Industrial companies are becoming increasingly connected, implementing more advanced processes and more efficient machinery that generate an overwhelming amount of information. This data overload can become an obstacle, limiting efficiency and hindering growth if there isn’t proper management to translate that data into decisions.
According to Seagate’s ‘Rethink Data‘ report, 68% of the data generated by companies goes unused. This clearly shows the need for many businesses to implement an operational optimization plan.
The real advantage doesn’t lie in the quantity of data but in how it is effectively managed, analyzed, and visualized. Below, we explain the role data plays in transforming a “blind” factory into a “smart” factory.
Data Management: The Starting Point for Turning Information into Value
Data management is a fundamental process that allows industrial companies to organize, store, and use information efficiently to extract its maximum value.
In the era of Industry 4.0, data is often compared to oil for its strategic value. However, poor management can turn it into disconnected silos, significantly limiting its impact.
Data management systems such as traditional databases, data lakes, Big Data platforms, and cloud solutions are key tools for helping industrial companies overcome common challenges, such as incomplete data or lack of integration between systems.
One essential concept in data management is Big Data, which enables the processing of large volumes of information in real time, identifying patterns in production processes, and anticipating failures. However, to unlock its true potential, it’s crucial to complement it with analytics, which transforms data into clear insights for decision-making.
With solutions like TOKII, you can integrate these technologies and practices into a single platform that combines management, analytics, and advanced visualization. For example, a company can apply prescriptive analytics to adjust production lines and reduce operational costs by leveraging patterns identified through predictive analytics.
Data Analysis: Transforming Data into Information
Data analysis is the key step that turns information into actionable knowledge to optimize industrial operations.
Analytics is classified into four types: descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what to do). Each type helps understand the past and present, predict the future, and define more effective strategies.
- Descriptive Analytics: Collects and analyzes historical data to understand the behavior of industrial processes and identify patterns.
- Diagnostic Analytics: Delves into the causes of problems or outcomes to propose data-driven solutions.
- Predictive Analytics: Uses historical data to forecast future scenarios, allowing companies to anticipate errors and take preventive measures.
- Prescriptive Analytics: Combines internal and external historical data to recommend specific actions that optimize industrial processes.
To implement effective data analysis, companies need tools that efficiently collect, process, and analyze data. With TOKII, factories can integrate the capabilities of systems like SCADA, IoT platforms, and analytics software into a single solution. TOKII not only replicates physical processes virtually but also enables scenario simulations, real-time predictions, and actionable recommendations to optimize operations.
Business Intelligence (BI): From Data to Decisions
After analyzing data, the next step is to transform those insights into strategic decisions, which is where Business Intelligence (BI) comes into play.
Business Intelligence is the set of tools, technologies, and strategies that companies use to collect, process, and analyze data to make more informed decisions. It helps understand what happened, why it happened, and, most importantly, what actions to take to improve future performance.
For instance, TOKII leverages Big Data to centralize, analyze, and visualize vast amounts of information in real time, connecting data from various sources. This unlocks the ability to make data-driven decisions through digital twins.
Furthermore, its integration with immersive technologies like augmented reality (AR) and virtual reality (VR) enhances data comprehension, ensuring every decision aligns with business objectives.
Data Visualization
Data visualization is a technique that transforms complex information into intuitive graphical representations, making it easier to interpret and make informed decisions.
Our brains process visual information faster and more effectively, which explains why graphical representations have been fundamental in transmitting knowledge throughout human history.
Visuals communicate universally what words sometimes cannot, making them an indispensable tool for companies handling large volumes of data. Visualization enables immediate insights, revealing patterns, trends, and relationships that might go unnoticed in traditional data formats. In the era of Big Data, this technique has become essential for extracting value from information and guiding effective business strategies.
Types of Data Visualizations
As technology advances, visualization techniques have expanded beyond traditional graphs, encompassing everything from simple 2D representations to complex 3D models and digital twins.
- 2D visualizations are the most traditional and are used in various types of dashboards and reports. These charts allow data to be presented clearly and concisely, making it easier to understand trends, comparisons, and distributions. Common examples include bar charts, line charts, pie charts, heat maps, treemaps, and scatter plots. They are ideal for presenting static data or visual summaries of complex information.
- 3D visualizations, also known as Visual Analytics, take data representation to the next level by enabling interaction with data in three dimensions. This type of visualization is particularly useful when data has a spatial component or when a more immersive experience is desired to understand dynamic processes.
Unlike the previous point, data visualization through a digital twin like TOKII allows you to immerse yourself in data analysis. This means you can simulate and monitor your factory’s operations while viewing real-time data to facilitate decision-making.
- Digital Twins enable advanced analyses and display results directly on the machines as they operate in real time. This type of visualization not only enhances data comprehension but also boosts responsiveness to problems or opportunities.
The Role of AI and Machine Learning in Data Analysis and Interpretation
Artificial Intelligence (AI) and Machine Learning (ML) are radically transforming how industrial companies analyze and interpret their data. Their capabilities go beyond analyzing historical data; their true strength lies in identifying complex patterns, predicting future outcomes, and, most importantly, adapting to new circumstances.
At IMMERSIA, we are applying these technologies in various projects, as we’ll see in the next section. One of our most compelling solutions is the no-code ML module, designed to enable companies to apply machine learning algorithms without requiring advanced technical expertise.
Companies using our digital twin, TOKII, can create virtual environments to train and test algorithms without disrupting daily operations, optimize resource consumption, and implement preventive actions.
The combination of AI and ML with advanced data management and visualization tools not only drives operational efficiency but also transforms how factories make decisions, shifting from reactive to proactive strategies.
Examples of Industrial Digital Twins
Construction and Engineering Sector
ACCIONA sought a clearer view of the processes at their recycling plant in Palencia. Traditional monitoring methods made effective data interpretation challenging and limited their ability to optimize processes and make informed decisions.
The digital twin TOKII was implemented with a high level of detail and realistic animations to improve process visualization and understanding. Additionally, by integrating with existing systems like SCADA, they could monitor data in an immersive environment and view their plant in action in real time.
Thanks to TOKII’s implementation, ACCIONA achieved its goal of gaining a precise view of its plant operations and recycling processes, improving operational efficiency, and making faster, more informed decisions through immersive visualizations of their key KPIs. Furthermore, they experienced a significant improvement in team collaboration, enabling more agile data handling, sharing, and comprehension.
Machine Tool Sector
DIMECO identified the need for an advanced graphical representation of the HMI (Human-Machine Interface) for its manufacturing lines, as well as the ability to calculate loop size and production speed within its manufacturing processes.
To address these needs, we tailored TOKII to DIMECO’s specific requirements, customizing it to integrate an advanced visualization solution and an integrated simulator. This simulator, equipped with cutting-edge technologies such as Machine Learning and Artificial Intelligence, enables them to calculate optimal distances and the size of the hole to be drilled between machines, adapting to varying production speeds.
With the implementation of TOKII, DIMECO has optimized the configuration of its production lines through precise simulations, reducing costs and avoiding unnecessary infrastructure work. Additionally, the advanced visualizations have allowed for real-time production monitoring, enhancing both productivity and the customer experience.
Data Analysis Tools for the Industrial Sector
In the face of the challenge of managing increasingly large volumes of data generated from multiple sources, the need arises to choose the right tool. Within the ecosystem of Big Data and Business Intelligence solutions, there are specific software tools and comprehensive platforms that offer multiple features.
If you’re looking for a comprehensive platform specifically designed for the industrial sector, TOKII includes everything necessary to integrate, analyze, manage, and visualize data. Its combination of digital twins, advanced analytics, and real-time visualization makes it an ideal solution for factories seeking to optimize critical processes such as predictive maintenance or real-time resource management.
On the other hand, Tableau is a tool that excels in data visualization and the creation of interactive dashboards. However, Tableau may be less effective in environments requiring real-time analysis or deep integration with industrial systems, such as SCADA or IoT.
Similarly, Power BI is widely recognized for its ease of use, seamless integration with systems, and the broader Microsoft ecosystem. The downside is that it’s not specifically designed for the industrial sector and falls short in more advanced industrial scenarios.
Qlik stands out for its ability to integrate real-time data and perform detailed analyses in complex industrial environments. However, its steeper learning curve may require additional resources for training or consulting, which could pose a limitation for companies with smaller teams.
