12 Guidelines for Success in Data Quality Projects

April 13, 2018

The need for accuracy, completeness, and quality of data generated and used in companies and organizations is not a new concept. The “father of computing”, Charles Babbage, asked over 150 years ago how “the right answers” could come out of his computing machine if the “wrong figures” were put in. The concept of “Garbage In, Garbage Out” was created by the earliest programmers in the 1950’s and subsequently taught to generations of IT professionals. This paper discusses key characteristics of data quality initiatives and provides actionable guidelines to help make your project a success, from conception through implementation and tracking your ROI.

Spotlight

Vaultree

Vaultree has developed the world’s first fully functional data-in-use encryption solution that solves the industry’s fundamental security issue: persistent data encryption, even in the event of a leak. Vaultree enables enterprises, including those in the financial services and healthcare /pharmaceutical sectors, to mitigate the great financial, cyber, legal, reputational, and business risk of a data breach. With Vaultree, organisations process, search and compute ubiquitous data at scale, without ever having to surrender encryption keys or decrypt server-side. If a leak occurs, Vaultree’s data-in-use encryption persists, rendering the data unusable to bad actors. Integrating Vaultree into existing database technologies is seamless, requiring no technology or platform changes. Google and other data-driven companies worldwide trust Vaultree to safeguard sensitive data.

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