MSc in Big Data Systems The program is focused on the value aspect of Big Data for large enterprises and the implementation of Big Data technology in the enterprise. It provides students with a knowledge and understanding of the fundamental principles and technological component of Big Data, preparing them for a career within companies or in scientific research http://www.itbusiness.ca/news/information-builders-launches-tool-for-internet- of-things/49279
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MSc in Big Data Systems
The program is focused on the value aspect of Big Data for large enterprises and the implementation of Big Data technology in the enterprise. It provides students with a knowledge and understanding of the fundamental principles and technological component of Big Data, preparing them for a career within companies or in scientific research
Schroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovative enterprises extract value from uncertain data
BIG DATA: DEFINITION
Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.http://www.gartner.com/it-glossary/big-data/
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. Big Data includes business transactions, e-mail messages, photos, surveillance videos and activity logs (machine-generated data, i.e., numerous system logs generated by the operating system and other infrastructure software in the normal course of the day, as well as Web page request and clickstream logs produced by Web servers, network management logs, telecom call detail records and so on. )http://www.pcmag.com/encyclopedia/term/62849/big-data
BIG DATA: DEFINITION
Other definitions of Big Data: http://www.opentracker.net/article/definitions-big-data
Measured in terms of volume, velocity, and variety, big data represents a major disruption in the business intelligence and data management landscape, upending fundamental notions about governance and IT delivery. With traditional solutions becoming too expensive to scale or adapt to rapidly evolving conditions, companies are scrambling to find affordable technologies that will help them store, process, and query all of their data. Innovative solutions will enable companies to extract maximum value from big data and create differentiated, more personal customer experiences. https://www.forrester.com/Big-Data
Economic/ Social Area
Environment
Maturity phase of technology
Expected Effect
Big Data implementation: important aspects
Big Data implementation: business
Fostering a Data Driven Culture. Economist Intelligence Unit.http://www.tableausoftware.com/sites/default/files/whitepapers/tableau_dataculture_130219.pdf?signin=a3841a8f840546fced0c759806b7a208
Importance of Data Analysis to the different parts of the organization(% respondents)
Social sphere: areas where Big Data analysis develops very quickly
Healthcare
Education
Services
Housing
Big Data technologies have significant influence on the sphere of science and culture
Высшая школа экономики, Москва, 2014
Оценка возможностей внедрения технологии больших данных
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Maximum positive effect of the introduction of Big Data is achieved with a strong environment, where staff are ready to use the new technology, and high values, when Big Data through specific marketing tools are an important part of the value chain.
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Environment ValueLow Medium High
Strong Compatible use Sufficient use Active, consistent and creative use
Bill Schmarzo Big Data Business Model Maturity Charthttps://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
Maturity phase of technology
Data Monetization is the level of business maturity where organizations are trying to
1. package their data (with analytic insights) for sale to other organizations
2. integrate analytics directly into their products to create “intelligent” products and/or
3. leverage actionable insights and recommendations to upscale their customer relationships and dramatically rethink their “customer experience”
Data Monetization (examples)
a smartphone app where data and insights about customer behaviors, product performance, and market trends are sold to marketers and manufacturers
companies that leverage new big data sources (sensor data, user click/selection behaviors) with advanced analytics to create “intelligent” products
companies that leverage actionable insights and recommendations to “up-level” their customer relationships and dramatically rethink their customer’s experience
Bill Schmarzo Big Data Business Model Maturity Charthttps://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
Ovens that learn how you like certain foods cooked and cooks them in that manner automatically, and also include recommendations as to other foods and cooking methods that “others like you” enjoy
Cars that learn your driving patterns and behaviors, and adjust driver controls, seats, mirrors, brake pedals, dashboard displays, etc. to match your driving style
Televisions and DVRs that learn what types of shows and movies you like, and searches across the different cable channels to find and automatically record those shows for youMapMyRun.com could
package the data from their smartphone application with audience and product insights for sale to sports apparel manufacturers, sporting goods retailers, insurance companies, and healthcare providers
Investor dashboards that assess investment goals, current income levels, and current financial portfolio to make specific asset allocation recommendations.
Bill Schmarzo Big Data Business Model Maturity Charthttps://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
In the third critical piece—substance—is where my thoughts on data science diverge from most of what has already been written on the topic. To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.
Business Informatics is the scientific discipline targeting information processes and related phenomena in their socio-economical business context, including companies, organizations, administrations and society in general
Business Informatics is a fertile ground for research with the potential for immense and tangible impact. As a field of study, it endeavors to take a systematic and analytic approach in aligning core concepts from
management science, organizational science, economics, information science, and informatics into an integrated engineering science
Program base: Business Informatics approach and research area
The program is interdisciplinary, it forms four groups of competences
Mathematics and technical knowledge and skills in area of exploration, modeling, analyzing and using the Big Data tools and techniques
The understanding of business, the connection between business and IT, the understanding, how to enable enterprise to be managed more effectively by using new Big Data technologies, value chains, produced by their implementation
Management skills in area of Big Data systems implementation, Big Data services
Research skills in area of analytics and optimization skills, focused on stochastic optimization, predictive modeling, forecasting, data mining, business analysis, marketing analytics and others
Competences and skills
Fields of work
Implementation and assessment of the efficiency of Big Data tools and technologies across the organization
Data Management: management of enterprise data
Decision Management: implementation and applying of analytic and decision support tools based on Big Data technologies , management of the decisions
Model Management: development of new models of enterprise information infrastructure based on the capabilities of the Big Data technology
Novel Models for Big Data
Data and Information Quality for Big Data
Big Data Infrastructure, Enterprise & Business transformation
Big Data Management
Big Data Search and Mining
Complex Big Data Applications in Business
Big Data Analytics
Real-life Case Studies of Value Creation through Big Data Analytics
Big Data as a Service
Experiences with Big Data Project Deployments
Research areas
Duration: 2 years, 24 months, full-time
Starts: September
Credits: 120
Language: English
Content: the program consists of core courses, option courses, course work (first year), scientific seminar and the research thesis (dissertation, second year)
MSc in Big Data Systems: key facts
Core courses
System Analysis & Organization Design
Economic and Mathematic Modeling
Enterprise Architecture Modeling
Advanced Data Analysis&Big Data for Business Intelligence
Big Data Systems Development and Implementation
MSc in Big Data Systems: content
Optional courses
More technology
Data Visualization
Predictive Modeling
Natural Language Processing
Cloud Computing
Big Data Collection, Storage&Processing in Heterogeneous
Distributed Computer Networks
Knowledge Discovery in Data at Scale Technologies
Applied Machine Learning
MSc in Big Data Systems: content
More management
Creating and Managing Enterprise Information Assets
Software: Magic Quadrant for Business Intelligence and Analytic Platforms
SCIENTIFIC COUNCIL
Dr. Diem Ho Manager of University Relations for IBM Europe, Middle East and Africa (EMEA)
Dr. Jorg Becker Vice-Rector for strategic planning and quality assurance of University of Münster, Germany. HSE Honorary Professor, Member of the Council:HSE International Expert Council on priority area of development ‘Management’,
Dr.Fuad T. Aleskerov HSE Faculty of Economics, Department of Higher Mathematics,: HSE International Laboratory of Decision Choice and Analysis, Laboratory Head, HSE Laboratory for Experimental and Behavioural Economics, Chief Research Fellow, HSE Tenured Professor, Member of the HSE Academic Council
