Consequently, a sustainable and efficient approach to the raw material data is not just a matter of economic importance, it is also a social issue – just like the sparing use of other resources.
This indiscriminate accumulation of data is becoming a real economic and social challenge. The problem is not just that many companies do not know how to deal with the seemingly unmanageable flood of data. Storing and processing all this information is also expensive, besides causing unnecessarily high energy consumption and associated carbon emissions – a waste of resources with consequences for the economy, the environment and, therefore, future generations.
Making the right decisions with the right data
In an increasingly digital world, companies will be able to achieve ever greater efficiency in processing the raw material data in pursuit of their goals. To this end, they need:
- the right mathematical, statistical, management and economic methods,
- increased automation in processing
- strategic knowledge of which data are relevant to the company now and in the future.
The goal must no longer be to simply collect the largest possible amount of data for potential future processing. Instead, it must be to single out only the most important data sources, which offer real benefits and generate economic value – both now and in the future. Besides process-related and domain-specific technical aspects, the statistical and mathematical relevance of the data is an important factor in this decision.
Data analytics is key
Distilling extensive and unstructured datasets into fewer but higher-quality data of strategic, tactical and operational relevance to companies, offering concrete long-term value and benefits, does not depend solely on improved technology or sensors for the collection, transmission and integration of data. In order for the information collected to be leveraged in concrete applications – i.e., to turn big data into smart data – it must be processed, enriched with additional relevant information (in some cases from external sources), and analyzed.
This significant field – data analytics – holds enormous potential. Advances in algorithmics in recent decades have surpassed those in the field of hardware by an order of magnitude as far as solving times are concerned. As a result, mathematical problems that would have required 75 years of processing time a little over two decades ago can now be solved in a single second.
Analytics research at the boundary of what is currently possible
Accordingly, our research seeks to continually improve analytics methods and processes in the field of artificial intelligence, pushing the boundaries of what is currently possible. As a result, appropriate filters and continually refined automated analytics methods will, in future, make it possible to analyze, optimize and act upon larger volumes of relevant data faster.
In this way, seemingly unmanageable amounts of data and related material will be transformed into manageable, high-quality data and information; in other words, the right data, with concrete economic benefits.