs to be cleaned of superfluous fields and values, and then normalized (An example of this is the numerous ways with which dates can be written, i.e. 1/12/22, 12-1-22, 22-1,12 and 12th January 2021) hence it is necessary to have specific, widely acceptable units and schemas for handling data. 

• Data Analytics: Tell the data story, to help solve business problems and lead organizations into making well thought out decisions.

• Interpret Results: Choose appropriate diagrams and charts to help describe my findings to stakeholders in a manner that is compelling and easy to comprehend.

• Re-iteration: Depending on the business goals, multiple rounds of analytics may be needed to help decision-makers adjust to new customer demands, and the economic climate.