Added Value through Data. Report on the Webinar with Know-Center

The Corona crisis has emphasized more than ever the importance of digitization in general and the need to be at the forefront of value creation from data. The webinar on 28th May conducted by Robert Ginthör, (CTO and Head of the Big Data Lab & Business Area Manager Data Management at Know-Center) and Wolfgang Kienreich (Director Business & Markets at Know-Center) dealt with the matters of data-driven AI, with a particular focus on use cases for data-driven value creation and the determination of success factors for companies of different sizes and occupations.

Before going to the topic of value creation form data, the webinar gave on overview of the development of AI, explaining its three forms (1. weak/narrow/applied AI; 2. strong/full AI, artificial general intelligence, respectively 3. the artificial super intelligence), and also contributing to the demystification of some widespread beliefs about it. The current development stadium is of the weak AI, characterized by the capacity of machines to imitate intelligent human behavior and to support them with simple tasks. One example is image recognition in autonomous driving, characterized by the ability to recognize a given traffic sign or speed limitation but the difficulty/impossibility to adequately interpret and react to new objects. A necessary explanation concerned “machine learning” that should be understood as a part of AI but not identical to this. The exciting thing  about machine learning is that one does not  program the machine by giving explicit instructions on what to do, but rather the machine extracts these instructions from the data it receives for learning. Different processes are employed: supervised learning, unsupervised learning, recommender systems and reinforcement learning.

Having a powerful tool at the disposition and the data, this leads to the next question: How can the added value be assessed in the company? The assessment can be made according to two criteria: 1. existing business/new business; respectively  2. doing the business with existing data or new data. Four strategies of creating added value from data emerge:

  • For existing business: optimization (existing data) and leveraging (new data)
  • For new business: exploitation (existing data) and disruption (new data).

Detailed explanations and examples have been given for each situation, which can support someone to assess the own situation and react accordingly.

Next interesting question: We have the data, we have the business, how proceed strategically and profit from data-driven chances, such as the ones below?

  •  Unknown but  presumed relationships between known and secured facts can be proven. This could be done by recognizing many weak signals in a large database, which  give hints to the presumed relationships: trends can be extracted from big data.
  • By using the data along the horizontal value chain, previous activities can be better controlled, improved or sustainably changed

Last but not the least, the webinar also offered an excellent opportunity to learn about the research history and focus at the Know-Center, the leading European Competence Center for Big Data and Data-Driven business. One of its most important industry offers concerns the data-driven AI. Around 130 researchers are currently working at the center in Graz  in close cooperation with a broad European research network and around 50 industrial partners, including Magna, Infineon and NXP. Six research groups cover the whole spectrum of data driven AI research, running from data management (where data come from), data security (IoT, Data Security, Network, Security, Blockchain and Privacy by design),  knowledge discovery (matters of data science, machine learning, text mining, sensor analytics, information retrieval and natural language processing) and knowledge visualization ; social computing (among others network analytics, behavior analytics, recommender, prediction of relationships, influencer analytics, etc.);  and data-driven business. The research at Know Center  addresses  four research fields: industry and manufacturing, insurance and finance, knowledge and law, respectively mobility and logistics.

The cooperation and support offer of Know-Center in the field of data driven AI f covers manifold areas and is oriented towards the needs of companions of all sizes. Highlights are: Data Value Check (simple and systematic procedure to identify data driven use cases in a company and avoid unpleasant surprises in implementation), trainings, support of strategy development, conception and design.
A final note on a novel product: TIMEFUSE – companies that want to test it are welcome!