As the market matures and technology evolves, today in 2016 the myriad of connected "things" are every bit a part of the Internet as iPhones and Netflix. But with the 50 billion devices we expect to see connected by 2020, comes a wide array of new challenges – far beyond the expectations set when the term "IoT" was coined back in 1999.
For many, the most obvious signs of this growing market sit squarely in the consumer domain. Smart light bulbs, smart bicycle locks, smart socks, practically any consumer product has been "upgraded" to a smart device – even your kitchen sink! Yet the industrial Internet of Things has been changing our day-to-day lives far longer, and enterprises stand to be the stakeholders most impacted by this technology.
As more business and industrial applications are created, more devices are being connected, forcing IT systems to handle greater volumes of data. And more importantly, these connected systems don't have the same tolerance or understanding for tardiness their human counterparts do. Performance – no matter the number of connections, volume of data, distance to travel, or network capability – is critical, and that's the dilemma facing many enterprise architects and systems integrators.
With the number of connected devices increasing at an exponential rate over the coming years, how will businesses keep up? How can developers create IoT apps that can consume – and generate – large amounts of data efficiently? And how does enterprise IT provide a scalable and reliable integration layer that won't buckle under the load or impact backend systems?
The Cost of Moving Data, Financial and Beyond
IoT is applicable to almost any industry and business application. IoT sensors can be used to monitor and analyze supply chain pipelines, allow companies to detect inefficiencies in manufacturing, improve energy efficiency, and the list goes on and on. Each of these applications requires data to be transferred through the network – and ultimately that's not free.
The true cost of moving data can be thousands of dollars per month. As CIOs work to reduce operational costs in all business areas, developers and architects need to think about how to reduce the financial burden of data transfer. But, the cost impact doesn't stop there. A lack of data efficiency can create latency in the network and, in high enough volumes, can even create total system failure. This could kick off a perfect storm of app inefficiency that tarnishes user experience, and have huge implications for the bottom line.
Understanding Data Complexity
Businesses and developers diving into the world of IoT need to understand data complexity and how to combat inefficiency. To begin, the quantity of data that is being distributed, and that can be accessed across IoT devices and systems is one of the most significant factors in this complexity. Currently, the amount of data living in the so-called "digital universe" has grown more in the past two years than in the entire history of mankind, and is expected to continue – growing 40 percent each year.
Next, the speed at which this volume of data is generated and distributed can greatly impact the networks it's traveling on. Consumers and businesses alike have high expectations for application speed. Any lags or degradation of service can significantly hinder system performance and user experience, which, in turn, can damage a product's long-term viability. With the quantity of data increasing exponentially network capacity can't possibly keep up, meaning system and app performance is the obvious loser.
Further, the growing digital universe also brings about diversity in data structure and locations of origin that creates further complexity regarding how quickly the data can be moved. For instance, dozens of IoT sensors can be used to monitor production in a factory, thousands of sensors can be utilized to optimize oil production, and for commercial aircraft a single jet engine can generate up to 10GB of data per second. As data is coming from disparate locations, real-time efficiency is necessary to prevent slowing down the data transfer process and, in turn, the application collecting and analyzing the data.
Each of the above aspects of data complexity contributes to the greater need for data efficiency and optimization or the implications can be catastrophic, and the costs incalculable.
Real-Time Data Transfer Addresses Future Pain Points
To address these issues, developers and architects need to stop sending "everything but the kitchen sink." Implement a data efficient real-time messaging solution to reduce latency by removing redundant, duplicate data, and ensure only useful information is transferred over whatever bandwidth is available. Rather than sending every byte generated through the system, only new, relevant and up-to-date data should be pushed through in real-time. With such an intelligent approach to data distribution, it will be possible to unlock the true potential of IoT without impacting application performance or user experience.
Ross Garrett is Director Product Marketing at Push Technology.
The Latest
If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...
On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
The mobile app industry continues to grow in size, complexity, and competition. Also not slowing down? Consumer expectations are rising exponentially along with the use of mobile apps. To meet these expectations, mobile teams need to take a comprehensive, holistic approach to their app experience ...
Users have become digital hoarders, saving everything they handle, including outdated reports, duplicate files and irrelevant documents that make it difficult to find critical information, slowing down systems and productivity. In digital terms, they have simply shoved the mess off their desks and into the virtual storage bins ...
Today we could be witnessing the dawn of a new age in software development, transformed by Artificial Intelligence (AI). But is AI a gateway or a precipice? Is AI in software development transformative, just the latest helpful tool, or a bunch of hype? To help with this assessment, DEVOPSdigest invited experts across the industry to comment on how AI can support the SDLC. In this epic multi-part series to be posted over the next several weeks, DEVOPSdigest will explore the advantages and disadvantages; the current state of maturity and adoption; and how AI will impact the processes, the developers, and the future of software development ...
Half of all employees are using Shadow AI (i.e. non-company issued AI tools), according to a new report by Software AG ...
On their digital transformation journey, companies are migrating more workloads to the cloud, which can incur higher costs during the process due to the higher volume of cloud resources needed ... Here are four critical components of a cloud governance framework that can help keep cloud costs under control ...
Operational resilience is an organization's ability to predict, respond to, and prevent unplanned work to drive reliable customer experiences and protect revenue. This doesn't just apply to downtime; it also covers service degradation due to latency or other factors. But make no mistake — when things go sideways, the bottom line and the customer are impacted ...