By Staff Writer. An insightful report on the current state of data analytics usage in industrial companies was published this week. You can download the Industrial Analytics Report 2016/17 here. This study was initiated and governed by the Digital Analytics Association e.V. Germany (DAAG), which runs a professional working group on the topic of Industrial Analytics. Research firm IoT Analytics has been selected to conduct the study and assure professional methods and standards are applied as part of the research effort. Today, we are facing a data-driven world that is changing faster than ever before, according to Knud Lasse Lueth, an IoT thought-leader as well as founder and MD at IoT Analytics, a German-based market insights firm for the Internet of Things.
“A large number of new methods, tools and technologies are finding their way into management circles, often accompanied by a variety of abstract buzzwords. For now, the world of data analytics seems to be dominated by visions rather than large-scale implementations. Reality shows that Industrial Analytics still has a long way to go before it is finally becoming that strategic and scalable business capability that it is promising to be,” Lueth said in the report published this week.Industrial Analytics (IA) describes the collection, analysis and usage of data generated in industrial operations and throughout the entire product life cycle, applicable to any company that is manufacturing and/or selling physical products. IA involves traditional methods of data capture and statistical modelling. However most of its future value will be enabled by advancements in connectivity (IoT) and improved methods for analyzing and interpreting data (Machine Learning). How analytics evolved towards automated decision-making The early analytics tools used for query and reporting were sold as “do-it-yourself” solutions for computer science experts. In the mid-1970s, several vendors began offering tools that allowed a non-programmer to delve in the world of data access and analysis. It thereby created the domain of Business Intelligence and allowed for the next level of structured analytics enabled decision-making. The role of analytics further increased through innovations in data mining methods, data warehouses, client-server systems and eventually Big Data repositories. Machine Learning and Internet of Things Today, the relevance of analytics for decision-making is gaining interest thanks to the availability of machine learning tools and the Internet of Things. Many decisions are now starting to be automated based on data and analytics, often in real-time. The report by IoT Analytics concluded that most decision-makers acknowledge the huge importance Industrial Data Analytics plays in the automation of important decisions and processes. Data-driven process automation: As more and more industrial processes and workflows become automated, intelligent data models help orchestrate actions requiring less human intervention (e.g., Real-time fault detection on products during the manufacturing process helps in automatically reducing scrap-related costs), according to the report. Machine Learning is a crucial element, especially for advanced and predictive analytics. It describes a set of techniques that extract knowledge from data so that systems can take smart and even autonomous decisions. Machine Learning in general is considered as a key technology to develop true artificial intelligence (AI), and today is a crucial element for data-driven decision-making in all kinds of businesses. With the Internet of Things gaining importance for industrial companies, understanding the specific characteristics of analytics applied to sensor data is important.Because analytics for IoT requires new approaches and different skills there is a new set of IoT Analytics experts and companies emerging and analytics companies are building up specific capabilities for handling data produced by the Internet of Things. Cheaper sensors and integrated information are however now making shop-floor entities smart agents which can process the information to take autonomous decisions. In this context, we may see smart processes and smart products that communicate within this environment and learn from their decisions, thereby improving performance over time. Manufacturing-as-a-Service Forward thinking manufacturers are considering new ways to use capacity that does not necessarily belong to them. Consider how Uber and Airbnb create value by using assets that they do not possess. The same movement may take over the manufacturing industry as it seeks to advance agility in product development and market testing. The report concluded that manufacturing-as-a-service trend requires perfect visibility into the flow of product and data in order to take momentum and is therefore highly reliant on IoT Data and the corresponding analytics. With Data Science being such an important skill for the success of Industrial Analytics projects, the report suggested that firms need to make recruiting and training for Data Science a strategic priority. For more download the report here and get more insights such as: • Results from an in-depth industry survey of 151 analytics professionals and decision-makers in industrial companies • Introductions to Industrial Analytics, its relation to the Internet of Things and Industry 4.0, how analytics has evolved over time, what Machine Learning is and what value and paradigm shifts Industrial Analytics brings to the industry • 3 prime case studies of actual Industrial Analytics projects (in the areas of energy, healthcare, and automotive) • Further insights into aspects such as how to organize for Industrial Analytics, which skills to build up and how to approach these projects ]]>