Integrating Data and Statistics

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

Data has become the key (if not the most important) part in business strategy and decision-making. The process of extracting values from data often requires the following components 👉 Data Collection: this is the first component. As we need data, we have to collect it. In data collection, we may have certain types of data which may not be utilized at the moment. But, we should not ignore them as data always has value (and we may want these types of data sooner or later). Remember that we can store all data that we have in a cheap storage (e.g., in a form of Data Lake). On the other hand, there could be other types of data we want to have, but they are not currently available. In this case, we need to find a way to get them. 👉 Data Cleaning and Aggregation: once we have the data, we need to clean it. It's because bad data does not bring any value to us (garbage in garbage out). In some extreme cases, bad data even damages our values. In addition to cleaning the data, if the data come from multiple sources, it's important to also aggregate the data so that later we have a single source of references when we use the data. The cleaned and integrated data can be stored in a normal Database or even better in a well designed Data Warehouse (for query optimization). 👉 Data Exploration and Visualization: given the cleaned and well integrated data, it comes to the point where we want explore and visualize the data to look for data values, which can be hidden patterns, trends, clusters, etc. This is the time when we examine relationships and build hypotheses according to the data. The easiest way to fulfill this component is by leveraging reporting and visualization tools such as Power Bi, Tableau... to display different types of graphs from scatter plots, line graphs, stacked bar charts, box-plots to heat-maps, area maps, histogram, etc. 👉 Data Utilization and Value Extraction: while the results from the previous component can be manually used to support business strategy and decision-making, it's better if they can be used automatically to generate values. This is when machine learning models can be brought in. Note that as we have a wide range of models from the traditional, basic models to the advanced, deep learning ones, each of which has its own advantages and disadvantages and there could be more than one model that works for a problem, it's important to select and design a model that works best for a given use-case with certain limitations/requirements. 👉Finally, while the above components are main components to extract values from data, there are other supporting components that go along with these ones. They are Data Monitoring and Data Governance components. These components play a role to guarantee that the data are exactly as what they should be and are used according to their terms and conditions. #datacomponents #datacollection #datacleaning #dataintegration #dataexploration #datavisualization #machinelearning

How do you bring GIS, BIM, and CAD data into a single usable system? We know the real power lies beyond visualisation. We talk constantly about integrating diverse datasets to build powerful digital twins. One indispensable tool in the expert's kit is FME - Feature Manipulation Engine. Think of it as the universal transformation powerhouse for spatial data. FME shines at the critical ETL. The Extract, Transform and Load stage. 1️⃣It extracts data from hundreds of formats like Esri Geodatabases, Revit via IFC, AutoCAD, point clouds, databases or APIs. 2️⃣It transforms that data into a unifying Coordinate Reference System (CRS), simplifying complex geometries for real-time performance and mapping attributes. 3️⃣It loads the results into engine-ready formats like FBX or glTF, or platforms like Unreal Engine and Unity. Mastering data integration is fundamental for intelligent digital twins 🌍 Make FME your data conversion "Swiss Army Knife". If you find this helpful... ----------- Follow Me for #digitaltwins Links in My Profile Florian Huemer