Analytics That Give the Digital Edge the Edge
Blog: The Tibco Blog
As the natural successor to cloud’s status as the primary disruptor of IT, all signs are pointing to edge computing as being best placed to drive the next era in today’s digital evolution.
There’s a reason why: We know that if optimal value is to be extracted from data then intelligence needs to be in captured in real time, at its freshest state as the basis to drive immediate operational data decisions.
Decentralizing the cloud and placing data storage and processing closer to the actual data source provides the agility to make this possible. Extracting value from the data sooner enhances security and privacy by slashing the time data travels across the network and with it potential exposure to corruption.
It’s a traction evident across a myriad applications, but perhaps no more acutely felt than in the IoT space and especially industrial IoT (IIOT), where a number of factors conspire to intensify the connectivity challenge for businesses sector-wide.
Here, with huge volumes of data transported across the variety of sensors, devices, assets and machinery in the field, often in unstructured, challenging, and remote conditions, the digital edge becomes a critical intervention in giving sensors the sufficient processing power to make their own decisions without needing to be constantly in touch with the central server.
The result not only tackles latency and drives cost savings, but enables the operation to respond to mission critical decisions when there isn’t time to send data to the data warehouse and start the involved process of cleaning it up from its raw state.
Not surprisingly, the case for the digital edge is compelling, but implementation still comes with a degree of complexity and potential pitfalls that can be prohibitive to those trying to capitalize on the opportunity.
In short, it’s not just about getting data to the brink and expecting the magic to happen. Applications in this space can still behave badly and as such demand careful management, maintenance, and upgrades, which is why the ability to monitor application performance with analytics as close to the apps as possible becomes a critical factor for peace of mind.
Yet analytics cannot always be easily handled by IT infrastructure at the edge or indeed those without the specialist data science skills. The nature of the IoT space with the pace and diversity of innovation and applications has inevitably spawned a new breed of developer more likely to create apps directly on the device, without the level of expertise that professional developers have.
Without these capabilities and specific skills, the choice of technology becomes paramount if IoT intelligence is to be augmented and the edge of digital businesses expanded for organizations. One solution is to take a two pronged attack which can both tackle the need for smarter connectivity while delivering real-time insights in a more accessible and easy-to-use way.
It’s an approach exemplified by the new breed of design bots and translytical databases as coined by research group Forrester and characterized by their lightning speed performance for both transactions and analytics.
The bots take an open-source approach to enable developers to build and deploy integration and data processing directly onto connected devices, bringing a surge of processing power to even the smallest connected smart devices. This in turn allows more agile applications to be built, which can be processed locally with smart devices, addressing the need to be connected all the time.
The hyper-fast database addresses the matter of storage and understanding of the complex relationships that have become ever more ubiquitous as part of our hyperconnected world. The end result promises a transformative effect on enterprise architects’ ability to deliver analytical insights at lightning speed, to be stored in the cloud for analysis and faster insights—the very cornerstone of digital transformation.
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