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Managing Missing Data in Analytics

November 13, 2023

Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data engineering. Data engineering is the extraction, cleansing, enriching, transformation, validation, and ingestion (and governance) of quality data into the consolidated system, commonly known as the data warehouse (or data mart or data lake). The data in the data warehouse is often the system of record from which the data scientists derive insights.