View Dr. Robin Bloor's presentation from the Dec. 2013 Big Data Conference in Rome.

1. Analytics
and Big Data
Analytics
Robin Bloor Ph D

2. The Sequence of Topics….
1
2
3
4
5
Data Science?
The Nature of
Analytics
Machine Learning Et
Al
The Business
Perspective
The Future

3. 1

4. What Is Data Science?
 There
is no “data
science.” It’s a
misnomer
 All science is
empirical and involves
data analysis.
 Science implements a
method.
 So do statisticians

5. What Is A Data Scientist?
 Project
manager
 Qualified statistician
 Domain Business
expert
 Experienced data
architect
 Software engineer
(It’s a team)

6. Data Scientist v Business Analysts
 Claims
that business
analysts can be data
scientists are dubious
 Good practitioners of
statistics understand
data (from years of
training)
 Software understands
nothing, it simply
implements algorithms

7. Who Understands Data?

8. Nevertheless!
You can know more
about a business
from its data than
by any other
means

9. 2
The
Nature
Of
Analytics

10. The Field of Business Intelligence
Hindsight
• Regular
reporting/operational
BI
Oversight
• Dashboards, OLAP,
BPM, etc.
Insight
• Data
mining, statistical
analysis
Foresight
• Predictive analytics

11. The Driving Force is Insight

12. A Process Not An Activity
 Data Analytics is a multidisciplinary end-to-end
process
 Until recently it was a
walled-garden. But recently
the walls were torn down
by…
 Data availability
 Scalable technology
 Open source tools

13. The Data Analytics Process - Detail

14. The CRITICAL Workload Issue
 Previously, we viewed
database workloads as
an i/o optimization
problem
 With analytics the
workload is a very
variable mix of i/o
and calculation
 No databases were
built for this – not
even Big Data
databases

15. 3
Machine
Learning
Et Al

16. Analytical Latencies
1 Data access
2 Data preparation
3 Model development
4 Execution
5 Implementation
6 Model Audit & Update
Speed = value (probably)

17. The Open Source Dynamic
 The R Language
 Over 1 million
users
 Hadoop and its
Ecosystem
 Reduced latency
for analytics
 Machine Learning
Algorithms
 Raw power
None of these are engineered for performance

18. Machine Learning Algorithms - 1
 There are many:
 Neural network(s)
 Bayesian networks
 Decisions
trees/random
forests
 Support vector
machines
 K-means
 Clustering
 Regression(s)
 Etc.

19. Machine Learning Algorithms - 2
 They are not newly
invented
 We did not
previously use them
much because we
never had the
computer power
 Now that we have
the power (at a
price) we can
employ them

20. Machine Learning Algorithms - 3
 Machine learning
algorithms can check
all possibilities
 We never had the
computer power
 Now that we have
the power (at a
price) we can
employ them

21. The Impact?
 Machine learning
and processing
power (parallelism)
will change the
data analysis
process
 The analytics team
needs to
understand IT

22. 4
The
Business
Perspective

23. Business Metamorphosis
 The role of data
analysis has not
changed
 Only the speed has
changed
 The process will
evolve
 It will be disruptive
for incumbent
vendors

24. The Data Analysis Budget
 Data Analysis is
Business R&D
 The focus is on
business process
 The outcome of
successful R&D is
a changed process
 Think of
manufacturing for
a useful analogy

25. The Data Analysis Budget
 Data Analysis is
Business R&D
 The focus is on
business process
 The outcome of
successful R&D is
a changed process
 Think of
manufacturing for
a useful analogy

26. 5
The
Future

27. Non èfinitafino a quando
la signora grassacanta
 Hardware disruption
 Software disruption
 Business process
disruption
 All we know is:
 Analytical
processing will get
faster
 Analytic latencies
will reduce
 Data will continue
to grow
 Analytics will be a
differentiator

28. In Summary…
1
2
3
4
5
Data Science?
The Nature of
Analytics
Machine Learning Et
Al
The Business
Perspective
The Future