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Big Data: Developing a Standard for Measuring Data Quality
The National Physical Laboratory (NPL) in the UK in developing a systematic approach to measuring the quality and accuracy confidence of data.
Alistair Forbes, Fellow at NPL, said that “we’re trying to apply our way of thinking that comes from the measurement domain to thinking about how it applies to the digital domain.”
NPL says that there are four “C’s” that need to be considered when working with Big Data:
- Collection – verifies the data source, it’s credibility and accuracy
- Connection – verifies that there was no problem in transporting/transmitting the data and that proper error correction was used if there was any interference
- Comprehension – quantifies that multi-sourced data is properly used
- Confidence – applies a measure to the level of confidence of its accuracy
Melanie Mecca, director of data management services at the CMMI Institute, said that “not much attention was paid to data. It was seen as the toothpaste in the tube of features, technology and automated capabilities. The data itself was never viewed as the foundation and the life blood of the organization’s business knowledge. That is why it has been neglected.”
