Introduction: IoT is transforming how data is collected and processed. It promises to improve business processes by aggregating and integrating massive amounts of data and delivering strategic outcomes across numerous industries, says Yash Mehta, an expert in IoT and big data science.
Businesses know the potential of Internet of Things (IoT) technology, which is why they have stepped up efforts to implement and scale IoT networks. According toGartnerAccording to the Data & Analytics Summit 2019 report, by 2024, 50% of enterprise application data will come from the Internet of Things.
The Internet of Things is changing the way data is collected and processed. It promises to improve business processes by aggregating and integrating massive amounts of data and delivering strategic outcomes across numerous industries, says IoT and big data science expert Yash Mehta.
A caveat revealing the potential of the Internet of Things involves data integration, as the diversity of devices, communication networks, and protocols involved in data collection poses severe challenges to data processing and analysis. Within the IoT ecosystem, the data integration step is crucial for gathering data and connecting it from various IoT devices or operating systems to the desired destination to obtain immediate and contextual information. Therefore, it is logical to say that the IoT is entirely about data. The raw data generated by devices must be collected and processed for analysis.
IoT technology can provide remote control and visibility into business processes, but with the increasing volume of data and the diversity of data sources, the integration or harmonization of data generated from different sources becomes critical for creating a successful IoT ecosystem.
Data integration is necessary for enterprises to make short- or long-term decisions that drive daily operations. Data collected from different business sources or through a network of sensory devices forms a single body of information, enabling enterprises to formulate strategies with greater certainty and effectiveness, proactively solve problems, and perform predictive maintenance. For example, enterprises can utilize data from IoT sensors to perform predictive maintenance on equipment to improve or eliminate the most frequent causes of failure.
The increasing number of complex data center environments, where multiple systems or devices communicate and generate massive amounts of data, makes it harder for enterprises to track all the data flowing in from different directions.
Furthermore, data collected from different sources is received in various formats, and sometimes it turns out to be redundant regarding the importance and requirements of data cleaning and structuring. When the devices used in the IoT network are provided by different vendors, maintaining consistent results collected from the IoT devices also becomes a challenge. This leads to incompatibility and security issues.
Addressing IoT Data Integration Challenges
Enterprises must proactively leverage correct data integration strategies and practices to ensure that they facilitate subsequent steps, such as establishing communication requirements, which are the communication patterns required between different devices and parts of the IoT network. To develop a successful data integration strategy, it is necessary to understand the scope of the IoT project, its impact, anticipated challenges and opportunities, and potential solutions.
Companies can use IoT platforms to unify network-wide operations on a single platform. There are many data integration platforms, such asK2View，Computer Science，Hevoetc., providing solutions for large-scale IoT implementations.
Data Integration Platform
K2View Data Integration is a platform that manages data from disparate sources of any technology or format, and models data fields for business entities (such as customers, locations, devices, and products). The Data Product Platform provides enterprises with a real-time, trusted, and holistic view of any business entity. The Customer Data Hub delivers meaningful customer insights across all sources, helping enterprises deliver consistent, personalized, and delightful experiences throughout the customer journey.
Informatica offers a simple cloud data integration platform that integrates data from on-premises or across multiple clouds and processes it within an enterprise cloud ecosystem. The Intelligent Data Management Cloud is an AI-powered data platform designed to manage the complexity of different data formats and handle workloads from any location.
Hevo provides pre-built integrations for real-time customer data with popular data sources and data warehouses. The platform collects data from any source and further analyzes data in various formats to synchronize it with the desired data warehouse and any business application.
Companies also use application programming interfaces (APIs) to facilitate IoT communication between software-driven devices within IoT networks. APIs are an effective solution for enabling device-to-device communication as well as overall network and data integration. The use of APIs helps minimize any discrepancies in data quality as it is transmitted from endpoints or sources to data centers. As a result, APIs can serve as a key tool for data integration in IoT networks.
Conclusion
The speed at which data is collected and processed from various sources or devices is increasing, and businesses need to synchronize different software platforms to achieve comprehensive system integration. Therefore, utilizing an IoT data integration platform helps standardize the identification, analysis, representation, and integration of both structured and unstructured data.Various platforms ensure the highest levels of security and integrity at scale. Consequently, using such an IoT data integration platform can eliminate the costs and complexity associated with managing different document types, data formats, and protocols, as well as creating and federating integrations for system-to-system and application-to-application scenarios.