IoT Analytics

Introduction: The IoT Opportunity 

In November 2021, McKinsey published a report titled The Internet of Things: Catching up to an accelerating opportunity. Some of the key findings from this study include:  

  • The economic value of leveraging IoT and related technologies could touch $5.5 trillion to $12.6 trillion by 2030.  
  • IoT devices hold the key for digital transformation in manufacturing, industrial and healthcare sectors. It plays a key role in bringing together the “physical” and “digital” worlds. By deploying sensors and other smart devices in the production line, business leaders are able to monitor, in real-time, key data points throughout the manufacturing lifecycle.  
  • In addition to sectors like manufacturing, oil and gas (where sensors are used to track data from difficult-to-reach places), automobile, aerospace, etc., there is immense potential to track human health with the right set of digital health trackers, which are essentially IoT-enabled devices.  

While enterprises around the world have started implementing IoT in select areas of business including production and logistics, there is still room for greater adoption. Today, most of the input data for business intelligence (BI) comes from data stored in business systems like ERP, CRM, Quality Management Systems (QMS), etc. By implementing smart devices to monitor data from “physical things” like manufacturing equipment, trucks, etc., we are able to get a more holistic view of data.  

In this blog, we highlight the role of IoT technologies in the process of digital transformation. We also talk about leveraging cloud technologies to derive maximum value from IoT data.  

#1 – Implementing IoT at Scale  

One of the biggest challenges facing IoT implementation projects revolves around scalability. First, there is a need to deploy sensors and IoT devices at various touch points. Two, once these smart devices are deployed, data from them have to captured, stored, analysed and processed.  

Let us say, for instance, there’s a manufacturing company with 70+ factories around the world. To truly leverage the potential of IoT, each of these facilities must be IoT-enabled. Once that is done, data from across all these locations must be captured for analytics. With the right set of operational reporting, analytics and business intelligence (BI) solutions, key decision-makers will garner insights from all this data. They will be able to drill-down and look at data at a single factory level; they could compare the operation effectiveness between different factories; they could check the status of equipment maintenance all over the world; and so on.  

The key questions are: What is the best way to “bring in” all this data from IoT devices into a central warehouse for data processing? Can it be combined with data from the ERP and CRM? What about data security and governance?  

By leveraging proven cloud computing capabilities of platforms like AWS, Azure, Google Cloud Platform (GCP), it becomes easier to store, manage and process IoT data at scale, with the right security frameworks in place.  

#2 – Faster and Reliable Processing of IoT Data for Analysis  

Most robust IoT system architectures include the following four stages for data processing. The first step is to capture data from a wide variety of smart devices. This is then passed through Internet gateways and data acquisition systems into a local edge computing system. From there it is moved to the cloud.  

It is critical for companies to implement an enterprise-grade software solution to manage streaming data from IoT devices. Companies like Striim offer a proven platform for IoT Data Management. The key is to have a reliable, stable and secure data streaming application to manage the flow of data from IoT devices to the BI engine.   

#3 – Flexibility and Agility – Critical Factors for IoT Analytics 

Companies keep evolving adding newer locations, additional assembly lines, new equipment and new products to manufacture. IoT implementation projects require a proven technology solutions partner who can ensure continuous improvement, keeping pace with rest of the business changes. It is critical to establish a DevOps process for constantly fine-tuning and deploying new applications and data management at scale.  

A Merit expert adds, “IoT implementation projects are extremely diverse. It requires expertise and experience across software development, application testing, data management, cloud computing and analytics. The key is to put together this proven team of experts who can plan the whole implementation holistically, keeping in mind the final business impact.”  

#4 – Staying Connected and Ensuring Security  

One of the biggest challenges with IoT data management revolves around data security. The right data streaming platform will enable in-flight data transformation with high-security. Without the right data streaming solution, it’ll become extremely difficult to run analytics on IoT data.  

It is also important to note that real-time data processing is critical when it comes to IoT data. While historic IoT data can be useful, the real value comes from knowing the “current state”. It is therefore important to stay connected at all times, and the technology solutions partner must ensure this by designing the right IoT architecture.  

Merit’s Expertise in Cloud Migration and Analytics on the Cloud for IoT Data 

Merit works with a broad range of clients and industry sectors, designing and building bespoke applications and data platforms combining software engineering, AI/ML, and data analytics. 

We migrate legacy systems with re-architecture and by refactoring them to contemporary technologies on modern cloud ecosystems. Our software engineers build resilient and scalable solutions with cloud services ranging from simple internal software systems to large-scale enterprise applications. 

Our agile approach drives every stage of the customer journey; from planning to design development and implementation, delivering impactful and cost-effective digital and data transformations. This includes our ability to build the right architecture and set of systems to manage IoT data at scale, move this to the cloud and run analytics.  

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