Data integration in the Internet of Things (IoT) ecosystem

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The potential of the Internet of things (IoT) technology is known to businesses, therefore ramping up their efforts to implement and expand their IoT networks. According to Gartner Data & Analytics Summit 2019 report, by 2024, 50% of enterprise application data will be sourced from IoT.

IoT is changing the way data is collected and processed. It promises business process improvements and delivers strategic outcomes across numerous industries by aggregating and integrating enormous volumes of data, says Yash Mehta, an IoT and big data science specialist

The caveat in unraveling the potential of IoT deals with Data Integration because the diversity of devices, communication networks, and protocols involved in data collection present critical challenges for data processing and analyses. In the IoT ecosystem, the Data integration step is crucial to collect and connect data from various IoT devices or operational systems to the required destination for immediate and contextual information. Thus, it is logical to say that IoT is all about data. The raw data generated by the devices have to be gathered and processed for analysis.

The ability of IoT technology to offer remote control and visibility over business processes, but with the increasing volume of data and diversity of data sources, the integration or collation of the data generated by the different sources becomes crucial to create a successful IoT ecosystem.

Data integration is necessary for businesses to make short or long-term decisions that drive the daily operations. The data collected from different business sources or through the network of sensory devices form a single body of information that can enable businesses to strategise with more certainty and effectiveness, proactively resolve problems, and perform predictive maintenance. For instance, a business can utilise the data from IoT sensors to perform predictive maintenance on equipment to improve or eliminate the most recurring cause of breakdowns. 

The increase in the number of complex data centre environments, where multiple systems or devices communicate and produce large volumes of data, make it harder for enterprises to keep track of all the data flowing in from different directions.

Additionally, data gathered from varied sources is received in different formats and sometimes found redundant pertaining to the importance and requirement of data cleaning and structuring. The maintenance of consistent results gathered from the IoT device also becomes a challenge when devices used in an IoT network are provided by different vendors. This leads to incompatibility and security issues.

Addressing the IoT data integration challenges

Businesses must proactively leverage right data integration strategy and practices to ensure that it helps in consequent steps such as making communication requirements, i.e., the modes of communication required between different devices and sections of the IoT network. To develop a successful data integration strategy, it is necessary to understand the scope of IoT projects, its implications, the expected challenges and opportunities, and potential solutions. 

Businesses can leverage IoT platforms that unify the network-wide operations on a single platform. There are numerous data integration platforms like K2View, Informatica, Hevo, etc., that offer solutions for large-scale IoT implementation. 

The data integration platforms

K2View Data Integration is a platform that manages data from disparate sources in any technology or format and models the data fields for business entities (e.g., customer, location, device, product). The Data Product Platform offers businesses a real-time, trusted, and holistic view of any business entity. Customer Data Hub provides meaningful customer insights available across all sources to help enterprises to deliver a consistent, personalised, and delightful experience across the customer journey.

Yash Mehta

Informatica offers a simple cloud data integration platform that integrates data on-premises or across multiple clouds and processes the data within the enterprise cloud ecosystem. The Intelligent Data Management Cloud is an AI-powered data platform dedicated to managing the complexity of different types of data formats and handling workload across any location.

Hevo provides pre-built integrations for real-time customer data with popular data sources and warehouses. The platform collects data from any source and further analyses data across various data formats to sync it with the desired data warehouse to any business application.

Businesses also leverage Application Programming Interfaces (API) for IoT communication between software-driven devices in an IoT network. It is an effective solution to enable device-to-device communication and overall network and data integration. The use of API helps in mitigating any discrepancies in the data quality as it transitions from endpoints or sources to the data centres. Thus, APIs can function as the primary tool for data integration in an IoT network.

Conclusion

The velocity at which data is being collected and processed from various sources or devices is increasing, businesses need to sync the different software platforms for total system integration. Thus, leveraging the IoT data integration platform helps to standardise how data is identified, analysed, represented, and integrated structured and unstructured data. The various platforms ensure that the highest level of security and integrity is maintained at scale. Thus, the use of such IoT data integration platforms eliminates the cost and complexity of managing different document types, data formats, protocols, or creating and syndicating integrations for system-to-system and application-to-application scenarios.

The author is Yash Mehta, an IoT and big data science specialist.

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