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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
http://www.itbusiness.ca/news/information-builders-launches-tool-for-internet-of-things/49279
BIG DATA: DEFINITION
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
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
Other definitions of Big Data:
http://www.opentracker.net/article/definitions-big-data
Big Data implementation:
important aspects
Economic/ Social Area
Environment
Maturity phase of technology
Expected Effect
Big Data implementation: business
Importance of Data Analysis to the different parts of the organization
(% respondents)
Fostering a Data Driven Culture. Economist Intelligence Unit.
http://www.tableausoftware.com/sites/default/files/whitepapers/tableau_dataculture_130219.pdf?signin=a3841a8f840546fced0c759806b7a208
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
Оценка возможностей внедрения технологии больших данных
Big Data as an innovation: implementation possibility
Environment
Strong
Value
Low
Medium
High
Compatible use
Sufficient use
Active, consistent
and creative use
фото
Weak
No use
No use
Random, non
sufficient use
Adoptation of K.Klein research for Big Data
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.
фото
*K. Klein. Innovation Implementation. http://www.management.wharton.upenn.edu/klein/documents/New_Folder/Klein_Knight_Current_Directions_Implementation.pdf
Высшая школа экономики, Москва, 2014
7
Maturity phase of technology
Bill Schmarzo Big Data Business Model Maturity Chart
https://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
Data Monetization (examples)
Data Monetization is the level of business maturity
where organizations are trying to
a smartphone
app where data
and insights
about customer
behaviors,
product
performance,
and market
trends are sold
to marketers
and
manufacturers
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”
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 Chart
https://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
Examples
MapMyRun.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
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 you
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
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 Chart
https://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
DATA SCIENCE: DEFINITION
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 flipside, 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.
Drew Conway
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
BD specialist competences
http://www.datasciencecentral.com/profiles/blogs/the-data-science-venn-diagram-revisited
Program base: Business Informatics
approach and research area
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
17th IEEE Conference on Business Informatics
Competences and skills
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
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
Research areas
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
MSc in Big Data Systems: key facts
Duration:
Starts:
2 years, 24 months, full-time
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: content
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
Optional courses
More management
Creating and Managing Enterprise Information Assets
Advanced Data Management
Big Data Based Marketing Analytics
Big Data Based Risk Analytics
Data Driven Process Control
MSc in Big Data Systems: content
Bridging courses
Data Bases
Enterprise Architecture
Data Analysis
MSc in Big Data Systems
software and partnership
IBM
SAP
Microsoft
Tableau
Oracle
EMC
Qlik
Software: Magic Quadrant for Business Intelligence and Analytic Platforms
http://www.tableausoftware.com/gartner-magic-quadrant-2014
SCIENTIFIC COUNCIL
Dr. Diem Ho
Manager of University Relations for IBM Europe, Middle East and
Africa (EMEA)
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
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’,
SCIENTIFIC COUNCIL
Dr. Stephane Marchand-Maillet, Viper IR & ML group, C-S
Department, CUI, University of Geneva, Switzerland
Dr. Tatyana K. Kravchenko, HSE Tenured Professor, Head
of Business Analytics Department, HSE School of Business
Informatics
Dr. Alexander I. Gromov, Head of Business Process
Modeling and Analysis Department, HSE School of Business
Informatics
Thank you for your
attention!
Contacts: Svetlana Maltseva
[email protected]
Ekaterina German
[email protected]