1.
1
Best
Practices
Brief
Four
Pillars
of
Business
Analytics
Improve
customer
experience
and
analytic
capabilities
with
Actuate
BIRT
Business
goals
for
applications
must
address
data,
people,
process
and
technology,
according
to
Gartner’s
Jamie
Popkin.
In
a
keynote
presentation
at
the
Gartner
Catalyst
Conference,
Popkin
called
this
framework
the
Four
Pillars
of
Business
Analytics.
“Gartner
indicates
by
2015,
25%
of
analytic
capabilities
will
be
embedded
in
business
applications
and
designing
data
visualizations
for
web
and
mobile
apps
will
become
a
major
growth
engine
for
the
worldwide
Business
Intelligence
and
Analytics
Software
Market.”
–
Jamie
Popkin,
Managing
VP,
Gartner
Transforming
analytic
data
into
usable
business
information
and
designing
compelling
data-‐
driven
customer-‐facing
applications
remains
both
an
art
and
a
science,
and
a
clear
path
to
success
is
sometimes
hard
to
identify.
Inspired
by
Popkin’s
talk,
Actuate
believes
the
Four
Pillars
framework,
shown
in
Figure
1:
Gartner’s
Four
Pillars
of
Business
Analytics,
can
help
application
initiatives
succeed.
The
Four
Pillars
can
help
developers
and
IT
managers
ask
better
questions
–
and
get
better
answers
–
when
they
develop
business
analytics
applications.
An
emerging
set
of
design
principles,
inspired
by
the
Four
Pillars,
provides
a
blueprint
for
delivering
apps
that
inform,
connect,
and
motivate
end
users.
This
best
practices
brief
describes
the
Four
Pillars
of
Business
Analytics
framework,
then
shows
how
you
can
employ
the
Four
Pillars
to
understand
your
application
needs
and
design
and
build
applications
that
inform,
connect
and
motivate
users.
The
brief
also
explains
why
Actuate’s
BIRT
platform
is
ideal
for
high-‐user,
high-‐volume
analytic
applications.
Best
Practices
Brief
2.
2
Best
Practices
Brief
Understanding
the
Four
Pillars
Figure
1:
Gartner’s
Four
Pillars
of
Business
Analytics
1. Information
management
foundation
(Data)
The
Data
pillar
balances
governance
and
access
in
the
information-‐driven
enterprise.
It
requires
connecting
to
disparate
data
sources
–
regardless
of
their
type
and
location
–
to
build
a
virtual
data
warehouse
that
is
easy
and
secure
to
consume
and
use.
2. Organization
(People)
The
People
pillar
brings
IT
and
Business
communities
together
to
meet
shared
company
goals.
IT
people
require
a
visual,
programmatic
and
assembly
style
development
environment,
with
deep
integration
APIs
for
embedding
processes.
Business
people
need
secure
and
personalized
self-‐service,
along
with
the
ability
to
embed
analytics
in
existing
applications
and
display
them
anywhere
–
including
wearable
and
mobile
devices
–
to
boost
usage.
For
IT
people,
engaging
business
groups
early
in
the
application
design
and
development
process
helps
to
drive
conversations
forward.
3.
3
Best
Practices
Brief
3. Fact-‐based
decision
making
(Process)
The
Process
pillar
requires
having
the
right
information
at
the
right
time
to
make
better,
faster
decisions.
Because
different
roles
make
different
types
of
decisions,
it’s
important
to
leverage
the
same
data
to
support
a
variety
of
processes.
For
example,
operational
and
executive
users
require
dashboards;
customers
want
statements,
proposals
and
reports;
and
departments
need
performance
scorecards.
All
of
these
outputs
should
be
built
with
reusable
components
and
shared
across
groups
to
ensure
maximum
use.
4. Appropriate
technology
platform
(Technology)
The
Technology
pillar
encompasses
development
and
deployment,
with
systems
that
break
down
silos
of
capability.
Integrated,
open,
extensible
tools
support
growth,
so
Actuate
embraces
standards-‐based
content
development
environment
and
provides
a
flexible,
scalable
and
secure
automated
deployment
server
(BIRT
iHub).
This
combination
has
the
flexibility
to
deliver
data
from
any
source
and
embed
it
in
any
application.
Another
way
to
understand
the
Four
Pillars
is
through
the
Business
Analytics
Framework
shown
in
Figure
2.
In
this
arrangement,
the
Data
pillar
is
the
Information
foundation
of
the
framework,
and
the
People,
Process,
and
Platform
(Technology)
pillars
are
broken
out
by
their
specific
needs
and
requirements.
It’s
important
to
note
in
Figure
2:
The
Business
Analytics
Framework
the
“Business
Models,
Business
Strategy
and
Enterprise
Metrics”
spans
all
of
the
pillars,
as
does
system
performance.
Figure
2:
the
Business
Analytics
Framework
4.
4
Best
Practices
Brief
Addressing
Complexity
in
Customer
Facing
Applications
Once
you
understand
your
application
needs
in
the
context
of
the
Four
Pillars,
look
at
each
application
in
terms
of
users
and
data.
How
many
people
will
use
an
application,
and
how
much
personalized
data
each
user
will
require
from
the
app?
As
illustrated
in
Figure
3:
Customer-‐Facing
Applications
–
Complexity
Comparison,
applications
with
the
most
users
and
the
highest
volume
of
personalized
data
per
user
are
typically
the
most
complex,
and
the
most
challenging
in
terms
of
design,
data
access,
management,
and
delivery.
These
applications
require
a
secure,
scalable
platform
–
Actuate
BIRT
–
to
meet
unique
challenges:
• Take
a
customer-‐centric
view,
in
order
to
focus
on
adding
value
• Manage
increased
complexity
as
customers
and
data
are
added.
These
apps
–
particularly
those
used
by
financial
institutions’
customers
–
must
support
millions
of
users
who
aren’t
consistently
tech-‐savvy
and
who
have
unique
information
requirements
• Serve
enterprise
analytics
needs.
These
apps
must
move
beyond
departmental
scale
to
support
massive
amounts
of
data
and
users
Figure
3:
Customer-‐Facing
Applications
–
Complexity
Comparison
5.
5
Best
Practices
Brief
Applications
in
the
upper-‐right
quadrant
–
those
with
large
numbers
of
users
and
high
volumes
of
data
per
user
–
deliver
more
value
to
users
when
they
employ
analytics.
Analytics
is
the
discipline
that
applies
logic
and
mathematics
to
data
to
provide
insights
that
help
people
make
better
decisions.
(Indeed,
analytics
is
synonymous
with
“fact-‐based
decision-‐making”
found
in
the
Process
pillar.)
Four
types
of
analytics
–
descriptive,
diagnostic,
predictive,
and
prescriptive
–
are
illustrated
in
Figure
4:
Four
Types
of
Analytics.
Each
type
of
analytics
starts
with
data
and
poses
a
question,
and
each
requires
some
amount
of
human
input
to
arrive
at
a
decision.
In
the
case
of
decision
automation
–
a
subset
of
prescriptive
analytics
–
specific
actions
can
be
taken
based
on
data
without
human
input.
Each
of
the
four
types
of
analytics
has
a
place
in
an
information-‐driven
enterprise
and
in
your
analytics
strategy.
They
are
not
a
hierarchy;
prescriptive
analytics
are
not
better
than
predictive
analytics,
for
example,
and
each
type
of
analytics
is
applicable
to
specific
use
cases.
Figure
4:
Four
Types
of
Analytics
The
ways
users
consume
and
interact
with
analytics
vary.
Embedded
analytics,
dashboards
and
reports
are
common
methods
for
presenting
analytics
to
users.
Capabilities
such
as
queries,
data
visualizations
and
packaged
analytic
solutions
for
specific
business
problems
are
often
built
into
analytic
applications.