Based in Sydney, Australia this a Blog of Associate Professor AmandeeP Sidhu. it focuses on everything Artificial Intelligence, Big Data and Biomedical Informatics.

Managing 163ZB of Data in 2025

The 163ZB Data Problem

Seagate and IDC released a white paper in April 2017 describing Data Age 2025. Since reading the white paper I am on look out for current or any R&D approaches that address the key recommendations made by the authors:

Data Criticality

IDC estimates by 2025 20% data will be critical and 10% will be hypercritical. We need to develop approaches for data management and storage that lower latency and aid real-time processing.


Real-Time Data Analytics

IDC predicts that will grow at 1.5 times the rate of overall data creation and majority of real-time data use will be driven by IoT devices. IDC also estimates that in 2025 26% of the data will be processed, stored or delivered by public cloud. Again increase in real-time data will require low-latency responsiveness from edge storage and endpoint solutions.


Artificial Intelligence and Data Tagging

Data Tagging is and important aspect of AI and cognitive systems. However IDC says that there is a large gap between the amount of tagged data and the amount that could benefit from tagging. IDC estimates that by end of 2025 only 15% of the data will be tagged and only one-fifth of that will actually be analysed.


Data Security

IDC says a vast majority of the data requires at least some sort of protection and actual amount of data protection falls far short of that. Privacy, Security and Ethics needs to be the foundation of the 163ZB Data Management Strategy so I am covering this recommendation in my next blog post.


163ZB Data Management Solution(s)

Now lets see what is out there to address this 163ZB Data Problem.

Data Storage

IDC predicts that growth in real-time data will cause shift in type of digital storage needed in future. Also from the huge amount of data created, we have to classify and prioritise data that needs to be retained post analysis.


More Data Tagging

We need to use Semantic Artificial Intelligence and automatic and semi-automatic approaches to improve the percentage of tagged data. This will also improve the quality of input data for AI and cognitive systems.

AI without Quality Data is useless

My ANZ Gartner Symposium Journey in Pictures

My ANZ Gartner Symposium Journey in Pictures