Data optimization is the practice of making changes to an organization’s data strategy to improve the speed and effectiveness of extracting, analyzing and using the data. Data can be optimized for specific use tiered value cases including 1) active use like security monitoring and investigation, 2) selective use at scale including troubleshooting, forensic investigation and machine learning algorithm datasets, or 3) archive and audit use cases like compliance.
Data optimization offers many benefits including:
There are many ways to optimize data. Some common patterns include moving certain data to the cloud in a centralized location accessible to more people and applications, standardizing data formats, using algorithms to tune-up data to fit organizational goals and storing lower value or unnecessary, redundant data cost effectively.