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Is Your Data Ready for Industry 4.0?

Jeff Tao
TDengine

Despite its popularity, ChatGPT poses risks as the face of artificial intelligence, especially for companies that rely on real-time data for insights and analysis. Aside from biases, simplifications, and inaccuracies, its training data is limited to 2021, rendering the free version unaware of current events and trends. With no external capabilities to verify facts, relying on outdated data for infrastructure management is akin to launching a new app on a flip phone. If you wouldn't do it there, why would you build new technology on old data now? For industries like manufacturing, where real-time data insights are essential, the effectiveness of AI hinges on the quality and timeliness of the underlying data.

As leaders across Industry 4.0 contemplate, scramble, or pivot to this new era, it's important to get their data to use AI effectively before all else. Tools like ChatGPT can be counterproductive if they require constant error-fixing, but using AI can be revolutionary if you're ready.

To unlock AI's true potential, we must address the core issue: data infrastructure readiness.

Clean, Centralize and Combine

As companies make acquisitions, they inherit different sites and systems, resulting in data fragmentation and inconsistencies that pose significant challenges for centralized data management, especially when using AI. Organizations must prioritize cleaning and aligning data across systems to address these data discrepancies and ensure consistency and accuracy. By centralizing and consolidating data into a unified system, such as a data warehouse, manufacturing companies can streamline data management, facilitate efficient analysis, and avoid inconsistencies from disparate sources for improved operational efficiency.

For Industry 4.0, innovative IIoT solutions are needed to merge, automate, and process the massive volume of timestamped data that needs to be shared, centralized, and analyzed. Large companies likely have a mix of different data systems, meaning that modern systems still need to interoperate with legacy infrastructure over common protocols like MQTT and OPC; ripping and replacing existing data systems to install one uniform system is difficult or impossible for most industrial enterprises.

For more efficiency and better collaboration among key stakeholders, combining data connectors with cloud services provides a powerful tool for leveraging open systems and seamless data sharing. With the combined data, organizations can now have one source of truth, making it easier for AI integration.

Data Sharing and Governance

It is important to audit current data sharing processes and develop standardized procedures to prepare data infrastructure for AI. Data subscription allows real-time sharing without repeated queries, providing partners with only predetermined data. This avoids potentially exposing sensitive information to outside parties. Companies can securely share data by implementing access controls, monitoring usage, and working with reputable vendors.

Next, a data governance strategy establishes procedures, policies, and guidelines for integrity, quality, compliance, and seamless transformation. By defining ownership, enforcing protections, and maintaining standards, manufacturers can create a strong foundation for AI insights. This helps teams use AI efficiently instead of fixing mistakes.

Embrace Open Systems

Sharing data externally is critical for AI success, and open systems are key to providing data sharing. Open systems provide flexibility to work with different AI providers and technologies, assisting the product selection process and letting enterprises choose the solutions that are best for their particular use case.

Transitioning from closed to open or semi-open systems enables effective data sharing across stakeholders while avoiding rip-and-replace scenarios. Open systems allow seamless data sharing via APIs while ensuring security. In addition, they allow third-party products and services for data management to be implemented to leverage AI and Industry 4.0 without extensive in-house infrastructure.

Are You Ready?

In the AI era, data infrastructure readiness is more important than ever. Outdated systems and inefficient tools will hold you back from reaping the benefits of the latest technology. Now is the time to position your organization for better decision-making and more advanced analytics by embracing the transformative effects of AI. The future belongs to the AI-ready. Are you?

Jeff Tao is CEO of TDengine

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Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

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Is Your Data Ready for Industry 4.0?

Jeff Tao
TDengine

Despite its popularity, ChatGPT poses risks as the face of artificial intelligence, especially for companies that rely on real-time data for insights and analysis. Aside from biases, simplifications, and inaccuracies, its training data is limited to 2021, rendering the free version unaware of current events and trends. With no external capabilities to verify facts, relying on outdated data for infrastructure management is akin to launching a new app on a flip phone. If you wouldn't do it there, why would you build new technology on old data now? For industries like manufacturing, where real-time data insights are essential, the effectiveness of AI hinges on the quality and timeliness of the underlying data.

As leaders across Industry 4.0 contemplate, scramble, or pivot to this new era, it's important to get their data to use AI effectively before all else. Tools like ChatGPT can be counterproductive if they require constant error-fixing, but using AI can be revolutionary if you're ready.

To unlock AI's true potential, we must address the core issue: data infrastructure readiness.

Clean, Centralize and Combine

As companies make acquisitions, they inherit different sites and systems, resulting in data fragmentation and inconsistencies that pose significant challenges for centralized data management, especially when using AI. Organizations must prioritize cleaning and aligning data across systems to address these data discrepancies and ensure consistency and accuracy. By centralizing and consolidating data into a unified system, such as a data warehouse, manufacturing companies can streamline data management, facilitate efficient analysis, and avoid inconsistencies from disparate sources for improved operational efficiency.

For Industry 4.0, innovative IIoT solutions are needed to merge, automate, and process the massive volume of timestamped data that needs to be shared, centralized, and analyzed. Large companies likely have a mix of different data systems, meaning that modern systems still need to interoperate with legacy infrastructure over common protocols like MQTT and OPC; ripping and replacing existing data systems to install one uniform system is difficult or impossible for most industrial enterprises.

For more efficiency and better collaboration among key stakeholders, combining data connectors with cloud services provides a powerful tool for leveraging open systems and seamless data sharing. With the combined data, organizations can now have one source of truth, making it easier for AI integration.

Data Sharing and Governance

It is important to audit current data sharing processes and develop standardized procedures to prepare data infrastructure for AI. Data subscription allows real-time sharing without repeated queries, providing partners with only predetermined data. This avoids potentially exposing sensitive information to outside parties. Companies can securely share data by implementing access controls, monitoring usage, and working with reputable vendors.

Next, a data governance strategy establishes procedures, policies, and guidelines for integrity, quality, compliance, and seamless transformation. By defining ownership, enforcing protections, and maintaining standards, manufacturers can create a strong foundation for AI insights. This helps teams use AI efficiently instead of fixing mistakes.

Embrace Open Systems

Sharing data externally is critical for AI success, and open systems are key to providing data sharing. Open systems provide flexibility to work with different AI providers and technologies, assisting the product selection process and letting enterprises choose the solutions that are best for their particular use case.

Transitioning from closed to open or semi-open systems enables effective data sharing across stakeholders while avoiding rip-and-replace scenarios. Open systems allow seamless data sharing via APIs while ensuring security. In addition, they allow third-party products and services for data management to be implemented to leverage AI and Industry 4.0 without extensive in-house infrastructure.

Are You Ready?

In the AI era, data infrastructure readiness is more important than ever. Outdated systems and inefficient tools will hold you back from reaping the benefits of the latest technology. Now is the time to position your organization for better decision-making and more advanced analytics by embracing the transformative effects of AI. The future belongs to the AI-ready. Are you?

Jeff Tao is CEO of TDengine

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...