Here’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.
A recent by IDC Futurescape, the need for speed is so great that by 2019, some 40% of IoT-created data will be stored, processed, analyzed, and acted upon close to, or at the edge, of a network. Use cases include , parking spaces, smart lighting and parking—almost any application with remote devices or applications that require a lot of bandwidth.
Analytics: the cornerstone of Asia’s smart cities
Particularly in Asia, the smart cities initiative is driving this particular need for speed and analytics. Singapore is en route to becoming the world’s first smart nation, while Hong Kong has already made significant steps in their own smart city, with the smart city blueprint set to be completed by the middle of the year.
With the Hong Kong government commissioning a study to map out development plans up to 2030 to create a smart city, Hong Kong has already begun testing technology for its smart city plans.
With the highest smartphone penetration rates on the world, and the sheer number of applications and devices being adopted, the success of this initiative balances on the Hong Kong government’s ability to harness insights from avalanche of data that is being generated every second. Speed is absolutely critical in ensuring that data is transformed into insights as quickly as possible.
Bringing insights to data as quickly as possible
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, 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.