%0 Journal Article %1 noauthororeditor %A v Smirnov |, Sergi %D 2023 %J CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES %K Archiving, Data Digitalization, Enterprise Management Systems Web application, %N 11 %P 55-60 %T REINVENTING ENTERPRISE DATA ARCHIVING FOR THE DIGITAL ERA %U https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/552/594 %V 4 %X This paper examines next generation approaches to transform enterprise data archiving methodologies for the burgeoning digital age. We analyze the limitations of traditional archiving and the innovations needed to effectively manage massive growth in enterprise data volumes. Detailed sections are included covering the following aspects:• The exponential growth in enterprise data and ensuing archiving challenges• Intelligent policy-based automation to enable smarter archiving.• Cloud-native architectures for highly scalable archives• Persistent metadata synchronization for greater archived data utility • Comparison of on-premises, cloud, and hybrid archiving models• Archiving best practices related to security, retention, discovery etc. • Case studies of real-world archiving implementations• Recommendations for a modern holistic enterprise archiving strategyAdvanced data archiving techniques such as machine learning policies, cloud repositories, active metadata, and unified cross-platform access are imperative today to control data sprawl, accelerate insights, and ensure regulatory compliance. By reimagining archiving for the digital age, enterprises can cost-efficiently extract maximum value from data while minimizing risks.

@article{noauthororeditor, abstract = {This paper examines next generation approaches to transform enterprise data archiving methodologies for the burgeoning digital age. We analyze the limitations of traditional archiving and the innovations needed to effectively manage massive growth in enterprise data volumes. Detailed sections are included covering the following aspects:• The exponential growth in enterprise data and ensuing archiving challenges• Intelligent policy-based automation to enable smarter archiving.• Cloud-native architectures for highly scalable archives• Persistent metadata synchronization for greater archived data utility • Comparison of on-premises, cloud, and hybrid archiving models• Archiving best practices related to security, retention, discovery etc. • Case studies of real-world archiving implementations• Recommendations for a modern holistic enterprise archiving strategyAdvanced data archiving techniques such as machine learning policies, cloud repositories, active metadata, and unified cross-platform access are imperative today to control data sprawl, accelerate insights, and ensure regulatory compliance. By reimagining archiving for the digital age, enterprises can cost-efficiently extract maximum value from data while minimizing risks.}, added-at = {2024-04-25T12:50:52.000+0200}, author = {v Smirnov |, Sergi}, biburl = {https://www.bibsonomy.org/bibtex/2dfcdc96ed9e378a8cdb42a3d8ce09d15/centralasian_20}, interhash = {782b5485df79a118ac70c3498482be4a}, intrahash = {dfcdc96ed9e378a8cdb42a3d8ce09d15}, issn = {2660-5309}, journal = {CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES}, keywords = {Archiving, Data Digitalization, Enterprise Management Systems Web application,}, language = {english}, month = nov, number = 11, pages = {55-60}, timestamp = {2024-04-25T12:50:52.000+0200}, title = {REINVENTING ENTERPRISE DATA ARCHIVING FOR THE DIGITAL ERA}, url = {https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/552/594}, volume = 4, year = 2023 }