Information Quality

It could be argued that most, if not all of the principles relating to processing of personal data in Article 5 are dependent on the quality of personal data. In particular, that personal data shall be accurate and kept up to date; every reasonable step must be taken to ensure that inaccurate personal data… are erased or rectified without delay. Data (information) quality is fundamental to protecting data subjects’ access rights under the GDPR.

What is meant by data (information) quality?

‘Data’ and ‘information’ are used interchangeably. The term is often defined as: the fitness for use of the information provided. Quality information begins with the collectors of data.  As data is a resource that is reused many times, costs arising from any flaws or inadequacies in the data are compounded and multiplied through this reuse.  If the data collected is bad, then the errors within will perpetuate and grow throughout the information value chain.

Why should you maintain the quality of personal data?

Quite often an organisation will want to fix their existing data. This has limited use, because unless the processes for procuring and maintaining the data are addressed, 'cleaned' data will soon be swamped under a relentless tide of new dirty data.  Quality must be designed into the fabric of an organisation.

Compliance: Maintaining quality data can be the difference between compliance and hefty fines. Upholding all of the GDPR’s principles requires quality personal data. Data quality is fundamental to protecting data subjects’ access rights under the GDPR. Having quality data should make it relatively easier to manage personal data breaches or security incidents. And it’s not only for reasons of compliance that we need quality data.

Decision making: The better the data quality, the more confidence users will have in the outputs they produce, lowering risk in the outcomes and increasing efficiency. The old ‘garbage in, garbage out’ adage is true, as is its inverse. And when outputs are reliable, guesswork and risk in decision making can be mitigated.

Productivity: Good-quality data allows staff to be more productive. Instead of spending time validating and fixing data errors, they can focus on their core mission.

Marketing: Better data enables more accurate targeting and communications. Especially where customers have objected to receiving marketing communications.

Lost revenue: Poor data can lead to lost revenue in many ways — e.g. communications that fail to convert to sales because the underlying customer data is incorrect.

Reputational damage: Reputational costs range from the small, everyday damage that organisations may never be aware of, to large public relations disasters. Efforts to improve the customer experience may also be undermined by bad data resulting in an incorrect spelling of a customer’s name, or obliviously sending communications to a deceased customer. The same may be said of employees’ personal data.


The content herein is provided for your convenience and does not constitute legal advice.
Compliance Technology Solutions B.V. 2018

R
Russell is the author of this solution article.

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