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With its exponential expansion, the worldwide IIoT industry is predicted to reach $1.11 trillion by 2028. Even though IIoT has many obvious benefits, organizations are having trouble getting the most return on their investments.
The difficulties in handling the massive volume of data produced by IoT devices are one of the primary causes.
Organizations may now analyze the data that IoT systems collect with strong tools thanks to the advancements in big data solutions.
However, businesses face challenges in effectively integrating data from various systems and extracting value from them. The solution to the problems these companies are facing is seamless data integration between the Internet of Things system and other IT platforms.
Importance Of Data Integration In IoT
Enterprises must integrate their data to maximize the benefits of their IoT systems and the data they gather. Organizations can detect and reduce losses and waste by using comprehensive data integration.
It can lessen operational downtime and minimize inefficiencies in business processes. For enterprises deploying IoT, data integration increases return on investment.
IoT data integration is also essential to enhance the quality of data inside an organization. It aids companies in determining what exists and what information the company needs to gather.
It aids in creating a single source of truth and eliminates discrepancies across many platforms and systems inside an organization.
By consolidating data, executives can see every aspect of an organization's systems and procedures. Organizations can pinpoint inefficiencies and bottlenecks preventing them from achieving their objectives as their operations become more transparent.
It gives companies an overview of their operations and allows them to make long-term, data-driven decisions.
Businesses may more accurately forecast future supply and demand with improved data, and they can then modify their course of action accordingly.
Better predictive maintenance data, for example, can help firms develop more effective asset management plans and lower the costs involved. Platforms and solutions for IoT data integration enable businesses to streamline operations and create clever, data-centric strategies to accomplish their objectives.
Read more: IoT & Industrial Automation: A Match Made in Efficiency Heaven? Cost, Gain & Impact Analysis Inside!
Common Challenges Associated With IoT Data Integration
Integrating IoT data is a difficult task. Organizations face many obstacles when trying to integrate their IoT solutions with data.
A handful of data integration challenges in IoT are listed here.
Noisy Data
A multitude of sources might contribute to noisy data in an IoT system. Internet of Things platforms are vulnerable to noisy data even with strong sensors.
Inaccurate data may be fed into the systems by malfunctioning sensors, overly sensitive sensors, or algorithms that interpret signals incorrectly.
Data integration can also be challenging if outdated records are kept in several platforms or systems or comparable metrics are monitored in various formats.
Businesses find it challenging to locate a single source of truth within their systems due to these problems.
Data Silos
Organizations frequently deploy IoT devices and solutions from several providers to suit their goals. These gadgets might not work with one another and frequently employ exclusive connectivity options designed for the OEM.
Additionally, each solution can have its own data storage and software systems. To make matters more complicated, IoT systems frequently employ a variety of communication protocols.
Various teams or departments within an organization may use disparate platforms for gathering and evaluating Internet of Things data.
As a result, organizations are unable to obtain a comprehensive picture since the data is gathered and kept in silos that are isolated from one another.
Legacy Systems
Data extraction from legacy systems is challenging since they may employ antiquated protocols or languages. Businesses will find it difficult to combine modern IoT systems with legacy systems, and current IoT systems cannot support these stacks.
Company-critical old systems can be expensive to replace. They can halt company processes, even in cases where there are contemporary alternatives.
Lack Of Talent
In large organizations, the lack of talent has been largely mitigated. More specialized experts with knowledge and abilities for IoT data integration were accessible as technology advanced.
However, small businesses continue to have difficulty finding qualified candidates for their data integration projects.
How Can Businesses Better Integrate Data Within IoT Systems?
Here's how companies may improve data integration in Internet of Things systems:
Build A Comprehensive Data-Centric IoT Strategy
Comprehend your IoT needs and your organization's goals, then develop an IoT strategy. The organization's skill pool and resources should be considered, along with an audit of the data's quality and how it is gathered, managed, and stored inside the company.
Please try to comprehend the problems with the data pipeline, the system's incompatibilities, and the resources required, then develop a plan to fix them.
Replace Your Legacy Systems
Outdated or legacy systems will impede your progress. Updating or integrating these systems with your new ones will be challenging, and you will have to make security and data integrity compromises.
IoT systems are relatively new, yet the industry is evolving so quickly that some older systems and methods are no longer relevant. Retaining them will complicate your digital transformation process. You could wind up with disjointed and fragmented data sets from these systems even after investing much money and effort.
Define Data Quality Standards
Establishing guidelines for data quality will support organizations in efficiently managing their data from the point of collection.
It will lessen technological debt, guarantee system consistency, and prevent further processing later.
Work With An IoT Integrator
IoT integrators have the knowledge and abilities to combine IoT data in a useful and smooth way. They frequently have their own IoT platform and are equipped to combine data from various sources.
IoT system integrators can provide their experience in anticipating and resolving potential issues and working with various manufacturers' solutions.
IoT Device Authentication Challenges In IoT Data Integration
There are several issues with the username + password security credentials used in traditional IoT device authentication techniques that can compromise both security and usability.
The following are some of the main restrictions that must be overcome:
- Social engineering, brute force, and malware (keyloggers, trojans, etc.) can be used to steal or guess passwords.
- Reusing passwords is one of the bad management practices that result from the difficulty of remembering lengthy, complicated passwords.
- Although it adds extra protection, two-factor authentication (2FA) is susceptible to phishing and SIM-swapping attacks.
- Although it's more difficult than credentials, biometric authentication techniques like fingerprint or facial recognition can be faked or tricked.
- It can be challenging to scale traditional authentication techniques to suit the demands of sophisticated systems or large organizations.
- Conventional authentication techniques can be laborious and time-consuming, irritating users and discouraging adoption.
The usage of digital certificates as a means of device authentication is growing in popularity. They function by uniquely identifying and confirming the identification of IoT devices through a cryptographic technique and certificate authority (CA).
Thanks to this, devices may now establish secure connections without the need for user involvement. However, digital certificate deployment and distribution, key management, and lifecycle management are not without their difficulties.
They are, nevertheless, a significant step in the right direction when applied appropriately.
Additionally, new approaches to IoT device authentication that take advantage of cutting-edge technology, such as behavioral analytics, machine learning, and artificial intelligence, are being developed.
These innovative techniques can increase scalability and usefulness while bolstering security. Passwordless, continuous, and risk-based authentication are a few examples. Numerous of these actions are included in top IoT integration platforms/services.
Best Practices For Overcoming IoT Data Integration Challenges
A combination of best practices, such as strong data governance, security and privacy rules, and the application of cutting-edge technology, are needed to overcome IoT data integration difficulties.
By employing best practices to overcome IoT integration hurdles, businesses can navigate through these challenges:
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Adopt a security-first approach: Security should be a primary concern across all stages of the IoT development process.
This involves identifying potential security threats, implementing security controls, designing secure systems, and regularly monitoring and updating security measures.
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Implement multi-factor IoT device authentication: Use two or more authentication factors, such as passwords and physical devices, to confirm a user's identity when implementing multi-factor IoT device authentication.
One-time passwords, smart cards, and biometric authentication are some techniques.
Not every MFA technique is as safe as the others; security keys, for instance, work better than SMS.
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Ensure compatibility and standardization: Try to use industry-wide best practices and standardized data formats while adhering to established protocols and standards for data sharing, such as MQTT and HTTP.
To ensure interoperability and facilitate data interchange, testing and certification programs can create a common understanding and method for managing IoT data.
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Regularly update firmware and software: This is important for maintaining security, enhancing device operation, and fixing faults or vulnerabilities, yet it is frequently disregarded.
Updating can prevent operational problems, risk user privacy, and expose networks and devices to cyber threats.
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Analyze and monitor data: Anomaly detection, artificial intelligence, and machine learning algorithms can all be used to spot unusual activity and possible security risks.
Organizations continuously monitoring IoT data to limit risk can immediately identify and address possible concerns.
Conclusion
IoT data integration has numerous obstacles, but some viable solutions exist. Adopting a security-first mindset across your IoT lifespan is crucial.
Additionally, to realize that security is about creating a positive user experience as much as the procedures you implement.
As a leading IoT integration solutions provider, CISIN has developed smooth Internet of Things systems for businesses worldwide.
Our professionals have years of expertise in developing Internet of Things solutions that benefit our customers even now.
