Big Data Innovation Center

Due to the increasing collection of unstructured data (e.g. from social networks) and the high volume of data from the Internet of Things (IoT) environment with many data sources (including sensors), the challenges of managing, storing and analyzing large amounts of data, which are currently discussed under the term "big data", are growing.

The insights gained from analyzing this data help companies to better understand their customers and place products and services in the right markets more quickly and in a more targeted manner. The potential offered by the use of big data technologies is very diverse due to the increasing digital transformation in companies.

The cooperating universities Bonn-Rhein-Sieg University of Applied Sciences, Fachhochschule Dortmund and Niederrhein University of Applied Sciences have founded a cross-university Big Data Innovation Center (BDIC) in order to pool expertise in this area.

The Big Data Innovation Center uses a joint SAP BW on HANA system for:

• Research
• Teaching and
• practical projects

The "Industry 4.0" initiative will revolutionize industrial production as a whole, with digitalization becoming a key factor for the competitiveness of companies. The digital transformation in companies represents a future market with considerable growth rates.

As a result of this situation, a cooperation agreement was signed in Mönchengladbach on 26.10.2011 between
Niederrhein University of Applied Sciences, Bonn-Rhein-Sieg University of Applied Sciences and the Fachhochschule Dortmund. The aim of this cooperation was to build on previous developments and ensure a regular exchange within the SAP working groups and teaching staff at the universities.
However, many companies lack the necessary technologies to analyze the largely unstructured mass data (big data).

This motivation led to the founding of the Big Data Innovation Center (BDIC) at the cooperating universities in 2016.

The members of the Big Data Innovation Center (BDIC) developed a joint further education program, which is offered as a "Certificate of Advanced Studies". This program was developed specifically to meet the growing demand for specialists in the context of big data and was structured in a target group-oriented manner. The following CASs can be acquired:

• "Data Strategist",
• "Data Analyst" and
• "Data Architect".

You can find more information about this further education program here:

• Continuing education "Data Strategist"(Opens in a new tab) 
• Further training "Data Analyst" (Opens in a new tab) 
• Further training "Data Architect"(Opens in a new tab) 

Coordinated with this training program, a joint book "Data Science" was published in 2021.

Information about the book can be found here: Springer Verlag(Opens in a new tab) 

The BDIC sees itself as a central point of contact for small and medium-sized enterprises (SMEs) in North Rhine-Westphalia (NRW) and neighboring economic areas.
It sees itself as a neutral and independent service provider for companies and universities on the subject of big data. The long-term goal here is to develop a cross-university certificate course in "Data Science" (Master of Science, 3 semesters, part-time option) with cooperation partners that may still need to be evaluated.

The immediate goals are in the foreground:

• A step-by-step development of content
  • Development of case studies
  • Integration of standard curricula into teaching
• A certificate program
  • Development of certificate courses
  • Later integration and expansion into a study program
• Improve the organization of the BDIC
  • Strengthen communication between the three independent partner universities
  • Later, if necessary, the establishment of a formal institute with its own legal personality

1. Volume (quantity)
   • defines the enormous amount of data that is produced daily in companies, for example
   • the data volume (gigabyte, terabyte, petabyte) is the reference value here
   • According to the International Data Corporation (IDC), the volume of data doubles every 1.5 years and is expected to increase worldwide by a factor of 6 in 5 years
2. Variety
   • Data is fundamentally structured into its different data formats and data sources
   • The degrees of structuring cover the entire spectrum
     • Structured (database management systems-RDBMS, e.g. customer master data)
     • Semi-structured (e.g. e-mails)
     • Unstructured (e.g. images, audio and video data)
   • Formats are subject to constant change, partly due to the use of sensors, social networks or smart devices
   • The challenge with big data in this aspect lies in the transformation of these different formats into an automatic evaluation
3. Velocity (speed)
   • refers to the speed at which data is generated, evaluated and further processed
   • Two aspects need to be considered separately
     • Processing speed of the data
       Nowadays, processing usually takes place in a fraction of a second or in real time due to in-memory technology, for example
     • Change dynamics of the data
       This refers to the speed at which data and relationships between data, as well as their meaning, change, e.g. sensor data, financial market data or social network data
4. Veracity (accuracy)
   • refers to the required level of data quality for certain types of data, e.g. future weather data, economic data or customer purchasing decisions
   • The problem here is the unpredictability and uncertainty, but these must be accepted to a certain extent because there are no 100% cleansing methods.
     For example, electricity producers determine a percentage of electricity that must be generated from renewable energy sources. However, sun and wind cannot be predicted exactly.
     However, the use of different analytical methods and the combination and contextualization of different data from several sources, including less reliable ones, can make the prediction more precise
5. Value (added value)
   • Refers to the business value of data
   • Depending on this value, many companies have now built and filled their own data platforms and data pools and invested a lot of money in their infrastructure.

1. Technical requirements
   • Real-time analysis
   • Short response times
   • Availability
   • Scalability
   • Reduction of administration effort
   • Database replication
   • Extensive interfaces
2. Personnel requirements
   • Resources with appropriate qualifications (professional, technical)
3. Organizational requirements
   • Regulations for handling the "new" data
   • Data protection rules
4. Project-specific requirements
   • Suitable scenarios
   • Business case

• Frick, D.; Gadatsch, A.; Kaufman, J. Lankes, B.; Quix, C.; Schmidt, A.; Schmitz, U. et al: Data Science - Concepts, Experiences, Case Studies and Practice, Springer Vieweg 2021,
• Gadatsch, A.: Digitalization and Big Data: Innovation through Digitalization - An Opportunity for Restructuring Processes in Healthcare, in: Pfannstiel M. A., Kassel K., Rasche C.: Innovations and Innovation Management in Healthcare, Wiesbaden 2019
• Gadatsch, A.; Neifer, T.; Schmidt, A., Bossauer, P.: Data Science Canvas: Ein Instrument zur Operationalisierung von Daten, in: Steven, M.; Klünder, T. (eds.): Big Data. Application and utilization potential in production, Stuttgart, 2020, Kohlhammer
• Gadatsch, A.: Big data in healthcare - relevant for hospital purchasing?, in: Stachel, K.; Eltzholtz, L.: Strategisches Einkaufsmanagement im Krankenhaus Instrumente - IT-Unterstützung - best practice, pp. 123-126, Berlin, 2019 (MWV-Verlag)
• Schmitz, U.: Use of big data technologies in social media marketing, Mönchengladbacher Schriften zur wirtschaftswissenschaftlichen Praxis, 2019, annual volume, pp. 50-65
• Schmitz, U.: In-memory technology: basics, advantages and possible applications, Der Controlling-Berater (ed. Gleich, R.; Kramer, A.; Esch, M.): In-memory databases: the basis for more effective corporate management, 2018, pp. 29-42
• Schmitz, U.: In-memory technology: basics, advantages and possible applications, In-Memory-Datenbanken: Auf dem Weg zur Unternehmenssteuerung der Zukunft, (eds. Gleich, R.; Kramer, A.; Esch, M.): 2018, pp. 29-42
• Schmitz, U.: Use of in-memory technology in BI, Handbuch Business Intelligence - Potenziale, Strategien und Best Practices (ed. Lang, M.), Symposium Verlag, 2015, pp. 233-248
• Schmitz, U.: Use of in-memory technologies for decision support, Mönchengladbacher Schriften zur wirtschaftswissenschaftlichen Praxis, Jahresband 2014/15, pp. 1-16
• Schmitz, U.: Analysis of different methods for a profitability analysis of analytical information systems, Mönchengladbacher Schriften zur wirtschaftswissenschaftlichen Praxis, Jahresband, 2012/2013, pp. 157-170