Business
analytics
&
Emerging
trends
Introduction
Data
Science
and
Value
of
Big
Data
Introduction
to
Data
Science
Information
management
perspective:
making
the
bridge
between
IT
and
business
The
appeal
of
Big
Data
We
live
in
an
era
of
big
data.
One
reason
is
that
a
lot
of
activities
have
been
digitized
and
this
creates
data
that
can
be
analyzed.
We
try
to
make
sense
of
this
big
data
development.
In
some
sense
the
whole
ideology
of
big
data
(‘replacing
science
and
laws’)
is
a
hype.
At
the
same
time,
big
data
technology
is
here
and
it
is
here
to
stay.
There
indeed
is
a
change
and
this
is
something
that
has
impact
on
business
domains.
What
is
this
change?
This
change
is
to
a
large
extent
enabled
and
supported
by
radical
changes
in
technology:
• The
way
elementary
data
are
captured:
sensors
(automated)
vs
keyboard
(human).
Keyboard
is
used
less,
data
is
captured
some
other
way
(voice
input,
clicks)
• The
way
data
is
stored:
main
memory
and
cloud
vs
disk.
Hard
disk
becomes
obsolete.
New
ways
of
storage
give
opportunities.
• The
way
data
is
analyzed:
data-‐driven
methods
vs
sampling.
In
the
past,
sampling
was
very
popular
and
on
the
base
of
the
sampling
you
could
say
something
about
the
whole
population.
Nowadays,
use
data
of
the
whole
population?
Sampling
is
becoming
obsolete
in
a
big
data
era.
• The
way
data
is
provided
to
users:
data
logistics
vs
data
integration.
• The
way
data
is
presented:
graphical
interactive
visualizations
vs
management
reports
• The
way
knowledge
(business
rules,
models)
is
created:
learning/mining
vs
(labor-‐
intensive)
knowledge
acquisition.
Knowledge
management
is
changing.
Nowadays,
knowledge
is
created
by
learning/mining
algorithms.
Big
data
is
nog
a
solution,
but
an
opportunity.
Why
Big
Data?
Why
are
we
suddenly
in
this
big
data
era?
Combination
of
technical
developments
and
societal
needs
First
of
all,
there
are
a
lot
of
technical
developments.
At
the
same
time,
societal
needs
of
businesses
grew
and
couldn’t
continue
growing
on
a
purely
paper-‐based
administrative
organization.
There
is
the
need
to
take
faster
decision
and
to
be
more
precise
in
targeting
customers.
è Internet
of
things,
in-‐memory
databases.
‘Change
is
the
only
constant.
Need
to
be
pro-‐active.’
1
,Availability
of
massive
amounts
of
digital
data
Availability
of
massive
amounts
of
digital
data.
Every
60
seconds,
a
lot
of
data
is
created.
There
are
massive
amounts
of
data
available,
so
you
could
use
it.
Philosophical
background
Philosophical
background:
waves
of
rationalism
versus
empiricism.
Rationalists
say
that
you
cannot
grasp
information
from
empirical
data
of
you
don’t
have
the
concepts
to
get
the
data.
You
need
prior
knowledge
to
make
sense
of
the
data.
Empiricists
say
they
don’t
trust
the
rationalist
thoughts.
Rationalists
think
they
can
come
to
true
knowledge
on
the
basis
of
their
thinking.
Empiricists
say
you
can
only
come
to
knowledge
on
the
basis
of
true
facts.
è Right
now,
we’re
in
an
empiricist
time.
People
want
data.
From
mainframe
computers
to
personal
computers.
All
the
small
devices
create
a
lot
of
data.
Culture
issue
Nowadays,
we
don’t
want
to
base
our
decisions
on
opinions
or
insights
from
experienced
people
(manager),
but
we
want
to
do
it
based
on
data.
It
seems
much
more
reliable
than,
for
example,
the
intuition
of
the
manager.
The
3
V’s
Volume,
Variety,
Velocity
• Increase
in
volume
of
data
• Increase
in
variety:
nowadays,
also
video
data,
Facebook
data,
sensor
data,
etc.
• Velocity:
data
are
refreshed
at
a
much
quicker
rate
and
needs
to
be
processed,
ideally,
real
time.
CHALLENGE
OPPORTUNITY
THREAT
VOLUME
Technical
challenges
You
can
become
-‐
General
threat:
on
your
processing
more
detailed
in
privacy
when
more
and
storage
analysis.
For
data
about
people
is
capabilities.
instance:
identify
collected.
customer
segments;
-‐
Responsibility
for
a
smaller
groups
and
company
that
owns
their
behavior.
all
the
data.
2
, -‐
Threat
of
drowning
in
data;
don’t
know
how
to
make
sense
of
it.
VARIETY
VELOCITY
You
need
newer
Real
time
reporting
techniques
for
processing
data.
One
technique:
map
produce
From
Big
Data
to
Business
Value
There
are
many
success
stories
about
the
value
of
big
data.
Report
In
a
recent
report,
Nucleus
Research
found
that
for
every
dollar
a
company
spends
on
analytics,
it
gets
back
$10.66.
According
to
this
report,
investments
in
analytics
have
a
very
high
return.
But
at
the
same
time,
it
is
also
known
from
empirical
research
that
many
companies
are
not
able
to
get
the
value
they
could
get.
They
don’t
align
analytics
with
the
business
goals.
Tom
Davenport
According
to
Tom
Davenport,
he
sees
the
main
value
of
big
data
in
the
new
products
and
services
that
become
available.
Also,
he
mentions
that
the
positive
aspect
of
big
data
is
that
‘finally
companies
start
to
look
at
outside
data’.
In
the
past,
businesses
only
looked
at
inside
data.
Yet,
there
is
so
much
data
outside.
What
are
the
business
challenges?
• Risk
management
(resilience)
• Handling
uncertainty:
the
environment
becomes
much
more
dynamic
(changing),
so
you
have
to
react
much
faster
in
order
not
to
get
behind
or
to
face
important
risks.
• Integrity
• Increased
competition/
consumerization
• Regulation
• Innovation
• Sustainability,
conscious
capitalism
Business
opportunities
Business
opportunities
are
for
the
time
being,
especially
in
increasing
operational
efficiency.
3
, The
expectation
is
that
this
will
change
more
and
more.
Development
of
services,
external
services
will
become
much
more
important
than
the
internal
ones.
From
big
data
to
big
impact
Article
gives
an
overview
of
the
developments.
The
general
framework
describes
an
evolution
in
the
business
intelligence
domain.
Evolution
(from
the
paper):
• BI&A
1.0:
data
management
and
warehousing
are
considered
the
foundation
• BI&A
2.0:
web
intelligence,
web
analytics,
and
the
user-‐generated
content
collected
through
Web
2.0-‐based
social
and
crowd-‐sourcing
systems
have
ushered
in
a
new
and
exciting
era
of
BI&A
2.0
research
in
the
2000s,
centered
on
text
and
web
analytics
for
unstructured
web
contents.
• BI&A
3.0:
a
new
research
opportunity
is
emerging.
Emerging
research
(from
the
paper):
• (Big)
data
analytics:
refers
to
the
BI&A
technologies
that
are
grounded
mostly
in
data
mining
and
statistical
analysis.
• Text
analytics:
significant
portion
of
the
unstructured
content
collected
by
an
organization
is
in
textual
format,
from
e-‐mail
communication
and
corporate
documents
to
web
pages
and
social
media
content.
Text
analytics
has
its
academic
roots
in
information
retrieval
and
computational
linguistics.
• Web
analytics:
HTTP/HTML-‐based
hyperlinked
web
sites
and
associated
web
search
engines
and
directory
systems
for
locating
web
content
have
helped
develop
unique
Internet-‐
based
technologies
for
web
site
crawling/spidering,
web
page
updating,
web
site
ranking,
and
search
log
analysis.
• Network
analytics:
recent
network
analytics
research
has
focused
on
areas
such
as
link
mining
and
community
detection.
Concerning
link
mining,
one
seeks
to
discover
or
predict
links
between
nodes
of
a
network
• Mobile
analytics:
mobile
computing
offers
a
means
for
IT
professional
growth
as
more
and
more
organizations
build
applications.
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