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The New DMA Initiative Could Be a Model for Other Industries to Follow

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The New DMA Initiative Could Be a Model for Other Industries to Follow

A recent announcement of a transparent and standardized approach to data quality gets the thumbs up. It offers tools for data labeling, agreed by consensus and validated by peer review.

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One gets the sense that the business world is finally seeing the imperative nature of data quality solutions — so much so that we're witnessing non-profit, membership organizations in the data and marketing space offer new initiatives on this issue as a membership benefit. The Data and Marketing Association recently announced the beginning of the Data Quality Labeling Standards program to help marketing professionals and data experts in the marketing field make better choices on third-party data vendors. To us, this seems like a fantastic initiative for other industries to follow.

Data Quality Standardization for the Marketing Industry

The program is nothing short of ambitious. Its focus will be on the creation of tools to offer marketing and media companies the following:

  1. An industry-agreed, data labeling standard for commercially available audience data that underlies tailored audience segments.
  2. Consensus among critical industry organizations, representing all members of the ecosystem.
  3. Peer-reviewed validation methods that make data labels comparable, and raise confidence across all users.

This initiative could not have come at a more opportune time for the industry, with the Facebook and Cambridge Analytica scandal shaking the confidence of many marketing and media companies, not to mention the faith and confidence of consumers in how these companies utilize consumer data. A more transparent, standardized approach to data quality is exactly what the industry needs.

How Would This Pair With Other DQ Solutions?

It should be noted that this initiative focuses only upon standardizing the different levels of data quality on offer in the marketing industry, namely the purchase of consumer data from third-party list vendors. This does not, then, solve any current data quality issues or even reduce the need for a data quality solution. The labeling system simply standardizes the different levels of data quality, much like, as the DMA points out, the FDA labels qualities of food for consumers. But this initiative is incredibly helpful at the point of sale, helping companies ensure a better ROI by purchasing higher-quality lists with DMA-back guarantees of quality and effectiveness. This is a far cry from the current situation in the industry, with list and data acquisition being more akin to the Wild West's version of snake oil salesmen and disappointed consumers.

This initiative could very well be an excellent model for other industries to follow. Imagine a uniformed and standardized data quality apparatus in the financial industry, manufacturing, energy, etc. Finally, actors in each industry can finally speak the same data-related language about the metrics and data within their industries. Plus, having data quality solutions on-hand would make proving adherence to these initiatives that much easier for the companies involved.

Read up on the Data Quality Labeling Standards and let us know what you think in the comments!

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big data ,data quality ,data validation

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