| NOTAS: | "The enterprise today is faced with two seemingly conflicting drivers: the pressure to leverage any (rather, all) data for business advantage, and the regulatory demand to defensibly lock down that same information for protection. Legislation such as the EU's General Data Protection Regulation (GDPR) has led many organizations to believe that their analytics efforts will be negatively impacted, reducing the volume of data available for analysis. However, both data protection and data analysis initiatives share the common need to be able to granularly control data at the source. Today's business challenge is striking a balance between these two needs, protecting data and leveraging data, by architecting data governance practices and infrastructure that can control data with a high level of precision at the source, wherever that data may reside. This report will explore the trends that are helping the enterprise balance the needs of both data protection and data analytics, achieving a new, highly functional normal as the world adopts increasingly sophisticated protections for personal data. Ovum has identified four key business and regulatory trends that will help shape the market for data governance technology in 2018 and beyond. Data protection legislation is becoming more commonplace, and under such regulations, the ability to granularly control all enterprise data will increasingly be a competitive differentiator for organizations: both in meeting regulatory requirements and in effectively leveraging data for analysis. Data control is the key to both compliance and profitability. Consumers are becoming more aware of the value of their personal data, and regulations support their rights to determine the appropriate use of that data. Technologies that facilitate consumer volition over personal data will strengthen trust and confer a competitive advantage if the technologies are properly integrated with existing information management architecture. Enterprise metadata, which encodes the policies assigned to data, will be an invaluable resource for artificial intelligence (AI) and analytics, as it provides a large, standardized data set to work from, exposing how data is used within the enterprise while excluding sensitive fields." |
