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1. The Value of Digital Technologies
Big Data
Sofia, 23 March 2018
Severino Meregalli
Scientific Coordinator – DEVO Lab
SDA Bocconi

2. THE BUSINESS CONTEXT: WHY DATA EXPLOITATION IS SO IMPORTANT
• Dynamism and complexity as structural elements
• Fuzzy business scenarios
• Complexity management and profit linkage
• The fall of management as a science and of prescriptive management
• The fall of the “legendary” long term strategic planning as an antidote to complexity
• The “evergreen” gap between Business Requirements and Information Systems
• Desperate search of insight and knowledge sources

3. THE (BIG) DATA LANDSCAPE
• Generating value from data and analytics is one of the pillars of competitive advantage
• Decision-making in complex and dynamic organizations calls for a full exploitation of data
resources
• Progressive digitalization of businesses vs skills needed to take advantage of large and
complex dataset
• Big Data, Data Discovery and Analytics have suffered all negative impacts due to hype and
the rise of improvised players
• Wide range of high performing technologies and players
• Cost/benefit leverage calls for a deep understanding of the real opportunities and hurdles in
Data exploitation

4. DATA EXPLOSION VS ABILITY TO EXECUTE
• There will be a shortage of talent necessary for organizations to take advantage of big data.
By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with
deep analytical skills as well as 1.5 million managers and analysts with the know-how to use
the analysis of big data to make effective decisions.
• Organizations need not only to put the right talent and technology in place but also
structure workflows and incentives to optimize the use of big data.
McKinsey Global Institute 2011

5. 5
THE MEDIA HYPE

6. A CROWDED MARKET

7. 7
HYPE… AS USUAL
Gartner Hype Cycle for Emerging Technologies, 2014

8. 8
THE CALL FOR A MANAGERIAL APPROACH (DEVO LAB SDA BOCCONI)
Value
Shortcut

9. 9
THE ISSUE
After the first wave of technology adoption for
managing and analyzing large datasets, both
the academic and the practitioner community
acknowledged the risks of (another) «hype
driven» approach

10. 10
THREE KEY TOPICS
Physical vs Social
Data quality
Context

11. • It is relatively less complex to get significant results when the focus of the analysis is on deterministic
phenomena (Natural Sciences) rather than on Social Sciences
• In Natural Sciences it is possible to explain/understand a phenomenon by observing a singularity (eg
a star with an odd orbit) …the same does not apply to Social Sciences (eg trendsetter vs crazy
behavior)
• Predictive analysis, as well as the mere understanding of phenomena impacted by social variables is
still characterized by issues difficult to address, even when companies have large amounts of data
and computing power
• The paradox is that in the digital world sometimes it is easier to influence behaviors rather than
understand them
• The short term economic value is proportional to the difficulty of the task: higher in Social Sciences,
lower in Natural Sciences
11
PHYSICAL VS. SOCIAL PHENOMENA

12. 12
DATA QUALITY MANAGEMENT
• The stratification of large amount of data, with different formats, different scopes, emphasizes the old
but evergreen concept of “Garbage-in, Garbage-OUT“
• Big Data tools and technologies have not yet solved this problem and, in some cases, it has been
amplified by the presence of data from sources that are out of control (i.e. Social Networks)
• “Data Quality" attitude is a precondition to initiate a virtuous cycle of data value exploitation
• Technology is here to help, but we still have issues:
– uniqueness (single source of truth)
– accountability for data quality (not IT)
– consistency of goals between who produces and who analyses the data
– availability of consistent and shared data information (metadata)
– legal issues

13. 13
UNDERSTANDING DOMAIN, CONTEXT AND DECODING RESULTS
• The breadth and variety of datasets allow analysts to find numerous correlations between variables,
which can not be found in small datasets
• The issue is to understand which are the meaningful correlations to be considered, since.. the more
the variables, the more correlations that can show significance
• Context is hard to interpret at scale and even harder to maintain when data are reduced to fit into a
model. Obtaining and managing context data will be a challenge.
The more variables, the more correlations
that can show significance. Falsity also grows
faster than information; it is nonlinear
(convex) with respect to data
N. Taleb - Professor of risk engineering at New York
University’s Polytechnic Institute.

14. 14
THE MORE VARIABLES, THE MORE CORRELATIONS THAT CAN SHOW SIGNIFICANCE…

15. • Contextual data are scarce and very often not available or not consistent
with the needs
• Each application domain requires to involve experts that know it from inside.
Statistical “brute force” approach does not work well in Social Sciences
• The issue is to find the sweet spot between “obvious” and “false” findings
15
UNDERSTANDING DOMAIN, CONTEXT AND DECODING RESULTS

16. • Differentiate between physical and social phenomena
• Measure the "quality" of available data
– Accuracy
– Reliability
– Completeness
– Consistency
– Timeliness
• Consider the availability of domain experts / knowledge when dealing with social
phenomena
16
THREE PILLARS FOR DATA VALUE

17. 17
THE ANALYSIS MODEL
Data Quality
Value
Level of
Determinism
Low
High
Low High
Value
Social phenomena
Physical phenomena

18. POSSIBLE PATHS
Level of
DeterminismLow
High
Low High
ValueData Quality
Value

19. 19
VOLVO CAR CORPORATION CASE HISTORY
The Company
• Global leader in the automotive industry
• Acquired by Geely Auto Group in 2010
• Focus on quality and safety : Our vision is to design cars that should not crash. In the shorter perspective
the aim is that by 2020 no-one should be killed or injured in a Volvo car.
Scope of Work
• Improving quality of data
collected from dealers,
engineering, production and
from diagnostic systems
(DRO)
• Build and unified repository
of integrated data
Achievements
• Problem identification and
prioritization of maintenance
activities
• Solving problems of quality
during the production
processes
• Warranty programs
management accuracy
• Potential failure predictive
analysis
The Needs
• Analyze mechanical
performances of the vehicles
in real driving conditions in
order to improve design,
production and after-sales
service (warranty) processes

20. 20
VOLVO CAR CORPORATION CASE HISTORY
Low
High
Low High
Value
FullPartialNull
Level of
Determinism
Data Quality
Value

21. 21
SCE SMART CONNECT CASE HISTORY
The Company
• Southern California Edison is the largest subsidiary of Edison International
• For over a century, the company provides electricity to about 14 million customers in Southern
California (Central, Coastal & Southern California)
Scope of Work
• Acquisition of data from
Smart Meter (720 readings
per month per customer,
about 5.6 billion of readings
per month total)
• Smart meters data
integration with expenses
and demographic
information
Achievements
• Improvement in production
and distribution flow
management
• Peak usage prediction
The Needs
• Provide customers with a
weekly reporting of energy
consumption, in order to
gain expenses control

22. 22
SOUTHERN CALIFORNIA EDISON CASE HISTORY
Low
High
Low
Value
Full
Level of
Determinism
Data Quality
Value

23. 23
GDF SUEZ CASE HISTORY
The Company
• French group, one of the main Utility worldwide (turnover of about 70 billion €)
• Founded in 2008 after Suez and GDF merge
• Core business: production and distribution of electricity, natural gas and renewable sources
Scope of Work
• Customer size wasn’t
addressed consistently
(admin vs. Commercial data)
• Improvement in: Data
Quality, CRM & Billing
integration, Marketing
Campaigns
• Incremental understanding
of customers’ related
phenomena
Achievements
• Customer’s value – based
segmentation
• Churn, due to customer’s
relocation, prevention
• «Gas-only» customer’s
acquisition (electricity)
The Needs
• After liberalization of the
energy market in France, B2C
(CH&P) Business Unit was
willing to pursue the
opportunity to grow in the
electricity market leveraging
their gas market share
• Understand customer
segmentation, where to
focus sales and marketing
initiatives and how

24. 24
GDF SUEZ CASE HISTORY
Low
High
Low High
Value
Level of
Determinism
Data Quality
Value

25. • The analysis of case studies highlights how a mature approach to Data Value
bank on two main dimensions:
– data quality
– the ability to interpret /understand phenomena
• Thanks to the analysis of case histories, it has been possible to identify a first set
of Data Value components
25
LESSON LEARNED FROM CASE HISTORIES

26. 26
DATA VALUE LAYERS
INTRINSIC VALUE
Data Model
Data Volume – Cross Section
Data Volume – Stock
Data Quality
Quantitative tools
Cognitive tools
Data Model
Data Volume – Cross Section
Data Volume – Stock
Data Quality
Quantitative tools
Cognitive tools
Physical Social

27. Domain Expertise
27
DATA VALUE LAYERS
POTENTIAL VALUE
DataToolsExpertise
Data Model
Data Volume – Cross Section
Data Volume – Stock
Data Quality
Quantitative tools
Cognitive tools
Data Model
Data Volume – Cross Section
Data Volume – Stock
Data Quality
Quantitative tools
Cognitive tools
Physical Social
Context - Data
Context - Models
Domain Expertise
Context - Data
Context - Models

28. Edge Computing
vs
Edge Organizations
NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES

29. 29
NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES
Data ownership and
side effects control

30. 30
NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES
Data storage
technologies
evolution is much
slower than data
growth

31. Big Data, Machine
Learning and
Quantum Computing:
the perfect storm ?
NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES

32. NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES

33. NEW FRONTIERS, NEW OPPORTUNITIES…NEW ISSUES
Research, consulting,
teaching, software
industry, working again
without hackatons
(or with real rewards
and better ethics)

34. 34
SUMMARY AND RECOMMENDATIONS
• There are no "big data". We have only data which are manageable / unmanageable with
state of the art technologies
• The real challenge is getting «Big Info» and take better decisions
• Natural and Social domains are different
• Data quality is still the precondition for any project
• Context understanding and contextual data are (in social applications) very often the real
bottleneck
• Use a checklist to asses data value components before starting a project
• Only consider vendors that are able to provide fully integrated solutions to their data issues
(no room for improvised players)
• Not to capitalize on data sets in Natural Sciences Domains is a big mistake
…..transforming data sets in value in Social Sciences is (still) a big challenge

35. • Davenport T.H., Big Data at Work, Dispelling the Myths, Uncovering the Opportunities, Harvard
Business Review Press, 2013
• Davenport T.H., Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review,
October, 2012 http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
• Gartner Hype Cycle for Emerging Technologies, 2014
• McKinsey Global Institute, Big data: The next frontier for innovation, competition, and productivity,
2011
• Redman T, Data’s Credibility Problem, Harvard Business Review, December 2013
http://hbr.org/2013/12/datas-credibility-problem/ar/1
• Ross J.W., Beath C.M., Quaadgras A., You may not need Big Data after all, Harvard Business
Review, December 2013, http://hbr.org/2013/12/you-may-not-need-big-data-after-all/ar/1
• Taleb N. N., Beware the Big Errors of ‘Big Data’, Wired, 2013 www.wired.com/2013/02/big-data-
means-big-errors-people/
REFERENCES