The success of innovative pioneers like SpaceX and Uber illustrates where the journey towards digitalisation is heading. New technology has the potential to turn entire industries on their head and supplant traditional companies and their business models. This is why businesses who want to survive long-term competition have to incorporate technology and innovation management into their operations so that they can respond to market changes in time and tap into new markets and/ or products. The main components of technology management are, firstly, identifying relevant trends at an early stage and, secondly, examining them in terms of potential changes. Without automated solutions, this process takes up a considerable amount of time and analytical work. At the same time, the quantity and complexity of the information sources that need to be taken into account are increasing. Other barriers to using this data include the variety of languages, the ambiguity of data, the cost of getting licences, and also technical hurdles such as the five big data Vs: Volume, Variety, Velocity, Validity and Value.
Lots of businesses tackle these challenges with the preventive approach of creating data-intensive silos or through the use of classic machine learning processes, which in many cases have proven to be expensive and not particularly sustainable dead ends. One of the main reasons why these approaches fail is that data is often detached from its original source and then collected and processed without a structured, uniform understanding of its content. As such, not even the best text analysis procedures are able to reliably determine whether the word “Apple” in a text is referring to the company or the fruit. Knowledge graphs enable conceptual properties to be learnt by putting the focus on the relationships between terms. Companies, technologies, points of interest and even people are depicted as nodes within a harmonised graph, which represents the realworld links between any such things. The results of refining data in this way aids the understanding of the meaning – in other words, the semantics – of any constructs within our world and provides us with a basis of data for recognising technology and market changes.
Initially launched as a joint research project, an innovative pilot application has been developed as part of the long-standing working relationship between Nuremberg Institute of Technology, the Fraunhofer Center for Applied Research on Supply Chain Services SCS (in Nuremberg) and Trivadis. This application enables the development of markets, technology and industries all over the world to be analysed regardless of their language or domain. The application was able to provide helpful information on the following typical issues:
To answer these questions, unstructured information sources, such as news feeds and other publicly available text documents like publications, patents or articles, are automatically recorded and mapped onto a knowledge graph as the central core of data using modern AI-supported procedures. This central core of data represents the knowledge from the collected data sources presented in a structured, fact-based manner. The success of the pilot project was able to demonstrate that using knowledge graphs for the intelligent consolidation of “data” as a raw material can be a key competitive factor.
Following our guiding principle of “Turning Data into Business”, Trivadis has worked with its partners to develop a Cloud-based platform and several trend analysis tools. Thanks to this solution, multiple SMEs are already equipped with a systematic trend radar for their specific areas of work and are able to integrate the results into businessrelevant decision-making processes. The ongoing research-oriented cooperation with the Fraunhofer Institute and Nuremberg Tech combines over 25 years of experience Trivadis has as a data expert and not only enables the team to “trial” innovative cutting-edge PoCs for trend monitoring but also allows them to implement sustainable solutions and operate them over the long term. The diagram on the previous page uses a simplified model to illustrate the path from a data source to fact-based knowledge, and ultimately to valuable analysis and reporting tools for day-to-day work.
Knowledge Graphs help to understand the meaning of any constructs in our world and serve as a data basis for identifying relevant technologies and market changes.
Dr. Roland Zimmermann, Professor for Information Systems and Statistics, Nuremberg Institute of Technology
The exciting challenges presented by a new upcoming key technology motivated Trivadis and its partners to set aside traditional ways of thinking about data management and apply their shared expertise in new areas. The core points include:
In Nuremberg and Bamberg, the Fraunhofer Working Group for Supply Chain Services SCS designs data rooms for networked overall systems and rapidly deployable IoT prototypes, develops state-of-the-art data analytics methods in concrete applications and provides support in the realisation of digital transformation. It combines economic methods and technological solutions with mathematical procedures and models. The Future Engineering research group, which belongs to the SCS, combines methods of machine processing of natural language with methods of market, trend and scenario research.