Digital Transformation with Industrial Data Management Technology
Your Frequently Asked Questions Answered
Organisations are increasingly looking for ways to harness the power of cloud, analytics and automation; it’s essential to ground these initiatives in tangible business needs. This blog offers practical guidance for navigating the evolving landscape of Industrial Data Management, MES integration and cloud-based strategies.
Through focused questions and actionable answers, it explores how to align digital efforts with organizational priorities, build collaborative teams, and leverage modern technologies securely and effectively.
Question
Our organization operates several small sites equipped only with SCADA systems. For our digital transformation initiatives related to these sites, where should we begin?
Answer
The best starting point is to align with your business priorities. While pursuing a digital strategy may seem inevitable and strategic in its own right, we recommend a more focused approach. Begin by identifying your business objectives and then design a digital architecture to support them.
Avoid the pitfall of creating digital initiatives that search for problems to solve. Typically, organizations already face enough challenges, so let your priorities guide you.
For instance, if your sites lack visibility or performance management tools needed to guide decision-making and operational expenditures, that should become a priority for shaping your OT/IT strategy.
From there, take an iterative approach. Avoid aiming for a “big bang” solution. Instead, start with small, successful initiatives and build upon them gradually. This method allows for scalable growth aligned with your core business needs.
Question
Security is a critical concern for us when considering cloud-based Industrial Data applications. Do cloud vendors’ security practices add additional risks compared to on-premise solutions?
Answer
Evaluating security objectively is challenging due to limited data samples and varying contexts. When using a cloud provider, you are essentially entrusting them with your security, much like organizations using Office 365 delegate email security to Microsoft.
If your current on-prem infrastructure is completely isolated from the outside world, it may be highly secure but makes digital transformation difficult. In contrast, cloud providers, whose reputations depend on security, invest heavily in safeguarding their systems. Comparing their capabilities with your internal expertise is essential.
Currently, most cloud-based implementations based on Industrial data involve one-way data flows, reducing risks like production or process interruptions. While architectures enabling closed-loop control exist, they are not yet widely adopted.
The concept of OT data lakes is a powerful shift in the industrial world, blending the strengths of Information Technology (IT) with Operational Technology (OT) , they leverage IT-style tools (cloud, big data, AI/ML, analytics) to unlock deeper insights and scale industrial intelligence on the OT side.
Ultimately, security risks must be assessed on a case-by-case basis. Compare the cloud’s potential vulnerabilities with your existing setup and consider how it aligns with your digital transformation goals.
Question
What advice do you have for organizing a cloud-based Industrial Data Management initiative? Which organizational roles are key, both new and existing?
Answer
Successful projects are grounded in genuine business needs. Start by identifying the business representatives who have a vested interest in the digital initiative. For example, if corporate emissions reporting is a priority, sustainability teams should lead the effort with the help from both the OT and IT organizations.
However, these representatives cannot succeed alone. Collaboration with a dedicated digital team is essential. Many organizations now centralize digital expertise in specialized groups focused on architecture, cloud systems, and implementation.
In cases involving complex analytics, consider involving a data science team. Additionally, your IT and OT departments should support the transition by transferring data from legacy systems to the new architecture.
Above all, align digital initiatives with business priorities. Digital transformation cannot succeed in isolation—it must integrate with core business functions. History shows that siloed digital efforts often fail, so collaboration and integration are critical to success.
Question
Do you think major cloud providers like AWS, Azure, and Google Cloud are competing with traditional MES vendors? What do they bring to the table?
Answer
Major cloud providers, such as AWS, Azure, and Google Cloud, have broad strategies that extend beyond specific industries. Their interest in real-time data reflects its growing relevance across various sectors, from process industries to intelligent vehicles, finance, and smart buildings.
While it’s unlikely that providers like Microsoft will directly compete with specialized applications, such as Aspen’s planning and scheduling tools, overlaps are emerging in basic functionalities. These include trending, historical data analysis, event management, and alerting—features that were traditionally part of MES/MOM but are now becoming more generic.
This overlap is where we might see competition. However, deep competition within niche vertical applications remains less likely. The primary impact will be in areas where general-purpose real-time capabilities meet traditional MOM functions.
Question
A specialty chemicals manufacturer seeks to reduce batch variability and enhance product quality by integrating Manufacturing Execution Systems (MES) with automation. What would your recommendations be for the next steps?
Answer
The implementation of an automated defect detection system within an MES/MOM framework typically follows a phased approach. In the initial 3-6 months, the focus is on planning and system integration. This includes selecting appropriate AI-driven/Advanced Analytics systems, configuring IoT sensors whenever needed and ensuring seamless communication between the solution and production equipment. During this phase, a pilot test is conducted on a limited production line to refine machine learning models for defect classification. Once validated, the system is scaled up across multiple lines, ensuring minimal disruption to ongoing operations.
By months 6-12, full-scale deployment is deployed, including automated alerts, machine adjustments and real-time dashboards for defect tracking. Data driven analytics are used to continuously optimize detection accuracy and improve predictive maintenance capabilities. Training sessions for operators and quality teams are conducted to enhance adoption. The aim is for the system to be fully operational, reducing manual inspections and improving overall equipment effectiveness (OEE).
Staff training and ongoing refinements, along with software updates, will ensure that, post-implementation, the system continues to improve in defect detection accuracy and process efficiency.