Cloud-infused scalability into business
In the last two decades, rapid inventions in the telecommunications industry resulted in increased speed, bandwidth, and penetration. This has led to the internet becoming a bridge between customers and businesses, disrupting traditional models and increasing the efficiency of the survivors. Digital Inclusion is all about connecting billions of people globally through the internet. Software intelligence and telecom inventions led to a profound effect called the ‘Asset-light Model’. From the 90s, organizations have been planning to exploit globalization and achieve topline growth. In scaling up, firms face serious bottlenecks in buying and managing the assets.
Spotting efficiency issues in the cloud model
From there on, organizations started leasing or outsourcing assets, so the entire capex burden transformed into periodic variable costs. This solved the problem of scale-up and asset upgradation. In the technology industry, ‘Cloud computing’ is a classic case of ‘Asset-light Model’. I expect an increase in IT spend on cloud infrastructure from $419m in 2019 to $909m in 2023, CAGR of 21%. Cloud was possible with a wider telecom bandwidth as huge amounts of data need to traverse between multiple communication nodes globally. However, there is an inherent problem with cloud – companies need to accept the additional bandwidth costs. Though the network speed and coverage increased drastically, there is some latency built into the system owing to cloud storage.
Introducing cutting ‘edge’ computing technology
To overcome the operational difficulties of cloud computing, the processing or storage capability needs to be moved from centralized to decentralized topology, leading to the rise of Internet of Things (IoT). As a few million devices are going to get added to the internet ecosystem, I expect data processing at origination becoming a new normal. The distributed computing and storage at edges of the network is called ‘Edge computing’, where data collection and computation happens close to the origination place and time in a decentralized manner. Tesla deployed edge computing in its cars – Machine learning algorithms run at the central cloud, passing outcomes to entire fleet; Cars equipped with processing capability will make decisions with the live data feed.
Edge computing leads to cost optimization– a two-pronged advantage. Processing capability shall be trimmed and data flow traverses in multiple directions, decreasing bandwidth needs. The inherent latency in the cloud, while responding to customers, is reduced and the distributed storage capacity reduces the risks of concentrated cyberthreats at scale.
What do marketers gain from ‘edge computing’?
Edge computing extends the usability of data to unimaginable dimensions. By fragmenting the analytics process, the central cloud shall hold the core algorithms to process one-level preprocessed information from the edge networks. The first processing happens at the level of consumer devices or aggregation of devices. Rather than pipelining the crude data collected from customers, the network passes on usable information to the central cloud. In the absence of decentralized edge processing, the cloud would pick up only fragmented customer data leading to loss of key insights from real-time data. For instance, when a banking customer initiates payment for a biller and if the payment process breaks down, the customer can log into the application again and continue the payment from a quick link retrieving the data. This highlights two important outcomes:
- Customer engagement is initiated right at data generation
- Personalized content curation is managed locally
From an operational perspective, tracking the customer journey across multiple channels is tedious. By locating edges across the network, journey details can be stored and retrieved from the edges on time, without losing any information. For customer experience (CX), the ulterior goal is to get customers hooked live in the brand’s digital ecosystem. With edge computing, data processing centers get closer to the customers, resulting in faster and engaging CX. If a wealth management client searches for private assets in the mobile app, edge processes this information and passes it to another edge, which can connect this client to the right wealth advisor in seconds, along with sharing the client’s profile with the advisor. Marketing teams will get relieved from operational aspects of data management. For marketers, edge computing helps in discovering the true sense of real-time data analytics. Businesses will identify data-based outcomes that resonate well with customers.
From a marketing perspective, edge computing creates value for businesses through personalization, contextualization, and timeliness, thereby scoring well on CX and reducing customer attrition rate. Edge computing is a coordinated effort. For instance, AT&T partnered with cloud enterprises, helping businesses to adopt edge computing along with 5G networks. The benefits maximize when the device clusters, co-located close to the data origination, start sharing information to optimize decision making. Edge computing is a potential disruptor and will gain momentum when IoTs proliferate at higher velocity.