There is core data, people, places, and things that needs to be tracked across your enterprise, the main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles.
Enterprise Data Governance supports high, and strives to prevent low, data quality, ensuring that the data can be trusted — and people will have to be held accountable for any adverse event that may happen, when evaluating a desktop or application virtualization platform, it should make sure it can effectively manage and protect application files, configuration settings, personal information and more.
With data environments getting more complex, diverse, and challenging to manage, organizations are discovering new technologies and methods for accessing and converting data from disparate data sources into real, actionable insight, because information is one of your most important assets, it should be closely monitored, conversely, when there is problem within the system, it may be easy to trace the cause of the problem without having to use a top down approach for the whole data warehouse.
No longer does the corporate IT organization have a monopoly on the development and implementation of software, singularly, information and business processes often remain siloed in these landscapes.
In the technology world, data obfuscation, which is also known as data masking, is the process of replacing existing sensitive information in test or development environments with the information that looks like real production information, but is of no use to anyone who might wish to misuse it, although it currently has only a few real-world applications, iot data analytics is gaining strength as it rides on the shoulders of the big data trend. Therefore, maintain an ongoing professional development in analytics and BI tools, and high-reaching, business intelligence techniques, master data management, information management and architecture, and vendor developments.
Develop and maintain advanced features, tools, and applications according to best practices in UI, UX, front-end development and hybrid mobile application development. Test and debug your ever-evolving data to improve their speed, scalability, and usability. Given that data typically exists in a multitude of different systems throughout organizations as well as within third-party (e.g, cloud) environments, internal audit frequently encounters difficulties when attempting to access data for analytics, plus, take the time and effort to ensure that each data element you intend to use for matching has good quality data.
As big data tools and technologies continue to rapidly change, cloud-based data lakes can be used as development or test environments to evaluate new tools and technologies before bringing them to production, either in the cloud or on-prem. Then next, governed self-service, which is implemented through a strong, yet flexible IT infrastructure and centralized data management, ensures consistently reliable data quality that users can trust.
A data culture can positively alter your organizations ability to adapt and thrive.
Want to check how your Enterprise Data Governance Processes are performing? You don’t know what you don’t know. Find out with our Enterprise Data Governance Self Assessment Toolkit: