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I recently heard the phrase, “One second is good for a human, but an eternity for a machine.” This made me think about the profound importance data transfer rate. Not only from a philosophical point of view, but also from a practical one. Users don’t really care how far the data has to travel, just that it gets there quickly. When processing events, the speed of receiving, processing and analyzing data is almost imperceptible. Data transfer speed also affects data quality.
Data comes from everywhere. We are already living in a new era of decentralized data, powered by next-generation devices and technologies. 5G, computer vision, IoT, AI/ML, not to mention the current geopolitical trends around data privacy. The amount of data generated is huge, 90% of it is noise, but all this data still needs to be analyzed. Data is important, it’s geographically distributed, and we need to understand it.
In order for businesses to gain valuable insights from their data, they must move away from a cloud-based approach and move to a new landscape approach. I’ll also discuss the limitations of the centralized cloud and three reasons why it fails the data-driven business.
The downside of a centralized cloud
In the context of enterprises, data must meet three criteria: fast, efficient and accessible. For more and more businesses operating globally, a centralized cloud cannot meet these requirements in a cost-effective way, which brings us to our first reason.
It’s damn expensive
The cloud was designed to collect all the data in one place so that we can do something useful with it. But moving data takes time, energy, and money—time is latency, energy is bandwidth, and cost is storage, consumption, and so on. Almost 2.5 quintillion bytes of data are created every day in the world. Depending on who you ask, there may be more than 75 billion IoT devices in the world – all of which generate massive amounts of data and require real-time analysis. Apart from the largest enterprises, the rest of the world will be virtually excluded from the centralized cloud.
It cannot scale
Over the past two decades, the world has adapted to the new data-driven world by building giant data centers. And in these clouds, the database is essentially “overclocked” to work across the globe over vast distances. The hope is that the current iteration of linked distributed databases and data centers will overcome the laws of space and time and become a geo-distributed, multi-master database.
The trillion dollar question becomes this: How do you coordinate and synchronize data across multiple regions or nodes and synchronize while maintaining consistency? Without consistency guarantees, applications, devices, and users see different versions of data. This, in turn, leads to unreliable data, data corruption and loss. The level of coordination required in this centralized architecture makes scaling a herculean task. And only then can businesses even consider analyzing and understanding that data, assuming it’s not out of date by the time it’s done, which brings us to the next point.
Unbearably slow at times.
For companies that don’t depend on real-time information to make business decisions, and as long as the resources are in the same data center, in the same region, everything scales as intended. If you do not need real-time distribution or geographic distribution, you have permission to stop reading. But on a global scale, distance creates delay, and delay reduces timeliness, and the lack of timeliness means that businesses are not acting on the most recent data. In industries like IoT, fraud detection, and time-sensitive workloads, 100 milliseconds is unacceptable.
For a person, one second is good, for a machine – eternity.
Edge native is the answer
Edge native versus cloud native created for decentralization. It is designed to receive, process and analyze data closer to the place of its creation. For business processes that require real-time insights, edge computing helps businesses extract the insights they need from their data without the prohibitive costs of recording data centralization. Additionally, these edge-native databases will not require application designers and architects to rebuild or redesign their applications. Edge’s native databases enable data orchestration across multiple regions without requiring specialized knowledge to create these databases.
The value of data to business
Data falls in value if no action is taken. When you look at the data and move it into a centralized cloud model, it’s not hard to see the contradiction. Data becomes less valuable from the moment it’s transferred and stored, it loses much-needed context due to movement, it can’t be changed as quickly due to all the movement from source to center, and by the time you finally interact with it, it’s in the queue there is already new data.
The edge is an exciting space for new ideas and breakthrough business models. And inevitably, every on-premise system vendor will claim to be on the cutting edge and build more data centers and produce more PowerPoint slides about “Now Serving Edge!” – but it doesn’t work like that. Sure, you can put together a centralized cloud to make quick decisions about data, but that will come at prohibitively high costs in terms of recording, storage, and expertise. It’s only a matter of time before global data-driven companies can’t afford the cloud.
The global economy requires a new cloud—a distributed one, not a centralized one. The cloud approaches of yesteryear that worked well in centralized architectures are now a hindrance to the data-driven global business. In a world of dispersion and decentralization, companies must look to the edge.
Chetan Venkatesh is the Co-Founder and CEO Macrame.
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