making them visible , measurable and manageable .
It will be no longer be sufficient to be able to simply report on enterprise-wide operations . In order to compete and survive , the enterprise will need to be able to model and predict how necessary changes will affect output , profitability and sustainability goals .
To achieve this new level of visibility and control , new architectures are emerging .
There is now an expectation for future enterprise architectures to comprise a mix of 20 % core data centres , 30 % public cloud and 50 % Edge deployments within the next three years .
This will bring with it new levels of sensors ( IIoT ), monitoring , visibility , management and analysis . Cloudbased systems will oversee these new architectures , encompassing evermore elements , from manufacturing lines , retail floors and healthcare bedsides , to Edge deployments , regional data facilities and central data centres .
The foundation of this approach will be not just visibility of data , but orchestration of the data infrastructure , with the emerging capabilities of DCIM3.0 .
A major part of this new , software-enabled world is the deep integration of Artificial Intelligence ( AI ) and Machine Learning ( ML ).
Resilience is achieved through reliability and predictability . Predictive analytics , through the deep application of these
Natalya Makarochkina – Senior Vice President , Secure Power Division , International Operations , Schneider Electric
technologies , can allow for highly effective preventative maintenance regimes that can either detect or prevent failures before they can impact on operations .
Adaptation with insight
An example of how this is playing out in the food industry , where market garden producers are reducing their greenhouse temperatures as a direct result of rising energy costs .
With a full sensor deck , from pot to shelf , growers can model the temperature change , predict yields and crop changes and understand where cost optimisation needs to be adopted to cope with the scenario . By being able to process the data close to where it is produced , before central modelling crunches the numbers , growers can adapt labour , transportation and distribution requirements based on accurate data and informed analytics . Other examples include healthcare where bedside inputs form clinicians can be analysed in Edge deployments for realtime analysis .
These approaches to data gathering and initial analysis are only possible through the seamless integration of Edge capabilities with central data resources , through data infrastructure management , from DCIM to data lakes and AI-driven analytics .
In connecting all the connection points between the user and applications , we must be aware of security and the risk involved .
This is why we have developed strong partnerships to understand the risks and ensure our applications and services can maintain resilience , security and sustainability for tomorrow ’ s IT systems , even as they encompass an increasingly sprawling hybrid IT environment .
Standardised and science-based metrics
These major themes of resilience and security must be joined by that other critical requirement : sustainability .
A major benefit of this new softwareenabled world is the ability to apply standardised metrics across the board to measure and manage emissions and environmental impact . Sciencebased emissions targets are becoming more widely adopted and can help organisations understand and reduce emissions , even amid the current trends .
Enabling the world
Despite the pace of technological change , software systems and controls , building on developments in IIoT , AI and ML and cloud and Edge Computing is enabling enterprises to have greater visibility , insight and oversight of operations .
This is providing a level of resilience previously not possible , to cope with the maelstrom of influences in today ’ s world .
Enabling greater management of cyberrisk , as well as providing a basis for sustainability commitments and goals , software has gone from consuming the world to enabling it . �
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