Smart devices powered by the hyper-connected Internet of Things (IoT) are becoming increasingly prevalent and ubiquitous in our lives, and this trend is only going to continue. Every industry is looking for ways to leverage device-driven insights to improve customers' lives and the health of machines. As the number of devices continues to grow, so do the opportunities to reshape industries and society using the IoT.
However, many organizations face challenges on their IoT journey. A Cisco survey revealed that only 26% of respondents considered their IoT initiatives successful, with most reporting that these initiatives were more complex or time-consuming than anticipated. A survey by the Center for the Future of Work at the Cognitive Center found that 60% of IT executives said the IoT would add significant complexity to their IT infrastructure in areas such as networking, integration, and data analytics.
In gaining the benefits of the Internet of Things (IoT), enterprises face numerous challenges, including integrating IoT infrastructure with existing systems, understanding unfamiliar data formats and communication protocols, and implementing new technologies across the IoT continuum. Addressing these challenges requires careful planning, domain knowledge, and rigorous implementation. To ensure the success of an IoT initiative, organizations should consider the following five fundamental process and practice requirements:
1. Edge computing/analysis
Edge computing technology, projected to grow at a high rate of 40% in the Asia-Pacific region by 2023, captures and analyzes data on distributed devices located at the network edge. It includes local sensors that collect data and edge gateways that process it. Edge computing brings data analysis closer to the capture location, enabling faster responses to changing conditions. In fact, compared to cloud systems, edge processing systems can respond within milliseconds, while cloud systems may take over 100 milliseconds.
Before considering edge computing, organizations should first conduct a comprehensive assessment of device lifecycle costs during the planning phase, taking into account operational overhead such as monitoring, upgrades, and power requirements. Secondly, they need to create policies to protect devices using appropriate firewalls and hardened operating systems, and to encrypt data at rest and in transit. Finally, organizations should assess which analytics are most critical to their business and execute them at the edge for immediate action.
2. Data Ingestion and Stream Processing
Six out of ten IT executives say that collecting, storing, integrating, and analyzing real-time data from endpoint devices is a major obstacle to successful IoT implementation. Organizations should establish processes to collect data from multiple devices and sensors and transform it for use by cloud-based analytics platforms. Data extraction refers to importing and converting device telemetry data into a format usable by cloud-based IoT services. It helps normalize data into a common data model, making it easier for business applications and users to analyze. Data extraction is also particularly useful when organizations must ensure that extracted data is stored in accordance with government or industry regulations (e.g., the EU's General Data Protection Regulation or Singapore's Personal Data Protection Act).
3. Security and Equipment Management
With the rapid proliferation of IoT sensors and the ever-increasing complexity and volume of data exchange, organizations must strengthen the adoption and implementation of highly advanced security practices and procedures. As IoT implementations scale up and begin to become a mainstay of organizational day-to-day operations, the scale of investment, talent, and thought leadership around security will need to increase significantly.
Enterprises need to ensure that their IoT devices are securely configured, communicate effectively, and can be updated using accelerated and agile methods. Device management encompasses the hardware, software, and processes that ensure devices are properly registered, managed, protected, and upgraded.
Essential functionalities include device configuration, security, command distribution, operational control, remote monitoring, and troubleshooting. Even if the cloud provider doesn't offer the required device management components, organizations will still need to consider these capabilities. Comprehensive device management enables connected devices to communicate easily and securely with other devices and cloud platforms, while helping enterprises reliably scale to billions of connected devices and trillions of messages.
4. Cold Path and Advanced Analysis
Currently, large-scale processing can include loads exceeding 100,000 events per second. By employing cold path processing and storing data on a cloud platform, large amounts of data can be analyzed using advanced algorithms.
This type of analysis can uncover trends or corrective actions needed to improve business or customer experience. Unlike stream analytics (hot paths), which apply relatively simple rules to data in real time to take short-term action (detecting fraud, security vulnerabilities, or critical component failures), cold path processing involves more sophisticated big data analytics, such as machine learning and AI, which are being applied to provide deeper insights.
To extract the greatest insights from data, organizations should consider using sophisticated event processing frameworks that combine data from multiple sources, such as enterprise applications and IoT devices, to dynamically define and process analytical rules by inferring meaning from complex situations. Aggregating data before, rather than during, analysis is also important to improve processing speed. Using a data lake that stores data in its raw format can also help consolidate data and simplify access. Organizations should also consider creating dedicated data services to make it easier for users to access data on demand.
5. Integration of enterprise and business systems
IoT insights need to be delivered to enterprise systems and receive reference metadata to interpret device data. Integration with business applications and enterprise systems allows for the sharing of raw and processed data, as well as analytics-driven insights.
Through deep enterprise integration, IoT architectures can deliver advantages such as increased efficiency, reduced costs, increased sales, improved customer satisfaction, and the ability to create and lead new markets. To share data and insights, enterprises need mechanisms such as application programming interface (API) gateways, service buses, and custom connectors.
Each implementation of the Internet of Things (IoT) will differ, depending on each enterprise's requirements, expected outcomes, level of IoT and data skills, and maturity of its technological infrastructure. However, in all cases, these five requirements are essential to ensure a successful IoT implementation with minimal cost and delay. Each enterprise must conduct a rigorous needs assessment and carefully plan its roadmap to deliver a flexible, secure, and scalable IoT solution. To help guide implementation, organizations should also consider utilizing pre-built solutions, reference architectures, and blueprints offered by experienced technology service providers.
