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 russia whatsapp number data 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. Typical data engineering activities include purging duplicates and unnecessary values, ingesting new records and attributes, transforming data values – including normalizing and standardizing – and finally, handling the missing data.
Data Engineering Process
Missing data is defined as the value that is not captured and stored for a specific data variable, attribute, or field. Missing, lost, or incomplete data presents various problems to the business, such as:
Reducing the utility and relevance of data for operations, compliance, and analytics.
Reducing the statistical power of the insights derived. Statistical power or sensitivity is the likelihood of a significance test detecting an effect when there is one.
Causing bias in the insights derived. Data bias occurs when the data set is inaccurate and fails to represent the entire population. This, in turn, can lead to incomplete responses and skewed outcomes.