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Three Major IoT Data-Related Challenges and How to Address Them

IoT has formed itself a crucial component for future-oriented solutions and holds a massive potential of economic value. McKinsey & Company estimates that by 2030, IoT (Internet of Things) will enable $5.5 trillion to $12.6 trillion in value worldwide, including the value captured by consumers and customers. For proof of its growing popularity and consumers’ dependency on it, you likely don't need to look any further than your own wrist. From fitness bands to connected vehicles, smart homes, and fleet-management solutions in manufacturing and retail, IoT already connects billions of devices worldwide, with many more to come. As more IoT enabled devices come online, with increasingly sophisticated sensors, choosing the right underlying technology to make IoT solutions easier to implement and help companies seize new innovative opportunities is essential. In this blog we will discuss how MongoDB has successfully addressed three major IoT data-related challenges across various industries, including Manufacturing, Retail, Telecommunications, and Healthcare. The challenges are the following: Data Management Real-Time Analytics Supply Chain Optimization FIgure 1: MongoDB Atlas for IoT Let's dive right in! Data management Storing, transmitting, and processing the large amount of data that IoT devices produce is a significant challenge. Additionally, the data produced by IoT devices often comes in variable structures. This data must be carefully timestamped, indexed, and correlated with other data sources to make the context required for effective decision-making. This data volume and complexity combination makes it difficult to effectively and efficiently process data from IoT devices. Bosch Consider Bosch IoT Suite , a family of products and services in IoT device management, IoT data management, and IoT edge by Bosch Digital. These products and services hold over 250 international IoT projects and over 10 million connected devices. Bosch implemented MongoDB to store, manage, and analyze data in real time. MongoDB’s ability to handle structured, semi-structured, and unstructured data, and efficient data modeling with JSON make it easy to map the information model of each device to its associated document in the database. In addition, dynamic schemas support agile development methodologies and make it simple to develop apps and software. Adding new devices, sensors, and assets is easy, which means the team can focus on creating better software. ThingSpace Another example is that of ThingSpace , Verizon’s market-leading IoT connectivity management platform, which provides the network access required to deliver various IoT products and services. Verizon works with companies that purchase network access from it to connect their devices, bundled together with their own solutions, which they sell to end users. ThingSpace’s customers each sell an IoT product that needs reliable connectivity to ensure the devices always work, which WiFi cannot offer. Verizon’s monolithic RDBMS-based system would not be able to scale to handle both transactional and time-series workloads, so Verizon decided it needed a distributed database architecture going forward. MongoDB proved to be the only solution that scaled to meet Verizon’s requirements across different use cases and combinations of workload types. The immense processing needs resulting from the high number of devices and high velocity of messages coming in were only addressed by MongoDB’s highly available, scalable architecture. Native MongoDB Time Series allow for improved performance, through optimized storage with clustered indexes and optimized Time-Series query operators. MongoDB's advanced capabilities, such as flexible data modeling, powerful indexing and Time Series provide an effective solution for managing the complex and diverse data generated by IoT devices. Real-time analytics Real-time data analytics, one of the most crucial parts of big data analytics today, brings value to businesses for making more data-driven real-time decisions. However, despite its importance, very few can respond to changes in data minute by minute or second by second. Many challenges arise when it comes to the implementation of real-time analytics for enterprises. Storing such a huge volume of data and analyzing it in real time is an entirely different story. Thermo Fisher Cloud Let’s consider the Thermo Fisher Cloud, one of the largest cloud platforms for the scientific community on AWS. MS Instrument Connect allows Thermo Fisher customers to see live experiment results from any mobile device or browser. Each experiment produced millions of "rows" of data, which led to suboptimal performance with existing databases. Internal developers needed a database that could easily handle a wide variety of fast-changing data. MongoDB's expressive query language and rich secondary indexes provided the flexibility to support both ad-hoc and predefined queries customers needed for their scientific experiments. Anytime I can use a service like MongoDB Atlas, I’m going to take that so that we at Thermo Fisher can focus on what we’re good at, which is being the leader in serving science. Joseph Fluckiger, Sr. Software Architect @Thermo Fisher MongoDB Atlas scales seamlessly and is capable of ingesting enormous amounts of sensor and event data to support real-time analysis for catching any critical events or changes as they happen. That gives organizations new capabilities, including: Capturing streaming or batch data of all types without excessive data mapping Analyzing data easily and intuitively with a built-in aggregation framework Delivering data insights rapidly and at scale with ease With MongoDB organizations can optimize queries to quickly deliver results to improve operations and drive business growth. Supply chain optimization Items move through different locations in the supply chain, making it hard to maintain end-to-end visibility throughout their journey. The lack of control on any stage can dramatically harm the efficiency of planning, slow down the entire supply chain and ultimately result in lower return on investment. From optimizing warehouse space by sourcing raw materials as needed, to real-time supply chain insights, IoT-enabled supply chains can help significantly optimize these processes by eliminating blind spots and inefficiencies. Longbow Advantage Longbow Advantage delivers substantial business results by enabling clients to optimize their supply chains. Millions of shipments move through multiple warehouses every day, generating massive quantities of data throughout the day that must be analyzed for real-time visibility and reporting. Its flagship warehouse visibility platform, Rebus, combines real-time performance reporting with end-to-end warehouse visibility and intelligent labor management. Longbow needed a database solution that could process quantities of that scale and deliver real-time warehouse visibility and reporting at the heart of Rebus, and it knew it could not rely on monolithic, time-consuming spreadsheets to do so. It became clear that MongoDB’s document database model was a good match and would allow Rebus to gather, store, and build visibility into disparate data in near real time. Another key component of smart supply chain solutions is IoT-enabled mobile apps that provide real-time visibility and facilitate on-the-spot data-driven decisions. In such situations, the offline first paradigm becomes crucial, since staff need access to data in areas where connectivity is poor or nonexistent. Realm by MongoDB is a lightweight, object-oriented embedded database technology for resource constrained environments. It is an ideal solution for storing data on mobile devices. By utilizing MongoDB’s Realm SDKs, which wrap the Realm database, and Atlas Device Sync , which enables seamless data synchronization between MongoDB and Realm on your mobile phone with minimal developer efforts, businesses can rapidly develop mobile applications and drive innovation. MongoDB provides a powerful solution for IoT-enabled supply chains that can optimize processes and eliminate inefficiencies, enabling organizations to make data-driven decisions and improve supply chain efficiency. Conclusion The IoT industry is rapidly evolving, and as the number of connected devices grows, so do the challenges faced by businesses leveraging these solutions. Through a range of real-world use cases, we have seen how MongoDB has helped businesses deal with IoT data management, perform real-time analytics and optimize their supply chains, driving innovation in a variety of industries. With its unique features and capabilities, designed to manage the heavy lifting for you, MongoDB is well-positioned to continue playing a crucial role in the ongoing digital transformation of the IoT landscape. Want to learn more or get started with MongoDB? Check out our IoT resources MongoDB IoT Reference Architecture Migrate existing applications - with Relational Migrator MongoDB & IIoT ebook IoT webpage