This post is also available in: Deutsch (German)
Smart Data stands for the detailed and structured result of an analysis of unstructured data masses (Big Data). Every smart data information is an explicit fact. These are also known as digital findings, which also allow links to other fine-grained information. In intralogistics, for example, smart data information from the control center is displayed in a comprehensible and usually graphically formatted manner.
While Big Data stands for the extraction of large amounts of data, Smart Data focuses on the challenges of generating efficient and usable datafrom huge amounts of data, which were hardly considered before. For this purpose, data specialists usually use powerful computers (including supercomputers) and algorithms, which in turn are repeatedly loaded with precisely formulated new questions. The goal of Smart Data is to decouple Big Data from ‘Big’ by placing the relevant data in an analytical context that serves the economy and the environment.
Only through an intelligent link Big Data becomes Smart Data. The use of semantic technologies is a prerequisite for this. It is true; 80 percent of the data accumulated worldwide is still stored unstructured on storage media and 95 percent of this data cannot be evaluated automatically.
Stefan Jähnichen, FZI Forschungszentrum Informatik and Head of Smart Data Accompanying Research / Digitale Technolgien ‚Von Big Data zu Smart Data – Herausforderungen für die Wirtschaft‘
Smart Data is realized, similar to Big Data, via the three criteria of the so-called ‘3-V-Definition’ (1):
- Volume – Processing very large amounts of data
- Velocity – the data is evaluated under tight time constraints
- Variety – different data types are evaluated, partly unstructured data
A fourth important criterion is called ‘Veracity’ and aims at the correctness of the information. This process is a must at the latest when information is evaluated and assessed.
Smart Data: Challenge and Outlook
The volume of data arising worldwide will have grown forty to fifty times in a few years. This corresponds to a data volume of about 40 zettabytes. This is the equivalent of forty trillion gigabytes or 4.4 trillion HD films, each two hours long. But even the estimated amount of all words ever spoken by humans would correspond to 42 zettabytes in digitized form. It is an enormous challenge for the industry to extract useful information from the pool of know-how. This is also because information from more and more areas of life merge and are available in digital form.
In the future, countless applications such as navigation or industrial routing, warehouse planning and process-related forecasts (see examples) will be characterized by massive but linked, precise and thus cumulative data. In contrast to today, they will no longer be project-related, but rather applied across projects. The Internet of Things, which allows automated and generally self-sufficient communication between manufacturing machines, products and all other objects involved (tools, spare parts and individual processes such as replenishment) along the value chain, will use smart data to create new requirements and opportunities for the industry.
Examples of Smart Data in the industry
In intralogistics, large amounts of data are generally avoided. The host, warehouse management system, material flow computer and the entire materials handling technology (including programmable logic controller) only require the information that is actually needed for the respective process. Looking at logisticsin general and from the perspective of a drone, the analysis of smart data comes to the fore especially in topics like simulation, decision support and prediction (predictive analytics). In addition, the use of smart information is already standard in vehicle fleets. Trucks in transport logistics and driverless transport vehicles as well as classic forkliftsare already remotely maintained using Smart Data: downtimes, targeted capacity utilization and anti-theft protection are just some of the features implemented.
Nowadays, large and prepared data volumes can also be used in production. For example, the data collected is immediately used for condition monitoring, i.e. checking facilities with regard to maintenance optimization and to identify optimization needs (predictive maintenance). In practice, facilities can, for example, regulate, increase or decrease energy consumption during production independently and in an environmentally friendly manner. Nowadays, wearing parts are able to report their quality condition or degree of wear to the maintenance department; if necessary, replacement and an exchange process can be initiated.
Smart Data in the consumer sector
In the consumer area, the Smart Data segment has already reached the end consumer. In conjunction with Google Now and Google Search, map services such as Google Maps show what is possible in real-time with the partially personalized information: train connections, location links, traffic jam reports, location-based weather forecasts – mobile and available at any time at the touch of a button or via voice query. Countless sensors and interfaces in smartphones and the associated OS (Android, iOS, BlackBerry (QNX system) recently ensured that the smart home and smart car sectors also attracted attention from the industry.
For more information on smart data and automation in industry, read the article: the ant algorithm.
Image source: Pixabay