443.The Art Of Turning Imperfect Data Into Powerful Business Intelligence With SAS
Can A Step-By-Step SAS Data Cleaning Project Turn Messy Data Into Reliable Business Intelligence? Introduction: Why Data Cleaning Is The Real Intelligence Layer If raw data is the “fuel,” then data cleaning is the refinery. Without it, even the most advanced analytics machine learning, forecasting, or regulatory reporting collapse under silent inaccuracies. In real-world clinical, banking, or retail systems, datasets are rarely pristine. They arrive fragmented, inconsistent, and sometimes outright misleading. As a SAS programmer, your value isn’t just writing code it’s restoring data integrity . In this project, we will simulate a real-world SAS Data Cleaning Project using a deliberately flawed dataset. Then, step-by-step, we will transform it into a business-ready, validated dataset . The Raw Dataset (With Intentional Errors) SAS Code (DATALINES) DATA raw_data; INPUT ID Name $ Age Salary Join_Date : DATE9. Score; FORMAT Join_Date DATE9.; DATALINES; 1 John 25 50000 01JAN2...