Industry 4.0 in Action: Balancing Risk, Data Flow and Operations


To ensure a smooth digital transformation, organizations should prioritize data interoperability, data authentication, and data governance as key operational considerations during their transition to Industry 4.0. highlights Phil Keys, market analyst at Intertrust.

Industry 4.0 has accelerated the digital transformation of industrial enterprises by incentivizing the introduction of the industrial internet of things (IIoT), artificial intelligence (AI), machine learning (ML), and cloud and cognitive computing into industrial ecosystems. By interconnecting cloud platforms, data centers, IoT/IIoT devices, sensors, analytics, and AI seamlessly to automate processes, companies are delivering tangible benefits such as consistent quality and flow, improved productivity, reduced downtime, streamlined processes and optimized costs. 

However, the transformation process can present significant challenges that put implementers in an uncomfortable bind, especially if mission-critical industrial systems are involved. These challenges must be addressed if enterprises are to fully realize the benefits of these technologies and best position themselves in terms of adaptability, agility and flexibility in today’s highly competitive global marketplace.

Data is the fuel that powers the digitized economy, so having comprehensive, seamless visibility and access to value-added data across operational ecosystem data can be a true differentiator. No company wants to have to reinvent the technology wheel, so in order to meet the goals of implementing and supporting new technologies, enterprises must figure out how to seamlessly graft them onto older fragmented IT systems, distributed devices, and siloed datasets.

Diffused ecosystems are inefficient, can lack consistent security and the lack of interoperability makes system integration expensive and protracted. Integrating these disparate pieces into a working system can not only take time and money, but it can also be a risky undertaking since the wrong choices can introduce security holes and slow down the process of getting actionable results from machine learning (ML) and AI algorithms.

Against a backdrop of immature standards, companies also must contend with a dizzying array of IoT and cloud-related vendors, facing the risk of overly complicated installations and vendor lock-in not only of the technology but of their data. 

Three Key Pillars for Successful Digital Transformation

There are a number of technologies, some new and some mature, that can be adopted to help streamline these systems, reduce security risks and optimize the crucial data flow for data ops systems. Here are three important areas that organizations need to successfully deploy to support smooth digital transformation: 

1. Data interoperability through virtualization 

Modern enterprise infrastructures and ecosystems are complex and often comprised of many internal and external stakeholders, for example, research and development, manufacturing, sales, third-party logistics companies, retailers, and customers. This means integrators have to contend with significant amounts of data assets found in different formats and protocols across a plethora of platforms, data layers, data warehouses, or within multiple cloud services. Such fragmented data makes executing data analysis that can yield valuable, actionable insights challenging.

To address these challenges and allow data to be unified and used together, irrespective of format and location, data interoperability is required to enable enterprises to maximize value from their data and overcome the significant challenges posed by distributed data assets. Achieving data interoperability can be achieved by deploying data virtualization software to create a ‘virtual layer’ of simplification over often complex data architectures. 

Data virtualization enables the creation of a single unified view across dispersed data sources without the need to move or physically integrate data. This helps avoid security risks associated with moving data and allows for the streamlining of data operations at the same time. Users can quickly receive answers to data queries and generate faster insights. Data virtualization allows enterprises to keep data assets where they want them and only move them around if they think they need to. 

2.The importance of authentication 

Data authentication, secure and reliable data transfer, and data protection are key elements of digital transformation. Authentication systems that an enterprise can control and deploy regardless of the technology vendor are essential for both IoT devices and software. Managed public key infrastructure (PKI) systems, which is the set of technology and processes that make up a framework of encryption to protect and authenticate digital communications, have been in the market for a couple of decades and have a good track record for authenticating both devices and software. Essentially PKI uses cryptographic public keys connected to a digital certificate, which authenticates devices or users sending digital communication. 

Enterprises can leverage PKI certificates to create clients, either developed in-house or by a third party, on a device that does several things. First, when the device sends a piece of data, the client authenticates that the device is known and good and if an update is required. If necessary, it can also encrypt that data. Importantly, it allows the user to sign that data digitally. Adding the client to the device then protects the data, no matter what networks or devices the data travels through on its way to the cloud, and can also help protect commands being sent to the device. Using a process called station-to-station protocol, when device data hits a prescribed server, the digital signature is compared to ensure everything checks out, and no data has been compromised or altered in transit.

3. Understanding data governance

Maintaining regulatory compliance is a fundamental requirement as organizations move through the Industry 4.0 digital transformation process. Data governance, which implements rules necessary for the adequate storage and processing of information, is essential to ensuring that companies meet their regulatory requirements and legal obligations.

Developing a data governance program enables organizations to put in place data access policies to ensure protection and reduce the operational risks associated with storing sensitive data. It also enables control over who is able to access and use data. Deploying data governance policies and processes to ensure data is managed effectively reduces the risk of non-compliance with regulatory and industry standards. 

Modern data governance systems, for both human and machine access control, as well as managing data rights, should be adopted by all enterprises. Making sure these systems also include robust auditing capabilities will greatly help with regulatory and business issues should they arise. Data governance fosters greater data integration, transparency, and visibility to help organizations improve their compliance activities.

See More: Why The US Must Make A Quantum Leap To Secure Sensitive Data

Efficient, Secure, Seamless

As organizations adopt Industry 4.0, which is no small undertaking, there are emerging technologies such as device authentication standards and older cryptographic standards that together can both protect and authenticate the flow of data from IoT devices to the cloud systems – and back to the devices – efficiently, securely and seamlessly. 

By adopting these technologies in their data ops platforms, data can be authenticated and governed through a single “pane of glass” system that can be deployed regardless of device vendors and that has the proper hardware to support the required security, whether it’s been updated or not.

Are you strengthening the pillars of data management discussed above? Share your thoughts with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

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