Boost IoT data with state-of-the-art, enterprise-grade storage
Khalid Wani - a young achiever with over a decade of work experience in technology distribution & international sales, Khalid joined Western Digital in 2008. Khalid has earned several recognitions for his incisive business insights & outcomes. Channel Middle East positioned him as the first runner up among the top 10 Channel Executives for the year2010 in the Middle East.
India has undergone an accelerated pace of digital transformation, especially in the recent past when the world was confined into their homes during the pandemic. The world has pivoted to digital with connected digital solutions and technologies, drawing exceptional digital experiences for consumers across the spectrum.
This unprecedented transformation to digital can be attributed to the meteoric rise of emerging technologies like IoT, AI/ML, etc. From smart cities, autonomous vehicles, to automated supply chains, the ecosystem of these technologies are experiencing large-scale expansion with data at the focal point. By 2025, it is estimated thatconnected IoT devices will generate up to 73.1 zettabytes of data.
The world produces heaps of data every second making it ubiquitous. Therefore there is always an urgent need for it to be secure, safely stored, analyzed, and transformed to generate actionable insights. Given the extensive nature of data transformation, it is imperative to have a comprehensive data architecture that can support the demands of a wide range of uses throughout the data journey.
Through the entire value chain of consumer’s technology consumption, there are scores of data generated - from smart devices, wearables, and connected machines. The main objective at this stage of data transmission is to mitigate network latencies and increase overall throughput between the layers (Cloud-to-endpoint and vice versa) for data-intensive purposes.
What is the value of your IoT data?
Today’s data infrastructures need to go far beyond the function of simply capturing data and storing it to being able to facilitate complex data transformation. Here are a few examples:
Autonomous Vehicles-These vehicles are packed with sensors, cameras, LIDAR, radar, and other gadgets that are expected to generate 2 terabytes of data per day. Using technologies like 3D mapping, advanced driver assistance systems (ADAS), over-the-air (OTA) updates, and vehicle-to-everything (V2X) connectivity enable users to leverage the feature of real-time driving decisions. Real-time decision-making is vital for passenger safety. Data infrastructures must reduce latency while enabling heavy throughput to execute critical tasks like predictive maintenance.
Medical wearables-Predications say that by 2021, worldwide end-user spending on wearable devices is expected to soar to a total of $81.5 billion. These wearable devices extract vital data that tracks biological mechanisms like sleep patterns, nutrition, blood oxygen levels and measure daily movements. This IoT-based data can be then changed in terms of daily, monthly, and yearly statistics and trends to make more informed decisions about one’s healthcare regimens. In such a use case, the storage priority will be centered around long-term data retention for maintaining critical health records.
Search and rescue drones–Drones are often used to operate in harsh environmental conditions like extreme temperatures and varying weather patterns. Hence, it is pertinent that storage solutions are highly resilient and durable in such technologies. For example, high endurance and highly reliable industrial-grade products like e.MMC and UFS embedded flash drives could be ideal for such a use condition.
Smart cities–For smart cities to be truly feasible, they require huge amounts of real-time and archived data to be stored. IoT technologies rely on storage at the edge and endpoints to leverage real-time data.
For example, smart public transport systems require real-time data on traffic, to accurately and swiftly accommodate spikes in demand, such as rush-hour traffic. This means that this application requires data storage similar to smart cars that allow low network latencies.
The storage for archival data, in comparison, requires less focus on rapid real-time transfer and more emphasis on long-term retention. Here, the cloud could be a potential storage solution.
General-purpose to purpose-built architecture
The use cases of connected technologies are ever-expanding and the best way to leverage such technologies in the digital era is to have a robust data storage strategy. For instance, NVMe storage solutions are perfect for use cases that require low latency and very high performance in their data journey. Therefore, it is necessary to have specialized storage to maximize the value gained from IoT data and this must be considered which developing the data infrastructure.
Many organizations still use general-purpose data architecture to manage their IoT-based data. Such a rudimentary architecture does not meet the demands of modern IoT applications and workloads for enterprises and consumers at large. Different use cases require different storage strategies.
Hence, there must be a pivot from general-purpose storage to a more purpose-built data storage.
While considering different criteria for data architecture, the goal is to maximize the value derived from data. For real-time IoT based use cases, your storage strategy must be designed specifically for IoT and must address the following aspects of developing a data structure:
1. Accessibility: Does it have good connectivity, serviceability, and maintenance?
2. Wear endurance: Are the workloads READ-intensive or WRITE-intensive?
3. Storage requirements: Nature of the data
4. Environment: Altitude, humidity, temperature, and vibration levels of the environment in which data will be stored?
Specialisation for optimisation
To take the best advantage of the ever-evolving IoT data landscape, one must use specialized storage strategies and solutions that drive actionable business outcomes. Since the requirements for different IoT applications vary quite drastically, it is not enough to rely on the standard, “one fit for all” ideal while seeking storage solutions.
Deploying personalized data storage solutions while being innovative with their data management strategies will enable businesses and enterprises to navigate the IoT landscape better. Being able to extract maximum value from data empowers organizations to stay ahead of the curve in the digital era.
India has undergone an accelerated pace of digital transformation, especially in the recent past when the world was confined into their homes during the pandemic. The world has pivoted to digital with connected digital solutions and technologies, drawing exceptional digital experiences for consumers across the spectrum.
This unprecedented transformation to digital can be attributed to the meteoric rise of emerging technologies like IoT, AI/ML, etc. From smart cities, autonomous vehicles, to automated supply chains, the ecosystem of these technologies are experiencing large-scale expansion with data at the focal point. By 2025, it is estimated thatconnected IoT devices will generate up to 73.1 zettabytes of data.
The world produces heaps of data every second making it ubiquitous. Therefore there is always an urgent need for it to be secure, safely stored, analyzed, and transformed to generate actionable insights. Given the extensive nature of data transformation, it is imperative to have a comprehensive data architecture that can support the demands of a wide range of uses throughout the data journey.
Through the entire value chain of consumer’s technology consumption, there are scores of data generated - from smart devices, wearables, and connected machines. The main objective at this stage of data transmission is to mitigate network latencies and increase overall throughput between the layers (Cloud-to-endpoint and vice versa) for data-intensive purposes.
What is the value of your IoT data?
Today’s data infrastructures need to go far beyond the function of simply capturing data and storing it to being able to facilitate complex data transformation. Here are a few examples:
Autonomous Vehicles-These vehicles are packed with sensors, cameras, LIDAR, radar, and other gadgets that are expected to generate 2 terabytes of data per day. Using technologies like 3D mapping, advanced driver assistance systems (ADAS), over-the-air (OTA) updates, and vehicle-to-everything (V2X) connectivity enable users to leverage the feature of real-time driving decisions. Real-time decision-making is vital for passenger safety. Data infrastructures must reduce latency while enabling heavy throughput to execute critical tasks like predictive maintenance.
Medical wearables-Predications say that by 2021, worldwide end-user spending on wearable devices is expected to soar to a total of $81.5 billion. These wearable devices extract vital data that tracks biological mechanisms like sleep patterns, nutrition, blood oxygen levels and measure daily movements. This IoT-based data can be then changed in terms of daily, monthly, and yearly statistics and trends to make more informed decisions about one’s healthcare regimens. In such a use case, the storage priority will be centered around long-term data retention for maintaining critical health records.
Search and rescue drones–Drones are often used to operate in harsh environmental conditions like extreme temperatures and varying weather patterns. Hence, it is pertinent that storage solutions are highly resilient and durable in such technologies. For example, high endurance and highly reliable industrial-grade products like e.MMC and UFS embedded flash drives could be ideal for such a use condition.
Smart cities–For smart cities to be truly feasible, they require huge amounts of real-time and archived data to be stored. IoT technologies rely on storage at the edge and endpoints to leverage real-time data.
For example, smart public transport systems require real-time data on traffic, to accurately and swiftly accommodate spikes in demand, such as rush-hour traffic. This means that this application requires data storage similar to smart cars that allow low network latencies.
The storage for archival data, in comparison, requires less focus on rapid real-time transfer and more emphasis on long-term retention. Here, the cloud could be a potential storage solution.
General-purpose to purpose-built architecture
The use cases of connected technologies are ever-expanding and the best way to leverage such technologies in the digital era is to have a robust data storage strategy. For instance, NVMe storage solutions are perfect for use cases that require low latency and very high performance in their data journey. Therefore, it is necessary to have specialized storage to maximize the value gained from IoT data and this must be considered which developing the data infrastructure.
Many organizations still use general-purpose data architecture to manage their IoT-based data. Such a rudimentary architecture does not meet the demands of modern IoT applications and workloads for enterprises and consumers at large. Different use cases require different storage strategies.
Hence, there must be a pivot from general-purpose storage to a more purpose-built data storage.
While considering different criteria for data architecture, the goal is to maximize the value derived from data. For real-time IoT based use cases, your storage strategy must be designed specifically for IoT and must address the following aspects of developing a data structure:
1. Accessibility: Does it have good connectivity, serviceability, and maintenance?
2. Wear endurance: Are the workloads READ-intensive or WRITE-intensive?
3. Storage requirements: Nature of the data
4. Environment: Altitude, humidity, temperature, and vibration levels of the environment in which data will be stored?
Specialisation for optimisation
To take the best advantage of the ever-evolving IoT data landscape, one must use specialized storage strategies and solutions that drive actionable business outcomes. Since the requirements for different IoT applications vary quite drastically, it is not enough to rely on the standard, “one fit for all” ideal while seeking storage solutions.
Deploying personalized data storage solutions while being innovative with their data management strategies will enable businesses and enterprises to navigate the IoT landscape better. Being able to extract maximum value from data empowers organizations to stay ahead of the curve in the digital era.
