Emergence of IoT Data Orchestration to Transform the Industrial OEM Business

In today’s world, data is generated at an unrivaled rate and is the major factor behind results that are changing the future and businesses. Be it Netflix or OEMs, leveraging IoT data orchestration is how OEM leaders outdo their competitors. Comprehending data silos generated from IoT devices and vast data production is the end goal to optimize operations, improve product quality and enhance customer experience. All over the world, Netflix Studios creates around 100s of petabytes of data every year, from images to texts to larger IMFs. With this amount of data production, what lies at stake is tons of data that remain ingested and unused if not orchestrated properly. The vast amount of produced data not only increases the storage space but also becomes a bottleneck for data optimization and data-driven decisions. And data orchestration is the only way to do it! 

A Lot to Learn for OEMs When It Comes to Data

In a recent Gartner survey, 87.5% of respondents had low data and analytics maturity, falling into “basic” or “opportunistic” categories. However, OEMs increasingly recognize the importance of data in optimizing operations, improving product quality, and enhancing customer satisfaction.  

Having access to the right data and orchestrating it to make informed decisions could help OEMs reach production and operational goals and provide customer-centric solutions. While data is one of the most crucial assets for businesses and core value-creation mechanisms within smart systems and IoT, most businesses and OEMs are still immature regarding data and analytics. To make the most out of the data value creation curve, OEMs need to have a mature approach, i.e., mastering the methods of IoT data orchestration.  

Some key areas where OEMs can benefit from IoT data orchestration include: 

• Predictive Maintenance: By analyzing data from machinery and equipment, OEMs can predict failures and schedule maintenance proactively, reducing downtime and repair costs.  

• Process Optimization: Data-driven insights can help OEMs streamline manufacturing processes, reduce waste, and improve efficiency.  

• Product Development: Understanding how products perform in real-world conditions enables OEMs to design better, more reliable products.  

• Customer Insights: IoT data provides valuable information about how customers use products, helping OEMs tailor their offerings to meet market demands.  

Before we understand its depths, let us first understand IoT data orchestration. 

What is IoT Data Orchestration?  

OEMs need IoT data orchestration if they want to combat the complications of IoT applications and make them a part of their business transformation journey. It provides businesses with an end-to-end platform that has all the necessary components needed for IoT connectivity: cloud services, edge and cloud processing, global warehouse connectivity, and end-to-end security.  

With the seamless integration of these components into a single platform, IoT data orchestration helps with easier and quicker testing of IoT applications. Data orchestration leverages the ability of IoT platforms to effectively connect, process data both on the edge and in the cloud and transmit the right amount of data to the right registration system. This helps improve the IoT infrastructure, which not only saves time but also adds substantial value to business processes.   

Data orchestration platforms unify data from a network of connected IoT devices and systems and use data by acting on multiple data pieces. These platforms also enable new source integrations and transform existing data silos into big data and ETL processes. ETL is a process that refers to the extraction, transformation, and loading (ETL) of data from various sources into a centralized system. ETL plays a crucial role in IoT data orchestration by:

• Extracting data from multiple sources, such as sensors, machines, and databases.  

• Transforming the data into a usable format, including cleaning, filtering, and aggregating it.  

• Loading the processed data into a central repository, such as a data lake or cloud-based platform, where it can be accessed for analysis and reporting.  

Key Components of Effective IoT Data Orchestration  

Data collection

Collecting data from a wide range of IoT devices so they can be analyzed collectively.  

Data integration  

Integrating the collected data into a centralized system.  

Data processing and analysis  

Processing and analyzing real-time IoT data to gain insights. This improves data-driven decision-making and operational efficiency.  

Data security 

Implementing stringent security measures and compliance with data protection regulations.  

3 Steps of IoT Data Orchestration  

To adopt effective IoT data orchestration, you need to follow these steps:   

Step 1: Data Ingestion and Collection  

In the initial phase, data is gathered from IoT devices, sensors, and other sources.  

• Device Communication: Establish communication protocols with IoT devices to collect data. This can involve using various protocols such as MQTT, CoAP, or HTTP.  

• Data Acquisition: Collect raw data from devices, such as telemetry data, sensor readings, or event logs.  

• Edge Processing: Optionally, preliminary processing or filtering of data at the edge (on the device or local gateway) is performed to reduce the volume of data that needs to be transmitted to central systems.  

• Data Aggregation: Aggregate data from multiple devices or sources to prepare for further processing.  

Key Considerations:  

• Data Format and Protocol Compatibility: Ensure that data formats and communication protocols are compatible with the data ingestion system.  

• Data Volume and Frequency: Plan for the volume and frequency of data to manage data flow efficiently.  

Step 2: Data Integration and Processing  

Once data is collected, it must be integrated from various sources and processed to make it actionable.  

• Data Transformation: Transform data into a compatible format or structure suitable for analysis and integration.  

• Data Integration: Combine data from different sources into a unified repository or platform. This may involve using data integration tools or middleware to handle data from disparate systems.  

• Data Enrichment: Enhance raw data with additional context or metadata to improve its value. This can involve adding information such as location, time, or user-defined tags.  

• Data Cleaning: Remove duplicates, correct errors, and handle missing values to ensure data quality. 

Key Considerations:  

• Data Interoperability: Ensure seamless integration of data from different sources. Mckinsey estimated that interoperability is required for 40% of the potential value that the IoT can provide.  

• Scalability: Design systems that can handle increasing volumes of data and more complex processing requirements.  

Step 3: Data Analysis and Visualization  

The final step involves analyzing the processed data to extract insights and presenting it in a meaningful way.  

• Data Analytics: Apply statistical analysis, machine learning, or more analytical approaches to determine patterns, trends, and anomalies in the data.  

• Real-Time Processing: Implement real-time analytics if immediate insights or actions are required.  

• Visualization: Create dashboards, reports, or visualizations that make data interpretation easy to derive actionable insights.  

• Decision Making: Use the insights gathered from the analysis to make informed decisions or trigger automated actions based on predefined rules or models.  

Key Considerations

• User Accessibility: Ensure that data visualizations and reports are accessible to users who need them and designed to be easily understandable.  

• Actionability: Focus on deriving actionable insights to drive operational improvements or strategic decisions.  

Complexities Involved in Building and Rolling Out IIoT Applications and How Data Orchestration Simplifies It  

Building, rolling out, and scaling Industrial Internet of Things (IIoT) applications involve several complexities that span across different stages of development and deployment. Here’s a detailed breakdown:

Building IIoT Applications  

Integration with legacy systems presents a significant challenge when developing industrial IoT applications, as it requires aligning new technologies with existing ones to ensure seamless data flow. Managing and storing the large volumes of data generated necessitates an efficient data studio to maintain integrity and accessibility. With its scalability and flexibility, it should handle the growing data loads and adapt to evolving requirements. Security plays an essential role as well! It demands robust measures to secure against cyber threats and unauthorized access. OEMs can make data-driven decisions with real-time data processing, necessitating low-latency systems and high availability.  

Rolling Out IIoT Applications  

A well-planned deployment strategy helps mitigate risks and minimize disruptions to existing operations. Interoperability with other systems and devices must be ensured for smooth operation. Additionally, establishing robust support and maintenance structures is essential for ongoing troubleshooting and system upkeep.  

Scaling IIoT Applications  

Expanding infrastructure must enhance data analytics capabilities to handle larger datasets and derive actionable insights, potentially through advanced analytics and machine learning. Network management becomes increasingly important as the number of IIoT devices grows, necessitating adaptable network architecture. Compliance with industry regulations and standards can become more complex as the system scales, and managing the additional costs associated with scaling while maintaining profitability is crucial.  

Simplifying IoT with Data Orchestration  

IoT Data orchestration involves managing the flow and integration of data across various sources and systems. Simplifying IoT with data orchestration can lead to increased ROI and support product R&D efforts in several ways:

1. Centralized Data Management: Centralized data management platforms help streamline data collection, storage, and processing. This reduces the complexity of integrating disparate data sources and improves data consistency. 
2. Automated Data Integration: Automation tools can simplify data integration from various sources, reducing manual intervention and minimizing errors. This accelerates the process of turning raw data into actionable insights. 
3. Data Standardization: Implementing data standardization protocols ensures that data is uniformly formatted and compatible across different systems, facilitating smoother data exchange and integration. 
4. Advanced Analytics and AI: Leveraging advanced analytics and AI for data processing can enhance the ability to extract meaningful insights from large datasets, driving better decision-making and innovation. 
5. Scalable Cloud Solutions: Cloud-based solutions offer scalable data storage and processing capabilities. They allow flexible scaling as data volume grows, reducing the need for significant upfront infrastructure investments. 
6. Real-Time Data Processing: Implementing real-time data processing solutions helps obtain immediate insights, which are crucial for timely decision-making and operational efficiency. 
7. Enhanced Visualization and Reporting: Tools that provide advanced data visualization and reporting capabilities help interpret complex data more efficiently, facilitating better strategic planning and R&D activities. 

By addressing these aspects, organizations can simplify IoT data orchestration, improve operational efficiency, make better decisions, and achieve a higher return on investment (ROI).  

Adding Value to Your Business  

The goal of IoT data orchestration is straightforward: to ensure the right data is available for generating timely and accurate insights, enabling truly data-driven decisions that translate into tangible value and measurable results for the company. It’s particularly beneficial for organizations with multiple data systems, as it avoids the need for extensive migrations or additional storage, which can complicate data management and create new silos. 

By embracing IoT data orchestration, OEMs can transform their business operations, drive innovation, and achieve long-term success in the rapidly evolving industrial landscape.