Disaster recovery in the age of data and AI

As data becomes the only real competitive advantage feeding increased operational efficiencies, better customer intimacy and constantly improving customer experience, it is imperative that enterprises shift their disaster recovery efforts from just focusing on availability and reliability of services to ensure that their data assets are recoverable and re-integratable into various data powered scenarios backing their business. Modern enterprises require data in many shapes and forms across the board for powering planning, ideating, experimenting and designing/developing new products and services. These business-critical scenarios are often dependent on data that has been transformed, processed and made suitable to meet the requirements. As the "distance" between raw data and transformed data that drives products and services increases due to increasingly complex techniques of transformation, disaster recovery needs to include the not just the time to bring up the copy of lost data online but the time it takes to retransform the data. AI techniques such as Machine Learning, NLP, Anomaly Detection etc. produce "models" that can be leveraged to drive predictions, classifications and categorization.