“Data wrapping is a technique used in data analytics and business intelligence to enhance the quality, accuracy, and usefulness of raw data.”
It involves layering additional information, metadata, or context around the original data to provide a more comprehensive understanding of its meaning and relevance.
Data wrapping enriches the raw data with additional attributes, such as time stamps, location details, data source identification, and data quality indicators. By adding these layers, data wrapping enhances the understanding and usability of the data.
According to MIT Sloan, effective data wraps will simultaneously:
→ Anticipate – predictive and proactive
→ Advise – data insights that inform customer decisions
→ Adapt – meeting customer needs in a personalized way
→ Act – integrated into customer processes or behaviors
One example of data wrapping is adding metadata to raw data. Metadata provides descriptive information about the data, including its source, format, structure, and any transformations applied to it. This metadata enables users to understand the origin and characteristics of the data, making it easier to interpret and analyze.
Benefits of data wrapping include:
- More accurate and consistent data from cleaning and transformation processes.
- A better understanding of customer behavior and operational processes.
- Promotion of data integration from the addition of consistent metadata.
Data wrapping is a powerful technique that enhances the value and usability of raw data. By layering additional information, metadata, and context, organizations can gain deeper insights, improve data quality, and drive informed decision-making.