The document discusses big data and analytics. It notes that big data is being generated from many sources and contains a variety of data. It also discusses why data scientists want more data to create more sophisticated models and discusses challenges around privacy, metadata, and data localization policies that can impact analytics outcomes.

1. IBM Research – Business Solutions and Mathematical Sciences
For the Love of Big Data
Dr. Bob Sutor
VP, Business Solutions and Mathematical Sciences

2. IBM Research – Business Solutions and Mathematical Sciences
What is Big Data?
 Big data is being generated by everything around us.
 Every digital process and social media exchange produces it.
 Systems, sensors and mobile devices transmit it.
 Big data is arriving from multiple sources at amazing
velocities, volumes and varieties.
 To extract meaningful value from big data, you
need optimal processing power, storage,
analytics capabilities, and skills.
© 2014 International Business Machines Corporation
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3. IBM Research – Business Solutions and Mathematical Sciences
Why do data scientists want more data, rather than less?
 It is there.
 Data is the basis of the models we create to explain, predict,
and affect behavior.
 With more data, our models become more sophisticated
and, we hope, more accurate.
 How much data is too much data?
© 2014 International Business Machines Corporation
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4. IBM Research – Business Solutions and Mathematical Sciences
What issues can analytics present?
 Are all aspects of privacy, anonymization, and liability
understood by the practitioners?
 If I tell you that you cannot look at some data but you can
infer the information (e.g., gender) anyway, is that all right?
 What are the rules for working with metadata and
summarized data?
 How do we process static, collected data together with more
real-time, rapidly changing information such as location?
© 2014 International Business Machines Corporation
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5. IBM Research – Business Solutions and Mathematical Sciences
Approach to policy can determine outcomes
 Reductions in the amount and kinds of data can produce
diminished or inaccurate results.
 Policy must take into account the value received by individuals
for the use of their personal data.
 Enforced data localization may decrease
analytical completeness unless we can
move intermediate results or the site of
computation.
© 2014 International Business Machines Corporation
5

6. IBM Research – Business Solutions and Mathematical Sciences
Approach to policy can determine outcomes
 Reductions in the amount and kinds of data can produce
diminished or inaccurate results.
 Policy must take into account the value received by individuals
for the use of their personal data.
 Enforced data localization may decrease
analytical completeness unless we can
move intermediate results or the site of
computation.
© 2014 International Business Machines Corporation
5